diff --git a/Bird Species Classification/Dataset/Readme.md b/Bird Species Classification/Dataset/Readme.md new file mode 100644 index 000000000..c2ab9f4fb --- /dev/null +++ b/Bird Species Classification/Dataset/Readme.md @@ -0,0 +1,7 @@ +- The link for the dataset used in this project: https://www.kaggle.com/datasets/veeralakrishna/200-bird-species-with-11788-images + +- Caltech-UCSD Birds-200-2011 (CUB-200-2011) is an extended version of the CUB-200 dataset, with roughly double the number of images per class and new part location annotations. + +- Number of categories: 200 + +- Number of images: 11,788 \ No newline at end of file diff --git a/Bird Species Classification/Images/InceptionV3.png b/Bird Species Classification/Images/InceptionV3.png new file mode 100644 index 000000000..11cc068aa Binary files /dev/null and b/Bird Species Classification/Images/InceptionV3.png differ diff --git a/Bird Species Classification/Images/inception_resnet_v2.png b/Bird Species 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Classification/Model/bird_species_classification.ipynb b/Bird Species Classification/Model/bird_species_classification.ipynb new file mode 100644 index 000000000..94f95e0ba --- /dev/null +++ b/Bird Species Classification/Model/bird_species_classification.ipynb @@ -0,0 +1,14524 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "72901526", + "metadata": { + "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", + "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5", + "execution": { + "iopub.execute_input": "2024-10-03T19:45:36.529099Z", + "iopub.status.busy": "2024-10-03T19:45:36.528412Z", + "iopub.status.idle": "2024-10-03T19:45:37.276497Z", + "shell.execute_reply": "2024-10-03T19:45:37.275654Z" + }, + "papermill": { + "duration": 0.757997, + "end_time": "2024-10-03T19:45:37.279096", + "exception": false, + "start_time": "2024-10-03T19:45:36.521099", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/kaggle/input/200-bird-species-with-11788-images/segmentations.tgz\n", + "/kaggle/input/200-bird-species-with-11788-images/CUB_200_2011.tgz\n" + ] + } + ], + "source": [ + "# This Python 3 environment comes with many helpful analytics libraries installed\n", + "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n", + "# For example, here's several helpful packages to load\n", + "\n", + "import numpy as np # linear algebra\n", + "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", + "\n", + "# Input data files are available in the read-only \"../input/\" directory\n", + "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n", + "\n", + "import os\n", + "for dirname, _, filenames in os.walk('/kaggle/input'):\n", + " for filename in filenames:\n", + " print(os.path.join(dirname, filename))\n", + "\n", + "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n", + "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "1eceb73d", + "metadata": { + "execution": { + "iopub.execute_input": "2024-10-03T19:45:37.291912Z", + "iopub.status.busy": "2024-10-03T19:45:37.291484Z", + "iopub.status.idle": "2024-10-03T19:45:56.487321Z", + "shell.execute_reply": "2024-10-03T19:45:56.486286Z" + }, + "papermill": { + "duration": 19.204429, + "end_time": "2024-10-03T19:45:56.489436", + "exception": false, + "start_time": "2024-10-03T19:45:37.285007", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extraction complete.\n" + ] + } + ], + "source": [ + "import tarfile\n", + "\n", + "# Path to the .tgz file\n", + "tgz_file_path = \"/kaggle/input/200-bird-species-with-11788-images/CUB_200_2011.tgz\"\n", + "\n", + "# Extract the .tgz file\n", + "with tarfile.open(tgz_file_path, \"r:gz\") as tar:\n", + " tar.extractall(path=\"./\") # Specify the destination folder\n", + "tgz_file_path = \"/kaggle/input/200-bird-species-with-11788-images/segmentations.tgz\"\n", + "\n", + "# Extract the .tgz file\n", + "with tarfile.open(tgz_file_path, \"r:gz\") as tar:\n", + " tar.extractall(path=\"./\") # Specify the destination folder\n", + "\n", + "print(\"Extraction complete.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e6df478b", + "metadata": { + "execution": { + "iopub.execute_input": "2024-10-03T19:45:56.501894Z", + "iopub.status.busy": "2024-10-03T19:45:56.501539Z", + "iopub.status.idle": "2024-10-03T19:45:57.880769Z", + "shell.execute_reply": "2024-10-03T19:45:57.879941Z" + }, + "papermill": { + "duration": 1.387799, + "end_time": "2024-10-03T19:45:57.883004", + "exception": false, + "start_time": "2024-10-03T19:45:56.495205", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "50690d11", + "metadata": { + "execution": { + "iopub.execute_input": "2024-10-03T19:45:57.895766Z", + "iopub.status.busy": "2024-10-03T19:45:57.895323Z", + "iopub.status.idle": "2024-10-03T19:45:57.903212Z", + "shell.execute_reply": "2024-10-03T19:45:57.902211Z" + }, + "papermill": { + "duration": 0.016357, + "end_time": "2024-10-03T19:45:57.905220", + "exception": false, + "start_time": "2024-10-03T19:45:57.888863", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['001.Black_footed_Albatross', '002.Laysan_Albatross', '003.Sooty_Albatross', '004.Groove_billed_Ani', '005.Crested_Auklet', '006.Least_Auklet', '007.Parakeet_Auklet', '008.Rhinoceros_Auklet', '009.Brewer_Blackbird', '010.Red_winged_Blackbird', '011.Rusty_Blackbird', '012.Yellow_headed_Blackbird', '013.Bobolink', '014.Indigo_Bunting', '015.Lazuli_Bunting', '016.Painted_Bunting', '017.Cardinal', '018.Spotted_Catbird', '019.Gray_Catbird', '020.Yellow_breasted_Chat', '021.Eastern_Towhee', '022.Chuck_will_Widow', '023.Brandt_Cormorant', '024.Red_faced_Cormorant', '025.Pelagic_Cormorant', '026.Bronzed_Cowbird', '027.Shiny_Cowbird', '028.Brown_Creeper', '029.American_Crow', '030.Fish_Crow', '031.Black_billed_Cuckoo', '032.Mangrove_Cuckoo', '033.Yellow_billed_Cuckoo', '034.Gray_crowned_Rosy_Finch', '035.Purple_Finch', '036.Northern_Flicker', '037.Acadian_Flycatcher', '038.Great_Crested_Flycatcher', '039.Least_Flycatcher', '040.Olive_sided_Flycatcher', '041.Scissor_tailed_Flycatcher', '042.Vermilion_Flycatcher', '043.Yellow_bellied_Flycatcher', '044.Frigatebird', '045.Northern_Fulmar', '046.Gadwall', '047.American_Goldfinch', '048.European_Goldfinch', '049.Boat_tailed_Grackle', '050.Eared_Grebe', '051.Horned_Grebe', '052.Pied_billed_Grebe', '053.Western_Grebe', '054.Blue_Grosbeak', '055.Evening_Grosbeak', '056.Pine_Grosbeak', '057.Rose_breasted_Grosbeak', '058.Pigeon_Guillemot', '059.California_Gull', '060.Glaucous_winged_Gull', '061.Heermann_Gull', '062.Herring_Gull', '063.Ivory_Gull', '064.Ring_billed_Gull', '065.Slaty_backed_Gull', '066.Western_Gull', '067.Anna_Hummingbird', '068.Ruby_throated_Hummingbird', '069.Rufous_Hummingbird', '070.Green_Violetear', '071.Long_tailed_Jaeger', '072.Pomarine_Jaeger', '073.Blue_Jay', '074.Florida_Jay', '075.Green_Jay', '076.Dark_eyed_Junco', '077.Tropical_Kingbird', '078.Gray_Kingbird', '079.Belted_Kingfisher', '080.Green_Kingfisher', '081.Pied_Kingfisher', '082.Ringed_Kingfisher', '083.White_breasted_Kingfisher', '084.Red_legged_Kittiwake', '085.Horned_Lark', '086.Pacific_Loon', '087.Mallard', '088.Western_Meadowlark', '089.Hooded_Merganser', '090.Red_breasted_Merganser', '091.Mockingbird', '092.Nighthawk', '093.Clark_Nutcracker', '094.White_breasted_Nuthatch', '095.Baltimore_Oriole', '096.Hooded_Oriole', '097.Orchard_Oriole', '098.Scott_Oriole', '099.Ovenbird', '100.Brown_Pelican', '101.White_Pelican', '102.Western_Wood_Pewee', '103.Sayornis', '104.American_Pipit', '105.Whip_poor_Will', '106.Horned_Puffin', '107.Common_Raven', '108.White_necked_Raven', '109.American_Redstart', '110.Geococcyx', '111.Loggerhead_Shrike', '112.Great_Grey_Shrike', '113.Baird_Sparrow', '114.Black_throated_Sparrow', '115.Brewer_Sparrow', '116.Chipping_Sparrow', '117.Clay_colored_Sparrow', '118.House_Sparrow', '119.Field_Sparrow', '120.Fox_Sparrow', '121.Grasshopper_Sparrow', '122.Harris_Sparrow', '123.Henslow_Sparrow', '124.Le_Conte_Sparrow', '125.Lincoln_Sparrow', '126.Nelson_Sharp_tailed_Sparrow', '127.Savannah_Sparrow', '128.Seaside_Sparrow', '129.Song_Sparrow', '130.Tree_Sparrow', '131.Vesper_Sparrow', '132.White_crowned_Sparrow', '133.White_throated_Sparrow', '134.Cape_Glossy_Starling', '135.Bank_Swallow', '136.Barn_Swallow', '137.Cliff_Swallow', '138.Tree_Swallow', '139.Scarlet_Tanager', '140.Summer_Tanager', '141.Artic_Tern', '142.Black_Tern', '143.Caspian_Tern', '144.Common_Tern', '145.Elegant_Tern', '146.Forsters_Tern', '147.Least_Tern', '148.Green_tailed_Towhee', '149.Brown_Thrasher', '150.Sage_Thrasher', '151.Black_capped_Vireo', '152.Blue_headed_Vireo', '153.Philadelphia_Vireo', '154.Red_eyed_Vireo', '155.Warbling_Vireo', '156.White_eyed_Vireo', '157.Yellow_throated_Vireo', '158.Bay_breasted_Warbler', '159.Black_and_white_Warbler', '160.Black_throated_Blue_Warbler', '161.Blue_winged_Warbler', '162.Canada_Warbler', '163.Cape_May_Warbler', '164.Cerulean_Warbler', '165.Chestnut_sided_Warbler', '166.Golden_winged_Warbler', '167.Hooded_Warbler', '168.Kentucky_Warbler', '169.Magnolia_Warbler', '170.Mourning_Warbler', '171.Myrtle_Warbler', '172.Nashville_Warbler', '173.Orange_crowned_Warbler', '174.Palm_Warbler', '175.Pine_Warbler', '176.Prairie_Warbler', '177.Prothonotary_Warbler', '178.Swainson_Warbler', '179.Tennessee_Warbler', '180.Wilson_Warbler', '181.Worm_eating_Warbler', '182.Yellow_Warbler', '183.Northern_Waterthrush', '184.Louisiana_Waterthrush', '185.Bohemian_Waxwing', '186.Cedar_Waxwing', '187.American_Three_toed_Woodpecker', '188.Pileated_Woodpecker', '189.Red_bellied_Woodpecker', '190.Red_cockaded_Woodpecker', '191.Red_headed_Woodpecker', '192.Downy_Woodpecker', '193.Bewick_Wren', '194.Cactus_Wren', '195.Carolina_Wren', '196.House_Wren', '197.Marsh_Wren', '198.Rock_Wren', '199.Winter_Wren', '200.Common_Yellowthroat']\n" + ] + } + ], + "source": [ + "import os\n", + "\n", + "# Path to the parent directory\n", + "parent_directory = '/kaggle/working/CUB_200_2011/images'\n", + "\n", + "# List of directories inside the parent directory\n", + "directories = [d for d in os.listdir(parent_directory) if os.path.isdir(os.path.join(parent_directory, d))]\n", + "\n", + "directories = sorted(directories)\n", + "print(directories)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b06f400a", + "metadata": { + "collapsed": true, + "execution": { + "iopub.execute_input": "2024-10-03T19:45:57.917370Z", + "iopub.status.busy": "2024-10-03T19:45:57.917100Z", + "iopub.status.idle": "2024-10-03T19:47:42.002433Z", + "shell.execute_reply": "2024-10-03T19:47:42.001521Z" + }, + "jupyter": { + "outputs_hidden": true + }, + "papermill": { + "duration": 104.094016, + "end_time": "2024-10-03T19:47:42.004756", + "exception": false, + "start_time": "2024-10-03T19:45:57.910740", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/kaggle/working/CUB_200_2011/images/001.Black_footed_Albatross\n", + "Train indexes: [19 51 12 56 30 3 5 26 1 31 45 42 22 43 50 38 41 10 18 46 6 34 11 35\n", + " 17 59 52 7 48 33 37 27 24 32 15 49]\n", + "Validation indexes: [ 8 55 47 58 20 0 28 40 23 4 16 53]\n", + "Test indexes: [44 2 39 13 14 36 21 54 25 29 9 57]\n", + "Masking Black_Footed_Albatross_0033_796086.jpg train completed - 1\n", + "Masking Black_Footed_Albatross_0079_796122.jpg train completed - 2\n", + "Masking Black_Footed_Albatross_0019_796104.jpg train completed - 3\n", + "Masking Black_Footed_Albatross_0086_796062.jpg train completed - 4\n", + "Masking Black_Footed_Albatross_0047_796064.jpg train completed - 5\n", + "Masking Black_Footed_Albatross_0005_796090.jpg train completed - 6\n", + "Masking Black_Footed_Albatross_0007_796138.jpg train completed - 7\n", + "Masking Black_Footed_Albatross_0041_796108.jpg train completed - 8\n", + "Masking Black_Footed_Albatross_0002_55.jpg train completed - 9\n", + "Masking Black_Footed_Albatross_0049_796063.jpg train completed - 10\n", + "Masking Black_Footed_Albatross_0069_796139.jpg train completed - 11\n", + "Masking Black_Footed_Albatross_0065_796068.jpg 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completed - 4\n", + "Masking Black_Footed_Albatross_0024_796089.jpg test completed - 5\n", + "Masking Black_Footed_Albatross_0057_796106.jpg test completed - 6\n", + "Masking Black_Footed_Albatross_0036_796127.jpg test completed - 7\n", + "Masking Black_Footed_Albatross_0082_796121.jpg test completed - 8\n", + "Masking Black_Footed_Albatross_0040_796066.jpg test completed - 9\n", + "Masking Black_Footed_Albatross_0046_18.jpg test completed - 10\n", + "Masking Black_Footed_Albatross_0014_89.jpg test completed - 11\n", + "Masking Black_Footed_Albatross_0088_796133.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/002.Laysan_Albatross\n", + "Train indexes: [26 56 45 44 11 25 46 29 42 49 1 51 6 35 59 3 17 4 8 27 22 36 21 2\n", + " 10 14 16 30 39 0 55 13 32 20 34 33]\n", + "Validation indexes: [ 9 41 43 58 15 47 37 24 54 50 38 28]\n", + "Test indexes: [ 5 12 48 23 7 53 40 52 31 19 57 18]\n", + "Masking Laysan_Albatross_0051_1020.jpg train completed - 1\n", + "Masking 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- 5\n", + "Masking Groove_Billed_Ani_0012_1784.jpg test completed - 6\n", + "Masking Groove_Billed_Ani_0044_1731.jpg test completed - 7\n", + "Masking Groove_Billed_Ani_0060_1505.jpg test completed - 8\n", + "Masking Groove_Billed_Ani_0036_1604.jpg test completed - 9\n", + "Masking Groove_Billed_Ani_0047_1706.jpg test completed - 10\n", + "Masking Groove_Billed_Ani_0074_1730.jpg test completed - 11\n", + "Masking Groove_Billed_Ani_0100_1646.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/005.Crested_Auklet\n", + "Train indexes: [ 8 25 39 5 14 19 37 34 7 26 4 32 17 15 40 11 41 13 27 21 23 43 2 24\n", + " 18 29]\n", + "Validation indexes: [22 20 30 9 3 35 12 36 0]\n", + "Test indexes: [31 42 6 28 16 38 1 10 33]\n", + "Masking Crested_Auklet_0018_1817.jpg train completed - 1\n", + "Masking Crested_Auklet_0052_794952.jpg train completed - 2\n", + "Masking Crested_Auklet_0073_785248.jpg train completed - 3\n", + "Masking Crested_Auklet_0011_794927.jpg train completed - 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"Masking Crested_Auklet_0001_794941.jpg valid completed - 9\n", + "Masking Crested_Auklet_0063_794901.jpg test completed - 1\n", + "Masking Crested_Auklet_0076_785252.jpg test completed - 2\n", + "Masking Crested_Auklet_0012_794960.jpg test completed - 3\n", + "Masking Crested_Auklet_0059_794929.jpg test completed - 4\n", + "Masking Crested_Auklet_0036_794905.jpg test completed - 5\n", + "Masking Crested_Auklet_0072_785260.jpg test completed - 6\n", + "Masking Crested_Auklet_0003_794962.jpg test completed - 7\n", + "Masking Crested_Auklet_0021_794938.jpg test completed - 8\n", + "Masking Crested_Auklet_0067_785249.jpg test completed - 9\n", + "/kaggle/working/CUB_200_2011/images/006.Least_Auklet\n", + "Train indexes: [35 19 24 10 32 17 39 25 26 27 18 14 22 30 15 12 4 21 34 7 28 29 6 38]\n", + "Validation indexes: [ 5 23 11 40 8 2 9 31]\n", + "Test indexes: [ 0 16 13 36 3 37 20 33 1]\n", + "Masking Least_Auklet_0054_795130.jpg train completed - 1\n", + "Masking 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Least_Auklet_0004_795112.jpg test completed - 1\n", + "Masking Least_Auklet_0025_795087.jpg test completed - 2\n", + "Masking Least_Auklet_0021_795127.jpg test completed - 3\n", + "Masking Least_Auklet_0055_795106.jpg test completed - 4\n", + "Masking Least_Auklet_0008_795071.jpg test completed - 5\n", + "Masking Least_Auklet_0057_795126.jpg test completed - 6\n", + "Masking Least_Auklet_0029_795122.jpg test completed - 7\n", + "Masking Least_Auklet_0050_1924.jpg test completed - 8\n", + "Masking Least_Auklet_0005_795119.jpg test completed - 9\n", + "/kaggle/working/CUB_200_2011/images/007.Parakeet_Auklet\n", + "Train indexes: [ 4 16 34 17 35 43 20 49 45 32 50 6 3 26 22 47 44 23 52 37 46 41 9 28\n", + " 19 40 48 21 33 1 31]\n", + "Validation indexes: [ 5 15 29 27 13 2 24 14 42 18 0]\n", + "Test indexes: [10 12 7 25 30 8 38 51 39 11 36]\n", + "Masking Parakeet_Auklet_0008_795992.jpg train completed - 1\n", + "Masking Parakeet_Auklet_0027_795925.jpg train completed - 2\n", + "Masking 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55 4 45 6 3 32 36 50 33 31 7 23 18 1 30\n", + " 22 41 58 44 9 10 39 38 2 53 27 52]\n", + "Validation indexes: [16 34 25 13 20 43 54 8 14 59 21 35]\n", + "Test indexes: [28 19 5 11 24 49 12 57 29 40 26 37]\n", + "Masking Rusty_Blackbird_0104_6685.jpg train completed - 1\n", + "Masking Rusty_Blackbird_0032_6611.jpg train completed - 2\n", + "Masking Rusty_Blackbird_0001_6548.jpg train completed - 3\n", + "Masking Rusty_Blackbird_0102_6590.jpg train completed - 4\n", + "Masking Rusty_Blackbird_0114_6760.jpg train completed - 5\n", + "Masking Rusty_Blackbird_0093_6628.jpg train completed - 6\n", + "Masking Rusty_Blackbird_0027_6593.jpg train completed - 7\n", + "Masking Rusty_Blackbird_0101_6880.jpg train completed - 8\n", + "Masking Rusty_Blackbird_0108_6867.jpg train completed - 9\n", + "Masking Rusty_Blackbird_0113_6664.jpg train completed - 10\n", + "Masking Rusty_Blackbird_0009_6853.jpg train completed - 11\n", + "Masking Rusty_Blackbird_0100_6597.jpg train completed - 12\n", + 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Lazuli_Bunting_0034_14864.jpg valid completed - 11\n", + "Masking Lazuli_Bunting_0102_14605.jpg valid completed - 12\n", + "Masking Lazuli_Bunting_0052_14618.jpg test completed - 1\n", + "Masking Lazuli_Bunting_0040_14923.jpg test completed - 2\n", + "Masking Lazuli_Bunting_0067_14672.jpg test completed - 3\n", + "Masking Lazuli_Bunting_0026_14669.jpg test completed - 4\n", + "Masking Lazuli_Bunting_0105_15017.jpg test completed - 5\n", + "Masking Lazuli_Bunting_0057_14775.jpg test completed - 6\n", + "Masking Lazuli_Bunting_0008_15195.jpg test completed - 7\n", + "Masking Lazuli_Bunting_0084_14815.jpg test completed - 8\n", + "Masking Lazuli_Bunting_0092_14656.jpg test completed - 9\n", + "Masking Lazuli_Bunting_0093_15030.jpg test completed - 10\n", + "Masking Lazuli_Bunting_0073_14594.jpg test completed - 11\n", + "Masking Lazuli_Bunting_0080_14893.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/016.Painted_Bunting\n", + "Train indexes: [ 0 36 24 18 2 4 41 52 30 54 19 25 13 12 56 49 6 20 1 7 47 15 26 46\n", + " 5 9 3 21 45 53 35 27 14 23]\n", + "Validation indexes: [48 50 57 8 22 32 33 44 11 10 51 37]\n", + "Test indexes: [17 28 39 31 29 34 16 55 42 40 38 43]\n", + "Masking Painted_Bunting_0001_16585.jpg train completed - 1\n", + "Masking Painted_Bunting_0071_15209.jpg train completed - 2\n", + "Masking Painted_Bunting_0049_16869.jpg train completed - 3\n", + "Masking Painted_Bunting_0034_15207.jpg train completed - 4\n", + "Masking Painted_Bunting_0004_16641.jpg train completed - 5\n", + "Masking Painted_Bunting_0006_15249.jpg train completed - 6\n", + "Masking Painted_Bunting_0078_16565.jpg train completed - 7\n", + "Masking Painted_Bunting_0094_16467.jpg train completed - 8\n", + "Masking Painted_Bunting_0058_16719.jpg train completed - 9\n", + "Masking Painted_Bunting_0098_15226.jpg train completed - 10\n", + "Masking Painted_Bunting_0036_16563.jpg train completed - 11\n", + "Masking Painted_Bunting_0050_16670.jpg train completed - 12\n", 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27\n", + "Masking Painted_Bunting_0040_16691.jpg train completed - 28\n", + "Masking Painted_Bunting_0083_16587.jpg train completed - 29\n", + "Masking Painted_Bunting_0096_15233.jpg train completed - 30\n", + "Masking Painted_Bunting_0070_16515.jpg train completed - 31\n", + "Masking Painted_Bunting_0054_16711.jpg train completed - 32\n", + "Masking Painted_Bunting_0027_16536.jpg train completed - 33\n", + "Masking Painted_Bunting_0046_16535.jpg train completed - 34\n", + "Masking Painted_Bunting_0086_16540.jpg valid completed - 1\n", + "Masking Painted_Bunting_0091_15198.jpg valid completed - 2\n", + "Masking Painted_Bunting_0102_16642.jpg valid completed - 3\n", + "Masking Painted_Bunting_0011_16690.jpg valid completed - 4\n", + "Masking Painted_Bunting_0044_16557.jpg valid completed - 5\n", + "Masking Painted_Bunting_0061_16930.jpg valid completed - 6\n", + "Masking Painted_Bunting_0066_15241.jpg valid completed - 7\n", + "Masking Painted_Bunting_0081_15230.jpg valid completed - 8\n", + "Masking Painted_Bunting_0019_15231.jpg valid completed - 9\n", + "Masking Painted_Bunting_0016_15200.jpg valid completed - 10\n", + "Masking Painted_Bunting_0093_15212.jpg valid completed - 11\n", + "Masking Painted_Bunting_0072_16697.jpg valid completed - 12\n", + "Masking Painted_Bunting_0032_16605.jpg test completed - 1\n", + "Masking Painted_Bunting_0055_15208.jpg test completed - 2\n", + "Masking Painted_Bunting_0076_16765.jpg test completed - 3\n", + "Masking Painted_Bunting_0060_15224.jpg test completed - 4\n", + "Masking Painted_Bunting_0056_16599.jpg test completed - 5\n", + "Masking Painted_Bunting_0069_16462.jpg test completed - 6\n", + "Masking Painted_Bunting_0029_16530.jpg test completed - 7\n", + "Masking Painted_Bunting_0099_16525.jpg test completed - 8\n", + "Masking Painted_Bunting_0079_15197.jpg test completed - 9\n", + "Masking Painted_Bunting_0077_16819.jpg test completed - 10\n", + "Masking Painted_Bunting_0073_16737.jpg test completed - 11\n", + "Masking 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6\n", + "Masking Gray_Catbird_0044_20955.jpg valid completed - 7\n", + "Masking Gray_Catbird_0100_20674.jpg valid completed - 8\n", + "Masking Gray_Catbird_0104_20716.jpg valid completed - 9\n", + "Masking Gray_Catbird_0103_20930.jpg valid completed - 10\n", + "Masking Gray_Catbird_0138_20945.jpg valid completed - 11\n", + "Masking Gray_Catbird_0031_21635.jpg valid completed - 12\n", + "Masking Gray_Catbird_0090_21464.jpg test completed - 1\n", + "Masking Gray_Catbird_0071_20974.jpg test completed - 2\n", + "Masking Gray_Catbird_0024_20739.jpg test completed - 3\n", + "Masking Gray_Catbird_0125_19833.jpg test completed - 4\n", + "Masking Gray_Catbird_0069_21065.jpg test completed - 5\n", + "Masking Gray_Catbird_0126_19446.jpg test completed - 6\n", + "Masking Gray_Catbird_0113_21270.jpg test completed - 7\n", + "Masking Gray_Catbird_0101_21178.jpg test completed - 8\n", + "Masking Gray_Catbird_0074_19601.jpg test completed - 9\n", + "Masking Gray_Catbird_0045_20950.jpg test completed - 10\n", + "Masking Gray_Catbird_0111_19550.jpg test completed - 11\n", + "Masking Gray_Catbird_0001_20695.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/020.Yellow_breasted_Chat\n", + "Train indexes: [33 51 49 2 40 19 25 41 10 43 0 14 34 28 31 23 3 35 15 26 29 18 46 56\n", + " 58 5 36 4 45 13 27 7 55 48 9]\n", + "Validation indexes: [24 16 37 17 47 42 38 22 57 50 21 32]\n", + "Test indexes: [53 1 52 30 54 8 44 12 11 6 20 39]\n", + "Masking Yellow_Breasted_Chat_0073_21932.jpg train completed - 1\n", + "Masking Yellow_Breasted_Chat_0101_21677.jpg train completed - 2\n", + "Masking Yellow_Breasted_Chat_0098_21987.jpg train completed - 3\n", + "Masking Yellow_Breasted_Chat_0005_21828.jpg train completed - 4\n", + "Masking Yellow_Breasted_Chat_0086_21877.jpg train completed - 5\n", + "Masking Yellow_Breasted_Chat_0039_21654.jpg train completed - 6\n", + "Masking Yellow_Breasted_Chat_0061_21967.jpg train completed - 7\n", + "Masking Yellow_Breasted_Chat_0087_21695.jpg 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"/kaggle/working/CUB_200_2011/images/022.Chuck_will_Widow\n", + "Train indexes: [47 14 4 5 54 17 9 1 13 50 36 24 18 40 51 0 38 32 55 21 27 53 25 49\n", + " 46 48 31 10 42 12 33 11 30]\n", + "Validation indexes: [ 8 37 7 41 16 43 35 22 39 20 23]\n", + "Test indexes: [29 34 26 15 2 28 45 44 6 19 52 3]\n", + "Masking Chuck_Will_Widow_0051_796991.jpg train completed - 1\n", + "Masking Chuck_Will_Widow_0016_796974.jpg train completed - 2\n", + "Masking Chuck_Will_Widow_0005_796998.jpg train completed - 3\n", + "Masking Chuck_Will_Widow_0006_796986.jpg train completed - 4\n", + "Masking Chuck_Will_Widow_0058_796999.jpg train completed - 5\n", + "Masking Chuck_Will_Widow_0019_22806.jpg train completed - 6\n", + "Masking Chuck_Will_Widow_0010_796964.jpg train completed - 7\n", + "Masking Chuck_Will_Widow_0002_796959.jpg train completed - 8\n", + "Masking Chuck_Will_Widow_0015_796962.jpg train completed - 9\n", + "Masking Chuck_Will_Widow_0054_22782.jpg train completed - 10\n", + "Masking 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Pelagic_Cormorant_0024_23712.jpg valid completed - 11\n", + "Masking Pelagic_Cormorant_0055_23415.jpg valid completed - 12\n", + "Masking Pelagic_Cormorant_0099_23784.jpg test completed - 1\n", + "Masking Pelagic_Cormorant_0009_23561.jpg test completed - 2\n", + "Masking Pelagic_Cormorant_0010_23711.jpg test completed - 3\n", + "Masking Pelagic_Cormorant_0088_23855.jpg test completed - 4\n", + "Masking Pelagic_Cormorant_0054_23812.jpg test completed - 5\n", + "Masking Pelagic_Cormorant_0019_23546.jpg test completed - 6\n", + "Masking Pelagic_Cormorant_0062_23623.jpg test completed - 7\n", + "Masking Pelagic_Cormorant_0061_23548.jpg test completed - 8\n", + "Masking Pelagic_Cormorant_0100_23894.jpg test completed - 9\n", + "Masking Pelagic_Cormorant_0011_23667.jpg test completed - 10\n", + "Masking Pelagic_Cormorant_0074_23511.jpg test completed - 11\n", + "Masking Pelagic_Cormorant_0075_23645.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/026.Bronzed_Cowbird\n", + "Train indexes: [23 27 3 30 13 35 29 21 19 32 0 50 46 44 41 20 4 12 57 9 11 48 43 18\n", + " 1 25 34 16 38 52 26 28 51 33 36 15]\n", + "Validation indexes: [58 56 53 24 54 10 37 31 47 8 2 40]\n", + "Test indexes: [22 14 49 42 45 7 59 5 17 6 55 39]\n", + "Masking Bronzed_Cowbird_0029_796256.jpg train completed - 1\n", + "Masking Bronzed_Cowbird_0034_796209.jpg train completed - 2\n", + "Masking Bronzed_Cowbird_0005_24173.jpg train completed - 3\n", + "Masking Bronzed_Cowbird_0040_24134.jpg train completed - 4\n", + "Masking Bronzed_Cowbird_0018_24140.jpg train completed - 5\n", + "Masking Bronzed_Cowbird_0051_24083.jpg train completed - 6\n", + "Masking Bronzed_Cowbird_0039_24026.jpg train completed - 7\n", + "Masking Bronzed_Cowbird_0026_796229.jpg train completed - 8\n", + "Masking Bronzed_Cowbird_0024_24167.jpg train completed - 9\n", + "Masking Bronzed_Cowbird_0044_24145.jpg train completed - 10\n", + "Masking Bronzed_Cowbird_0001_796219.jpg train completed - 11\n", + "Masking 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5\n", + "Masking Bronzed_Cowbird_0014_24030.jpg valid completed - 6\n", + "Masking Bronzed_Cowbird_0053_24170.jpg valid completed - 7\n", + "Masking Bronzed_Cowbird_0043_796224.jpg valid completed - 8\n", + "Masking Bronzed_Cowbird_0073_796226.jpg valid completed - 9\n", + "Masking Bronzed_Cowbird_0012_796247.jpg valid completed - 10\n", + "Masking Bronzed_Cowbird_0003_796246.jpg valid completed - 11\n", + "Masking Bronzed_Cowbird_0056_796222.jpg valid completed - 12\n", + "Masking Bronzed_Cowbird_0027_24022.jpg test completed - 1\n", + "Masking Bronzed_Cowbird_0019_796242.jpg test completed - 2\n", + "Masking Bronzed_Cowbird_0076_796235.jpg test completed - 3\n", + "Masking Bronzed_Cowbird_0058_796239.jpg test completed - 4\n", + "Masking Bronzed_Cowbird_0062_796238.jpg test completed - 5\n", + "Masking Bronzed_Cowbird_0011_24138.jpg test completed - 6\n", + "Masking Bronzed_Cowbird_0092_796215.jpg test completed - 7\n", + "Masking Bronzed_Cowbird_0009_24033.jpg test completed - 8\n", 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completed - 10\n", + "Masking Fish_Crow_0072_25945.jpg valid completed - 11\n", + "Masking Fish_Crow_0080_25861.jpg valid completed - 12\n", + "Masking Fish_Crow_0059_25864.jpg test completed - 1\n", + "Masking Fish_Crow_0083_25949.jpg test completed - 2\n", + "Masking Fish_Crow_0078_26144.jpg test completed - 3\n", + "Masking Fish_Crow_0055_26077.jpg test completed - 4\n", + "Masking Fish_Crow_0037_26071.jpg test completed - 5\n", + "Masking Fish_Crow_0068_25859.jpg test completed - 6\n", + "Masking Fish_Crow_0005_25912.jpg test completed - 7\n", + "Masking Fish_Crow_0069_26008.jpg test completed - 8\n", + "Masking Fish_Crow_0056_25851.jpg test completed - 9\n", + "Masking Fish_Crow_0032_26014.jpg test completed - 10\n", + "Masking Fish_Crow_0053_26067.jpg test completed - 11\n", + "Masking Fish_Crow_0058_25999.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/031.Black_billed_Cuckoo\n", + "Train indexes: [51 56 3 52 6 23 31 40 48 54 47 45 33 29 19 49 25 58 9 28 32 35 2 22\n", + " 1 11 14 42 53 16 43 36 13 5 38 39]\n", + "Validation indexes: [10 41 24 55 34 20 18 57 12 46 21 4]\n", + "Test indexes: [50 17 15 37 8 7 59 44 0 30 26 27]\n", + "Masking Black_Billed_Cuckoo_0087_795300.jpg train completed - 1\n", + "Masking Black_Billed_Cuckoo_0092_795313.jpg train completed - 2\n", + "Masking Black_Billed_Cuckoo_0007_26320.jpg train completed - 3\n", + "Masking Black_Billed_Cuckoo_0088_26217.jpg train completed - 4\n", + "Masking Black_Billed_Cuckoo_0017_26221.jpg train completed - 5\n", + "Masking Black_Billed_Cuckoo_0046_795328.jpg train completed - 6\n", + "Masking Black_Billed_Cuckoo_0058_795292.jpg train completed - 7\n", + "Masking Black_Billed_Cuckoo_0074_795286.jpg train completed - 8\n", + "Masking Black_Billed_Cuckoo_0084_26175.jpg train completed - 9\n", + "Masking Black_Billed_Cuckoo_0090_26311.jpg train completed - 10\n", + "Masking Black_Billed_Cuckoo_0083_795315.jpg train completed - 11\n", + "Masking Black_Billed_Cuckoo_0081_26209.jpg 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completed - 6\n", + "Masking Mangrove_Cuckoo_0017_794623.jpg test completed - 7\n", + "Masking Mangrove_Cuckoo_0031_26401.jpg test completed - 8\n", + "Masking Mangrove_Cuckoo_0048_26351.jpg test completed - 9\n", + "Masking Mangrove_Cuckoo_0014_26388.jpg test completed - 10\n", + "Masking Mangrove_Cuckoo_0044_26393.jpg test completed - 11\n", + "/kaggle/working/CUB_200_2011/images/033.Yellow_billed_Cuckoo\n", + "Train indexes: [ 8 9 3 20 10 28 40 14 22 47 0 53 46 17 6 24 37 11 42 29 31 54 5 43\n", + " 48 35 39 49 13 30 58 23 18 41 15]\n", + "Validation indexes: [50 34 7 16 25 36 1 19 12 26 52 57]\n", + "Test indexes: [38 51 33 45 56 2 4 21 44 27 32 55]\n", + "Masking Yellow_Billed_Cuckoo_0012_26712.jpg train completed - 1\n", + "Masking Yellow_Billed_Cuckoo_0014_26754.jpg train completed - 2\n", + "Masking Yellow_Billed_Cuckoo_0005_26684.jpg train completed - 3\n", + "Masking Yellow_Billed_Cuckoo_0033_26521.jpg train completed - 4\n", + "Masking Yellow_Billed_Cuckoo_0018_26535.jpg 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"Masking Yellow_Billed_Cuckoo_0119_26550.jpg valid completed - 12\n", + "Masking Yellow_Billed_Cuckoo_0073_26744.jpg test completed - 1\n", + "Masking Yellow_Billed_Cuckoo_0097_26713.jpg test completed - 2\n", + "Masking Yellow_Billed_Cuckoo_0060_26686.jpg test completed - 3\n", + "Masking Yellow_Billed_Cuckoo_0088_26812.jpg test completed - 4\n", + "Masking Yellow_Billed_Cuckoo_0117_26651.jpg test completed - 5\n", + "Masking Yellow_Billed_Cuckoo_0004_26790.jpg test completed - 6\n", + "Masking Yellow_Billed_Cuckoo_0006_26578.jpg test completed - 7\n", + "Masking Yellow_Billed_Cuckoo_0034_26694.jpg test completed - 8\n", + "Masking Yellow_Billed_Cuckoo_0084_26761.jpg test completed - 9\n", + "Masking Yellow_Billed_Cuckoo_0045_26685.jpg test completed - 10\n", + "Masking Yellow_Billed_Cuckoo_0059_26828.jpg test completed - 11\n", + "Masking Yellow_Billed_Cuckoo_0116_26544.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/034.Gray_crowned_Rosy_Finch\n", + "Train indexes: [57 45 15 51 5 8 22 20 44 1 58 33 37 14 54 26 49 39 25 32 24 38 29 36\n", + " 11 4 17 6 13 23 2 41 12 16 53]\n", + "Validation indexes: [10 9 0 48 34 55 21 28 40 46 19 30]\n", + "Test indexes: [52 42 18 56 50 31 7 27 35 47 3 43]\n", + "Masking Gray_Crowned_Rosy_Finch_0083_797281.jpg train completed - 1\n", + "Masking Gray_Crowned_Rosy_Finch_0066_797298.jpg train completed - 2\n", + "Masking Gray_Crowned_Rosy_Finch_0024_27057.jpg train completed - 3\n", + "Masking Gray_Crowned_Rosy_Finch_0074_27156.jpg train completed - 4\n", + "Masking Gray_Crowned_Rosy_Finch_0010_27039.jpg train completed - 5\n", + "Masking Gray_Crowned_Rosy_Finch_0015_797291.jpg train completed - 6\n", + "Masking Gray_Crowned_Rosy_Finch_0031_797299.jpg train completed - 7\n", + "Masking Gray_Crowned_Rosy_Finch_0029_797300.jpg train completed - 8\n", + "Masking Gray_Crowned_Rosy_Finch_0065_797301.jpg train completed - 9\n", + "Masking Gray_Crowned_Rosy_Finch_0004_797272.jpg train completed - 10\n", + "Masking 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Gray_Crowned_Rosy_Finch_0016_27181.jpg valid completed - 2\n", + "Masking Gray_Crowned_Rosy_Finch_0001_27211.jpg valid completed - 3\n", + "Masking Gray_Crowned_Rosy_Finch_0071_797285.jpg valid completed - 4\n", + "Masking Gray_Crowned_Rosy_Finch_0048_27236.jpg valid completed - 5\n", + "Masking Gray_Crowned_Rosy_Finch_0079_797294.jpg valid completed - 6\n", + "Masking Gray_Crowned_Rosy_Finch_0030_27068.jpg valid completed - 7\n", + "Masking Gray_Crowned_Rosy_Finch_0041_27105.jpg valid completed - 8\n", + "Masking Gray_Crowned_Rosy_Finch_0057_27107.jpg valid completed - 9\n", + "Masking Gray_Crowned_Rosy_Finch_0067_797289.jpg valid completed - 10\n", + "Masking Gray_Crowned_Rosy_Finch_0028_27114.jpg valid completed - 11\n", + "Masking Gray_Crowned_Rosy_Finch_0043_26990.jpg valid completed - 12\n", + "Masking Gray_Crowned_Rosy_Finch_0075_27165.jpg test completed - 1\n", + "Masking Gray_Crowned_Rosy_Finch_0063_27123.jpg test completed - 2\n", + "Masking Gray_Crowned_Rosy_Finch_0027_27153.jpg test completed - 3\n", + "Masking Gray_Crowned_Rosy_Finch_0080_27108.jpg test completed - 4\n", + "Masking Gray_Crowned_Rosy_Finch_0073_27104.jpg test completed - 5\n", + "Masking Gray_Crowned_Rosy_Finch_0044_26976.jpg test completed - 6\n", + "Masking Gray_Crowned_Rosy_Finch_0013_27110.jpg test completed - 7\n", + "Masking Gray_Crowned_Rosy_Finch_0040_26985.jpg test completed - 8\n", + "Masking Gray_Crowned_Rosy_Finch_0051_26988.jpg test completed - 9\n", + "Masking Gray_Crowned_Rosy_Finch_0068_27196.jpg test completed - 10\n", + "Masking Gray_Crowned_Rosy_Finch_0007_797278.jpg test completed - 11\n", + "Masking Gray_Crowned_Rosy_Finch_0064_27007.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/035.Purple_Finch\n", + "Train indexes: [13 29 50 17 41 36 27 51 32 34 59 33 43 5 38 46 42 0 48 44 40 57 12 15\n", + " 55 3 58 7 2 4 24 26 6 20 49 52]\n", + "Validation indexes: [28 30 35 14 25 45 19 16 37 47 21 1]\n", + "Test indexes: [11 31 18 39 23 54 56 9 53 10 8 22]\n", + "Masking Purple_Finch_0030_27255.jpg train completed - 1\n", + "Masking Purple_Finch_0074_28101.jpg train completed - 2\n", + "Masking Purple_Finch_0114_27837.jpg train completed - 3\n", + "Masking Purple_Finch_0035_27972.jpg train completed - 4\n", + "Masking Purple_Finch_0098_27280.jpg train completed - 5\n", + "Masking Purple_Finch_0091_27425.jpg train completed - 6\n", + "Masking Purple_Finch_0067_27431.jpg train completed - 7\n", + "Masking Purple_Finch_0115_28190.jpg train completed - 8\n", + "Masking Purple_Finch_0081_27913.jpg train completed - 9\n", + "Masking Purple_Finch_0086_27637.jpg train completed - 10\n", + "Masking Purple_Finch_0134_27526.jpg train completed - 11\n", + "Masking Purple_Finch_0082_27639.jpg train completed - 12\n", + "Masking Purple_Finch_0103_27461.jpg train completed - 13\n", + "Masking Purple_Finch_0008_27455.jpg train completed - 14\n", + "Masking Purple_Finch_0094_27323.jpg train completed - 15\n", + "Masking 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completed - 31\n", + "Masking Purple_Finch_0065_27473.jpg train completed - 32\n", + "Masking Purple_Finch_0011_27633.jpg train completed - 33\n", + "Masking Purple_Finch_0046_27295.jpg train completed - 34\n", + "Masking Purple_Finch_0113_27547.jpg train completed - 35\n", + "Masking Purple_Finch_0117_27427.jpg train completed - 36\n", + "Masking Purple_Finch_0071_27443.jpg valid completed - 1\n", + "Masking Purple_Finch_0076_27441.jpg valid completed - 2\n", + "Masking Purple_Finch_0088_28076.jpg valid completed - 3\n", + "Masking Purple_Finch_0031_28175.jpg valid completed - 4\n", + "Masking Purple_Finch_0059_27966.jpg valid completed - 5\n", + "Masking Purple_Finch_0106_27607.jpg valid completed - 6\n", + "Masking Purple_Finch_0043_28117.jpg valid completed - 7\n", + "Masking Purple_Finch_0033_27926.jpg valid completed - 8\n", + "Masking Purple_Finch_0092_27264.jpg valid completed - 9\n", + "Masking Purple_Finch_0110_27750.jpg valid completed - 10\n", + "Masking Purple_Finch_0048_27670.jpg valid completed - 11\n", + "Masking Purple_Finch_0002_27266.jpg valid completed - 12\n", + "Masking Purple_Finch_0023_27986.jpg test completed - 1\n", + "Masking Purple_Finch_0079_27258.jpg test completed - 2\n", + "Masking Purple_Finch_0036_27641.jpg test completed - 3\n", + "Masking Purple_Finch_0096_27688.jpg test completed - 4\n", + "Masking Purple_Finch_0050_27702.jpg test completed - 5\n", + "Masking Purple_Finch_0119_27260.jpg test completed - 6\n", + "Masking Purple_Finch_0124_27567.jpg test completed - 7\n", + "Masking Purple_Finch_0020_27961.jpg test completed - 8\n", + "Masking Purple_Finch_0118_27604.jpg test completed - 9\n", + "Masking Purple_Finch_0021_28235.jpg test completed - 10\n", + "Masking Purple_Finch_0014_27322.jpg test completed - 11\n", + "Masking Purple_Finch_0049_27507.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/036.Northern_Flicker\n", + "Train indexes: [39 5 30 31 0 40 7 44 28 4 32 20 26 38 1 23 41 13 24 59 27 9 56 25\n", + " 42 47 45 2 51 46 17 33 53 55 11 8]\n", + "Validation indexes: [36 34 37 22 48 49 50 29 14 52 12 19]\n", + "Test indexes: [35 3 58 18 57 16 43 54 6 15 21 10]\n", + "Masking Northern_Flicker_0099_28312.jpg train completed - 1\n", + "Masking Northern_Flicker_0021_28741.jpg train completed - 2\n", + "Masking Northern_Flicker_0084_28318.jpg train completed - 3\n", + "Masking Northern_Flicker_0085_28378.jpg train completed - 4\n", + "Masking Northern_Flicker_0004_28854.jpg train completed - 5\n", + "Masking Northern_Flicker_0100_28898.jpg train completed - 6\n", + "Masking Northern_Flicker_0024_29041.jpg train completed - 7\n", + "Masking Northern_Flicker_0107_28667.jpg train completed - 8\n", + "Masking Northern_Flicker_0078_28338.jpg train completed - 9\n", + "Masking Northern_Flicker_0016_28603.jpg train completed - 10\n", + "Masking Northern_Flicker_0086_28360.jpg train completed - 11\n", + "Masking Northern_Flicker_0055_28344.jpg train completed - 12\n", + "Masking 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Acadian_Flycatcher_0050_795608.jpg test completed - 2\n", + "Masking Acadian_Flycatcher_0043_29115.jpg test completed - 3\n", + "Masking Acadian_Flycatcher_0009_29155.jpg test completed - 4\n", + "Masking Acadian_Flycatcher_0040_795629.jpg test completed - 5\n", + "Masking Acadian_Flycatcher_0028_795611.jpg test completed - 6\n", + "Masking Acadian_Flycatcher_0030_795591.jpg test completed - 7\n", + "Masking Acadian_Flycatcher_0045_795587.jpg test completed - 8\n", + "Masking Acadian_Flycatcher_0036_795577.jpg test completed - 9\n", + "Masking Acadian_Flycatcher_0051_29077.jpg test completed - 10\n", + "Masking Acadian_Flycatcher_0035_795618.jpg test completed - 11\n", + "Masking Acadian_Flycatcher_0059_29102.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/038.Great_Crested_Flycatcher\n", + "Train indexes: [17 11 38 59 58 20 26 23 35 6 44 43 39 16 48 15 27 52 24 1 12 14 55 0\n", + " 40 5 46 18 13 33 2 57 30 49 45 54]\n", + "Validation indexes: [41 42 36 4 22 7 34 8 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"/kaggle/working/CUB_200_2011/images/040.Olive_sided_Flycatcher\n", + "Train indexes: [44 40 48 13 18 46 56 31 4 20 19 45 34 38 23 9 7 8 6 41 39 14 5 24\n", + " 30 12 52 28 0 58 33 29 47 32 1 42]\n", + "Validation indexes: [15 26 51 49 43 50 36 37 53 10 16 11]\n", + "Test indexes: [ 2 55 35 22 3 57 59 21 54 25 27 17]\n", + "Masking Olive_Sided_Flycatcher_0064_30485.jpg train completed - 1\n", + "Masking Olive_Sided_Flycatcher_0057_796898.jpg train completed - 2\n", + "Masking Olive_Sided_Flycatcher_0071_30647.jpg train completed - 3\n", + "Masking Olive_Sided_Flycatcher_0021_796880.jpg train completed - 4\n", + "Masking Olive_Sided_Flycatcher_0027_30549.jpg train completed - 5\n", + "Masking Olive_Sided_Flycatcher_0069_30544.jpg train completed - 6\n", + "Masking Olive_Sided_Flycatcher_0080_30515.jpg train completed - 7\n", + "Masking Olive_Sided_Flycatcher_0045_796888.jpg train completed - 8\n", + "Masking Olive_Sided_Flycatcher_0008_30686.jpg train completed - 9\n", + "Masking 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55 8 34 22 51 33]\n", + "Validation indexes: [28 7 58 9 39 47 52 24 53 30 5 48]\n", + "Test indexes: [20 59 2 18 10 15 0 32 40 31 19 49]\n", + "Masking Scissor_Tailed_Flycatcher_0103_41938.jpg train completed - 1\n", + "Masking Scissor_Tailed_Flycatcher_0058_41948.jpg train completed - 2\n", + "Masking Scissor_Tailed_Flycatcher_0123_41638.jpg train completed - 3\n", + "Masking Scissor_Tailed_Flycatcher_0092_41767.jpg train completed - 4\n", + "Masking Scissor_Tailed_Flycatcher_0005_41727.jpg train completed - 5\n", + "Masking Scissor_Tailed_Flycatcher_0048_41646.jpg train completed - 6\n", + "Masking Scissor_Tailed_Flycatcher_0059_41998.jpg train completed - 7\n", + "Masking Scissor_Tailed_Flycatcher_0089_41810.jpg train completed - 8\n", + "Masking Scissor_Tailed_Flycatcher_0021_41931.jpg train completed - 9\n", + "Masking Scissor_Tailed_Flycatcher_0119_41879.jpg train completed - 10\n", + "Masking Scissor_Tailed_Flycatcher_0088_41700.jpg train completed - 11\n", + "Masking 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completed - 5\n", + "Masking Vermilion_Flycatcher_0045_42219.jpg valid completed - 6\n", + "Masking Vermilion_Flycatcher_0022_42559.jpg valid completed - 7\n", + "Masking Vermilion_Flycatcher_0026_42375.jpg valid completed - 8\n", + "Masking Vermilion_Flycatcher_0009_42234.jpg valid completed - 9\n", + "Masking Vermilion_Flycatcher_0002_42390.jpg valid completed - 10\n", + "Masking Vermilion_Flycatcher_0018_42474.jpg valid completed - 11\n", + "Masking Vermilion_Flycatcher_0048_42298.jpg valid completed - 12\n", + "Masking Vermilion_Flycatcher_0073_42573.jpg test completed - 1\n", + "Masking Vermilion_Flycatcher_0036_42389.jpg test completed - 2\n", + "Masking Vermilion_Flycatcher_0055_42442.jpg test completed - 3\n", + "Masking Vermilion_Flycatcher_0040_42398.jpg test completed - 4\n", + "Masking Vermilion_Flycatcher_0019_42241.jpg test completed - 5\n", + "Masking Vermilion_Flycatcher_0035_42282.jpg test completed - 6\n", + "Masking Vermilion_Flycatcher_0007_42214.jpg test completed - 7\n", + "Masking Vermilion_Flycatcher_0005_42478.jpg test completed - 8\n", + "Masking Vermilion_Flycatcher_0014_42533.jpg test completed - 9\n", + "Masking Vermilion_Flycatcher_0039_42423.jpg test completed - 10\n", + "Masking Vermilion_Flycatcher_0024_42506.jpg test completed - 11\n", + "Masking Vermilion_Flycatcher_0057_42562.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/043.Yellow_bellied_Flycatcher\n", + "Train indexes: [23 33 57 20 40 45 49 28 44 27 52 53 15 6 24 41 26 8 56 47 35 17 55 2\n", + " 12 50 54 5 21 42 58 19 32 39 30]\n", + "Validation indexes: [13 36 46 0 9 34 31 37 48 11 4 22]\n", + "Test indexes: [14 43 16 3 1 38 7 25 10 51 18 29]\n", + "Masking Yellow_Bellied_Flycatcher_0026_795489.jpg train completed - 1\n", + "Masking Yellow_Bellied_Flycatcher_0037_795500.jpg train completed - 2\n", + "Masking Yellow_Bellied_Flycatcher_0066_795514.jpg train completed - 3\n", + "Masking Yellow_Bellied_Flycatcher_0023_795478.jpg train completed - 4\n", + 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completed - 12\n", + "/kaggle/working/CUB_200_2011/images/048.European_Goldfinch\n", + "Train indexes: [56 55 49 42 26 51 59 47 35 34 18 54 19 8 20 43 12 33 4 17 13 7 24 25\n", + " 53 15 27 39 1 38 46 36 21 32 22 28]\n", + "Validation indexes: [30 23 6 48 10 45 52 9 44 5 11 14]\n", + "Test indexes: [41 58 16 37 29 0 31 3 57 40 50 2]\n", + "Masking European_Goldfinch_0104_794634.jpg train completed - 1\n", + "Masking European_Goldfinch_0103_794664.jpg train completed - 2\n", + "Masking European_Goldfinch_0093_794653.jpg train completed - 3\n", + "Masking European_Goldfinch_0084_33147.jpg train completed - 4\n", + "Masking European_Goldfinch_0053_794639.jpg train completed - 5\n", + "Masking European_Goldfinch_0097_794688.jpg train completed - 6\n", + "Masking European_Goldfinch_0108_33176.jpg train completed - 7\n", + "Masking European_Goldfinch_0090_794648.jpg train completed - 8\n", + "Masking European_Goldfinch_0074_33348.jpg train completed - 9\n", + "Masking 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completed - 2\n", + "Masking Horned_Grebe_0088_35023.jpg valid completed - 3\n", + "Masking Horned_Grebe_0006_34718.jpg valid completed - 4\n", + "Masking Horned_Grebe_0057_34590.jpg valid completed - 5\n", + "Masking Horned_Grebe_0085_34713.jpg valid completed - 6\n", + "Masking Horned_Grebe_0050_34561.jpg valid completed - 7\n", + "Masking Horned_Grebe_0062_34523.jpg valid completed - 8\n", + "Masking Horned_Grebe_0069_34990.jpg valid completed - 9\n", + "Masking Horned_Grebe_0011_34687.jpg valid completed - 10\n", + "Masking Horned_Grebe_0078_34799.jpg valid completed - 11\n", + "Masking Horned_Grebe_0066_34738.jpg valid completed - 12\n", + "Masking Horned_Grebe_0098_34662.jpg test completed - 1\n", + "Masking Horned_Grebe_0055_35104.jpg test completed - 2\n", + "Masking Horned_Grebe_0076_34841.jpg test completed - 3\n", + "Masking Horned_Grebe_0084_34936.jpg test completed - 4\n", + "Masking Horned_Grebe_0031_34626.jpg test completed - 5\n", + "Masking Horned_Grebe_0072_34497.jpg 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Pied_Billed_Grebe_0120_35764.jpg train completed - 34\n", + "Masking Pied_Billed_Grebe_0052_35937.jpg train completed - 35\n", + "Masking Pied_Billed_Grebe_0123_35469.jpg train completed - 36\n", + "Masking Pied_Billed_Grebe_0101_35464.jpg valid completed - 1\n", + "Masking Pied_Billed_Grebe_0037_35598.jpg valid completed - 2\n", + "Masking Pied_Billed_Grebe_0041_35224.jpg valid completed - 3\n", + "Masking Pied_Billed_Grebe_0070_35472.jpg valid completed - 4\n", + "Masking Pied_Billed_Grebe_0005_35437.jpg valid completed - 5\n", + "Masking Pied_Billed_Grebe_0122_35970.jpg valid completed - 6\n", + "Masking Pied_Billed_Grebe_0020_35958.jpg valid completed - 7\n", + "Masking Pied_Billed_Grebe_0071_35386.jpg valid completed - 8\n", + "Masking Pied_Billed_Grebe_0059_35507.jpg valid completed - 9\n", + "Masking Pied_Billed_Grebe_0024_35949.jpg valid completed - 10\n", + "Masking Pied_Billed_Grebe_0095_35496.jpg valid completed - 11\n", + "Masking Pied_Billed_Grebe_0106_35418.jpg valid completed - 12\n", + "Masking Pied_Billed_Grebe_0056_35623.jpg test completed - 1\n", + "Masking Pied_Billed_Grebe_0029_35551.jpg test completed - 2\n", + "Masking Pied_Billed_Grebe_0065_35713.jpg test completed - 3\n", + "Masking Pied_Billed_Grebe_0114_35493.jpg test completed - 4\n", + "Masking Pied_Billed_Grebe_0062_35955.jpg test completed - 5\n", + "Masking Pied_Billed_Grebe_0115_35362.jpg test completed - 6\n", + "Masking Pied_Billed_Grebe_0040_35981.jpg test completed - 7\n", + "Masking Pied_Billed_Grebe_0044_35425.jpg test completed - 8\n", + "Masking Pied_Billed_Grebe_0032_35931.jpg test completed - 9\n", + "Masking Pied_Billed_Grebe_0063_35529.jpg test completed - 10\n", + "Masking Pied_Billed_Grebe_0045_35962.jpg test completed - 11\n", + "Masking Pied_Billed_Grebe_0035_35518.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/053.Western_Grebe\n", + "Train indexes: [52 47 1 31 41 24 25 35 0 45 59 11 21 3 8 42 16 53 55 18 49 40 58 28\n", + " 19 54 50 26 29 6 14 51 37 5 2 12]\n", + "Validation indexes: [39 9 34 44 4 48 33 32 57 13 10 56]\n", + "Test indexes: [38 27 23 17 30 36 43 7 20 46 22 15]\n", + "Masking Western_Grebe_0092_36121.jpg train completed - 1\n", + "Masking Western_Grebe_0087_36570.jpg train completed - 2\n", + "Masking Western_Grebe_0002_36518.jpg train completed - 3\n", + "Masking Western_Grebe_0056_36216.jpg train completed - 4\n", + "Masking Western_Grebe_0077_36355.jpg train completed - 5\n", + "Masking Western_Grebe_0045_36425.jpg train completed - 6\n", + "Masking Western_Grebe_0046_36118.jpg train completed - 7\n", + "Masking Western_Grebe_0064_36613.jpg train completed - 8\n", + "Masking Western_Grebe_0001_36481.jpg train completed - 9\n", + "Masking Western_Grebe_0085_36224.jpg train completed - 10\n", + "Masking Western_Grebe_0106_36509.jpg train completed - 11\n", + "Masking Western_Grebe_0022_36148.jpg train completed - 12\n", + "Masking Western_Grebe_0042_36035.jpg train completed - 13\n", + "Masking 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Western_Grebe_0050_36163.jpg train completed - 29\n", + "Masking Western_Grebe_0013_36383.jpg train completed - 30\n", + "Masking Western_Grebe_0029_36379.jpg train completed - 31\n", + "Masking Western_Grebe_0091_36194.jpg train completed - 32\n", + "Masking Western_Grebe_0069_36486.jpg train completed - 33\n", + "Masking Western_Grebe_0011_36522.jpg train completed - 34\n", + "Masking Western_Grebe_0004_36130.jpg train completed - 35\n", + "Masking Western_Grebe_0025_36251.jpg train completed - 36\n", + "Masking Western_Grebe_0071_36536.jpg valid completed - 1\n", + "Masking Western_Grebe_0020_36241.jpg valid completed - 2\n", + "Masking Western_Grebe_0061_36181.jpg valid completed - 3\n", + "Masking Western_Grebe_0083_36033.jpg valid completed - 4\n", + "Masking Western_Grebe_0009_36477.jpg valid completed - 5\n", + "Masking Western_Grebe_0088_36428.jpg valid completed - 6\n", + "Masking Western_Grebe_0058_36403.jpg valid completed - 7\n", + "Masking Western_Grebe_0057_36157.jpg valid completed - 8\n", + "Masking Western_Grebe_0104_36164.jpg valid completed - 9\n", + "Masking Western_Grebe_0028_36196.jpg valid completed - 10\n", + "Masking Western_Grebe_0021_36282.jpg valid completed - 11\n", + "Masking Western_Grebe_0103_36515.jpg valid completed - 12\n", + "Masking Western_Grebe_0070_36162.jpg test completed - 1\n", + "Masking Western_Grebe_0048_36399.jpg test completed - 2\n", + "Masking Western_Grebe_0044_36188.jpg test completed - 3\n", + "Masking Western_Grebe_0034_36149.jpg test completed - 4\n", + "Masking Western_Grebe_0051_36249.jpg test completed - 5\n", + "Masking Western_Grebe_0067_36610.jpg test completed - 6\n", + "Masking Western_Grebe_0081_36578.jpg test completed - 7\n", + "Masking Western_Grebe_0014_36185.jpg test completed - 8\n", + "Masking Western_Grebe_0038_36363.jpg test completed - 9\n", + "Masking Western_Grebe_0086_36478.jpg test completed - 10\n", + "Masking Western_Grebe_0043_36183.jpg test completed - 11\n", + "Masking Western_Grebe_0032_36439.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/054.Blue_Grosbeak\n", + "Train indexes: [14 21 38 29 30 5 15 33 43 41 54 53 49 56 48 4 17 51 40 46 50 19 12 20\n", + " 8 27 57 11 26 47 44 58 3 39 22 7]\n", + "Validation indexes: [23 55 1 52 24 37 35 6 34 45 28 10]\n", + "Test indexes: [ 9 31 42 18 13 59 36 0 16 32 25 2]\n", + "Masking Blue_Grosbeak_0029_37197.jpg train completed - 1\n", + "Masking Blue_Grosbeak_0042_36742.jpg train completed - 2\n", + "Masking Blue_Grosbeak_0075_36963.jpg train completed - 3\n", + "Masking Blue_Grosbeak_0057_37116.jpg train completed - 4\n", + "Masking Blue_Grosbeak_0061_36633.jpg train completed - 5\n", + "Masking Blue_Grosbeak_0015_36985.jpg train completed - 6\n", + "Masking Blue_Grosbeak_0031_37173.jpg train completed - 7\n", + "Masking Blue_Grosbeak_0067_36965.jpg train completed - 8\n", + "Masking Blue_Grosbeak_0085_36990.jpg train completed - 9\n", + "Masking Blue_Grosbeak_0081_37034.jpg train completed - 10\n", + "Masking Blue_Grosbeak_0104_36984.jpg train completed - 11\n", + "Masking Blue_Grosbeak_0103_36673.jpg train completed - 12\n", + "Masking Blue_Grosbeak_0095_37007.jpg train completed - 13\n", + "Masking Blue_Grosbeak_0107_36696.jpg train completed - 14\n", + "Masking Blue_Grosbeak_0094_36995.jpg train completed - 15\n", + "Masking Blue_Grosbeak_0014_36708.jpg train completed - 16\n", + "Masking Blue_Grosbeak_0036_37048.jpg train completed - 17\n", + "Masking Blue_Grosbeak_0099_36828.jpg train completed - 18\n", + "Masking Blue_Grosbeak_0079_36656.jpg train completed - 19\n", + "Masking Blue_Grosbeak_0092_36711.jpg train completed - 20\n", + "Masking Blue_Grosbeak_0097_37067.jpg train completed - 21\n", + "Masking Blue_Grosbeak_0038_37095.jpg train completed - 22\n", + "Masking Blue_Grosbeak_0025_36646.jpg train completed - 23\n", + "Masking Blue_Grosbeak_0041_37174.jpg train completed - 24\n", + "Masking Blue_Grosbeak_0020_36967.jpg train completed - 25\n", + "Masking Blue_Grosbeak_0051_37065.jpg train completed - 26\n", + "Masking Blue_Grosbeak_0109_36914.jpg train completed - 27\n", + "Masking Blue_Grosbeak_0024_36680.jpg train completed - 28\n", + "Masking Blue_Grosbeak_0049_37180.jpg train completed - 29\n", + "Masking Blue_Grosbeak_0093_37107.jpg train completed - 30\n", + "Masking Blue_Grosbeak_0086_36818.jpg train completed - 31\n", + "Masking Blue_Grosbeak_0111_36841.jpg train completed - 32\n", + "Masking Blue_Grosbeak_0010_37108.jpg train completed - 33\n", + "Masking Blue_Grosbeak_0078_36655.jpg train completed - 34\n", + "Masking Blue_Grosbeak_0043_37200.jpg train completed - 35\n", + "Masking Blue_Grosbeak_0019_37205.jpg train completed - 36\n", + "Masking Blue_Grosbeak_0045_36672.jpg valid completed - 1\n", + "Masking Blue_Grosbeak_0106_36986.jpg valid completed - 2\n", + "Masking Blue_Grosbeak_0004_14988.jpg valid completed - 3\n", + "Masking Blue_Grosbeak_0101_36719.jpg valid completed - 4\n", + "Masking Blue_Grosbeak_0046_37119.jpg valid completed - 5\n", + "Masking Blue_Grosbeak_0074_37155.jpg valid completed - 6\n", + "Masking Blue_Grosbeak_0072_36774.jpg valid completed - 7\n", + "Masking Blue_Grosbeak_0016_36862.jpg valid completed - 8\n", + "Masking Blue_Grosbeak_0071_36948.jpg valid completed - 9\n", + "Masking Blue_Grosbeak_0087_36780.jpg valid completed - 10\n", + "Masking Blue_Grosbeak_0053_36709.jpg valid completed - 11\n", + "Masking Blue_Grosbeak_0023_37069.jpg valid completed - 12\n", + "Masking Blue_Grosbeak_0022_37082.jpg test completed - 1\n", + "Masking Blue_Grosbeak_0065_36847.jpg test completed - 2\n", + "Masking Blue_Grosbeak_0082_36991.jpg test completed - 3\n", + "Masking Blue_Grosbeak_0037_36794.jpg test completed - 4\n", + "Masking Blue_Grosbeak_0027_36703.jpg test completed - 5\n", + "Masking Blue_Grosbeak_0112_36679.jpg test completed - 6\n", + "Masking Blue_Grosbeak_0073_37148.jpg test completed - 7\n", + "Masking Blue_Grosbeak_0002_36648.jpg test completed - 8\n", + "Masking Blue_Grosbeak_0033_36980.jpg test completed - 9\n", + "Masking Blue_Grosbeak_0066_36632.jpg test completed - 10\n", + "Masking Blue_Grosbeak_0048_36809.jpg test completed - 11\n", + "Masking Blue_Grosbeak_0009_36992.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/055.Evening_Grosbeak\n", + "Train indexes: [36 33 46 40 11 4 27 24 10 12 44 5 58 13 23 2 14 48 54 56 18 51 49 17\n", + " 34 32 41 38 16 22 37 47 53 30 19 52]\n", + "Validation indexes: [31 26 45 3 42 55 20 28 0 59 43 50]\n", + "Test indexes: [57 9 25 1 15 6 8 7 21 35 29 39]\n", + "Masking Evening_Grosbeak_0077_38160.jpg train completed - 1\n", + "Masking Evening_Grosbeak_0072_37301.jpg train completed - 2\n", + "Masking Evening_Grosbeak_0102_37927.jpg train completed - 3\n", + "Masking Evening_Grosbeak_0086_38048.jpg train completed - 4\n", + "Masking Evening_Grosbeak_0024_37404.jpg train completed - 5\n", + "Masking Evening_Grosbeak_0007_37312.jpg train completed - 6\n", + "Masking Evening_Grosbeak_0057_37392.jpg train completed - 7\n", + "Masking Evening_Grosbeak_0050_37336.jpg train completed - 8\n", + "Masking Evening_Grosbeak_0022_37761.jpg train completed - 9\n", + "Masking Evening_Grosbeak_0025_37230.jpg train completed - 10\n", + "Masking Evening_Grosbeak_0100_37863.jpg train completed - 11\n", + "Masking Evening_Grosbeak_0011_37913.jpg train completed - 12\n", + "Masking Evening_Grosbeak_0133_37976.jpg train completed - 13\n", + "Masking Evening_Grosbeak_0027_37824.jpg train completed - 14\n", + "Masking Evening_Grosbeak_0046_37280.jpg train completed - 15\n", + "Masking Evening_Grosbeak_0004_37960.jpg train completed - 16\n", + "Masking Evening_Grosbeak_0028_37410.jpg train completed - 17\n", + "Masking Evening_Grosbeak_0107_37933.jpg train completed - 18\n", + "Masking Evening_Grosbeak_0122_37864.jpg train completed - 19\n", + "Masking Evening_Grosbeak_0130_37813.jpg train completed - 20\n", + "Masking Evening_Grosbeak_0036_37978.jpg train completed - 21\n", + "Masking Evening_Grosbeak_0115_37490.jpg train completed - 22\n", + "Masking Evening_Grosbeak_0112_37922.jpg train completed - 23\n", + "Masking Evening_Grosbeak_0034_37349.jpg train completed - 24\n", + "Masking Evening_Grosbeak_0075_37302.jpg train completed - 25\n", + "Masking Evening_Grosbeak_0070_37767.jpg train completed - 26\n", + "Masking Evening_Grosbeak_0090_37225.jpg train completed - 27\n", + "Masking Evening_Grosbeak_0079_37979.jpg train completed - 28\n", + "Masking Evening_Grosbeak_0033_37707.jpg train completed - 29\n", + "Masking Evening_Grosbeak_0044_37938.jpg train completed - 30\n", + "Masking Evening_Grosbeak_0078_38051.jpg train completed - 31\n", + "Masking Evening_Grosbeak_0104_37661.jpg train completed - 32\n", + "Masking Evening_Grosbeak_0120_37378.jpg train completed - 33\n", + "Masking Evening_Grosbeak_0065_37296.jpg train completed - 34\n", + "Masking Evening_Grosbeak_0038_37228.jpg train completed - 35\n", + "Masking Evening_Grosbeak_0118_37299.jpg train completed - 36\n", + "Masking Evening_Grosbeak_0069_37684.jpg valid completed - 1\n", + "Masking Evening_Grosbeak_0053_38005.jpg valid completed - 2\n", + "Masking Evening_Grosbeak_0101_37697.jpg valid completed - 3\n", + "Masking Evening_Grosbeak_0005_37331.jpg valid completed - 4\n", + "Masking Evening_Grosbeak_0093_37608.jpg valid completed - 5\n", + "Masking Evening_Grosbeak_0129_37831.jpg valid completed - 6\n", + "Masking Evening_Grosbeak_0040_37429.jpg valid completed - 7\n", + "Masking Evening_Grosbeak_0061_38026.jpg valid completed - 8\n", + "Masking Evening_Grosbeak_0001_37437.jpg valid completed - 9\n", + "Masking Evening_Grosbeak_0141_37398.jpg valid completed - 10\n", + "Masking Evening_Grosbeak_0098_37532.jpg valid completed - 11\n", + "Masking Evening_Grosbeak_0114_37416.jpg valid completed - 12\n", + "Masking Evening_Grosbeak_0132_38025.jpg test completed - 1\n", + "Masking Evening_Grosbeak_0021_37789.jpg test completed - 2\n", + "Masking Evening_Grosbeak_0051_37954.jpg test completed - 3\n", + "Masking Evening_Grosbeak_0003_37698.jpg test completed - 4\n", + "Masking Evening_Grosbeak_0031_37239.jpg test completed - 5\n", + "Masking Evening_Grosbeak_0015_37238.jpg test completed - 6\n", + "Masking Evening_Grosbeak_0017_37379.jpg test completed - 7\n", + "Masking Evening_Grosbeak_0016_37613.jpg test completed - 8\n", + "Masking Evening_Grosbeak_0041_37928.jpg test completed - 9\n", + "Masking Evening_Grosbeak_0076_37818.jpg test completed - 10\n", + "Masking Evening_Grosbeak_0063_37409.jpg test completed - 11\n", + "Masking Evening_Grosbeak_0085_37487.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/056.Pine_Grosbeak\n", + "Train indexes: [16 50 56 22 38 31 32 10 51 1 13 48 7 55 12 18 9 47 34 25 3 2 59 17\n", + " 4 14 21 46 35 11 20 54 23 26 5 0]\n", + "Validation indexes: [29 58 37 45 8 30 41 39 28 19 15 24]\n", + "Test indexes: [49 53 57 40 42 44 33 27 43 6 52 36]\n", + "Masking Pine_Grosbeak_0033_38945.jpg train completed - 1\n", + "Masking Pine_Grosbeak_0102_38216.jpg train completed - 2\n", + "Masking Pine_Grosbeak_0109_38451.jpg train completed - 3\n", + "Masking Pine_Grosbeak_0042_38432.jpg train completed - 4\n", + "Masking Pine_Grosbeak_0071_38207.jpg train completed - 5\n", + "Masking Pine_Grosbeak_0054_38953.jpg train completed - 6\n", + "Masking Pine_Grosbeak_0055_38730.jpg train completed - 7\n", + "Masking Pine_Grosbeak_0019_38845.jpg train completed - 8\n", + "Masking Pine_Grosbeak_0104_38362.jpg train completed - 9\n", + "Masking Pine_Grosbeak_0003_38437.jpg train completed - 10\n", + "Masking Pine_Grosbeak_0025_38443.jpg train completed - 11\n", + "Masking Pine_Grosbeak_0097_38735.jpg train completed - 12\n", + "Masking Pine_Grosbeak_0014_38398.jpg train completed - 13\n", + "Masking Pine_Grosbeak_0108_38281.jpg train completed - 14\n", + "Masking Pine_Grosbeak_0024_38403.jpg train completed - 15\n", + "Masking Pine_Grosbeak_0035_38729.jpg train completed - 16\n", + "Masking Pine_Grosbeak_0018_38374.jpg train completed - 17\n", + "Masking Pine_Grosbeak_0096_38441.jpg train completed - 18\n", + "Masking Pine_Grosbeak_0058_38431.jpg train completed - 19\n", + "Masking Pine_Grosbeak_0046_38275.jpg train completed - 20\n", + "Masking Pine_Grosbeak_0006_38421.jpg train completed - 21\n", + "Masking Pine_Grosbeak_0004_38396.jpg train completed - 22\n", + "Masking Pine_Grosbeak_0115_38330.jpg train completed - 23\n", + "Masking Pine_Grosbeak_0034_38987.jpg train completed - 24\n", + "Masking Pine_Grosbeak_0008_38486.jpg train completed - 25\n", + "Masking Pine_Grosbeak_0029_38995.jpg train completed - 26\n", + "Masking Pine_Grosbeak_0039_38400.jpg train completed - 27\n", + "Masking Pine_Grosbeak_0094_38912.jpg train completed - 28\n", + "Masking Pine_Grosbeak_0059_38581.jpg train completed - 29\n", + "Masking Pine_Grosbeak_0021_38382.jpg train completed - 30\n", + "Masking Pine_Grosbeak_0038_38956.jpg train completed - 31\n", + "Masking Pine_Grosbeak_0107_38351.jpg train completed - 32\n", + "Masking Pine_Grosbeak_0043_38992.jpg train completed - 33\n", + "Masking Pine_Grosbeak_0048_38434.jpg train completed - 34\n", + "Masking Pine_Grosbeak_0009_38609.jpg train completed - 35\n", + "Masking Pine_Grosbeak_0002_38214.jpg train completed - 36\n", + "Masking Pine_Grosbeak_0052_38804.jpg valid completed - 1\n", + "Masking Pine_Grosbeak_0114_38259.jpg valid completed - 2\n", + "Masking Pine_Grosbeak_0068_38981.jpg valid completed - 3\n", + "Masking Pine_Grosbeak_0091_38811.jpg valid completed - 4\n", + "Masking Pine_Grosbeak_0016_38743.jpg valid completed - 5\n", + "Masking Pine_Grosbeak_0053_38805.jpg valid completed - 6\n", + "Masking Pine_Grosbeak_0078_38242.jpg valid completed - 7\n", + "Masking Pine_Grosbeak_0075_38619.jpg valid completed - 8\n", + "Masking Pine_Grosbeak_0050_38475.jpg valid completed - 9\n", + "Masking Pine_Grosbeak_0036_38968.jpg valid completed - 10\n", + "Masking Pine_Grosbeak_0032_38473.jpg valid completed - 11\n", + "Masking Pine_Grosbeak_0044_38766.jpg valid completed - 12\n", + "Masking Pine_Grosbeak_0100_38988.jpg test completed - 1\n", + "Masking Pine_Grosbeak_0106_38218.jpg test completed - 2\n", + "Masking Pine_Grosbeak_0111_38741.jpg test completed - 3\n", + "Masking Pine_Grosbeak_0077_38929.jpg test completed - 4\n", + "Masking Pine_Grosbeak_0082_38552.jpg test completed - 5\n", + "Masking Pine_Grosbeak_0088_38830.jpg test completed - 6\n", + "Masking Pine_Grosbeak_0056_38940.jpg test completed - 7\n", + "Masking Pine_Grosbeak_0049_38380.jpg test completed - 8\n", + "Masking Pine_Grosbeak_0083_38508.jpg test completed - 9\n", + "Masking Pine_Grosbeak_0012_38466.jpg test completed - 10\n", + "Masking Pine_Grosbeak_0105_38210.jpg test completed - 11\n", + "Masking Pine_Grosbeak_0067_38524.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/057.Rose_breasted_Grosbeak\n", + "Train indexes: [57 29 3 37 58 13 2 54 17 8 18 49 52 48 0 51 44 11 15 9 23 50 27 10\n", + " 16 30 25 59 46 21 38 45 53 35 20 1]\n", + "Validation indexes: [36 47 12 5 55 4 34 6 43 7 41 24]\n", + "Test indexes: [56 40 22 42 32 28 19 39 26 14 31 33]\n", + "Masking Rose_Breasted_Grosbeak_0126_39361.jpg train completed - 1\n", + "Masking Rose_Breasted_Grosbeak_0060_39279.jpg train completed - 2\n", + "Masking Rose_Breasted_Grosbeak_0008_39481.jpg train completed - 3\n", + "Masking Rose_Breasted_Grosbeak_0080_39734.jpg train completed - 4\n", + "Masking Rose_Breasted_Grosbeak_0128_39204.jpg train completed - 5\n", + "Masking Rose_Breasted_Grosbeak_0024_39398.jpg train completed - 6\n", + "Masking Rose_Breasted_Grosbeak_0004_39143.jpg train completed - 7\n", + "Masking Rose_Breasted_Grosbeak_0117_39601.jpg train completed - 8\n", + "Masking Rose_Breasted_Grosbeak_0029_39434.jpg train completed - 9\n", + "Masking Rose_Breasted_Grosbeak_0016_39265.jpg train completed - 10\n", + "Masking Rose_Breasted_Grosbeak_0032_39772.jpg train completed - 11\n", + "Masking Rose_Breasted_Grosbeak_0104_39189.jpg train completed - 12\n", + "Masking Rose_Breasted_Grosbeak_0110_39373.jpg train completed - 13\n", + "Masking Rose_Breasted_Grosbeak_0103_39580.jpg train completed - 14\n", + "Masking Rose_Breasted_Grosbeak_0001_39801.jpg train completed - 15\n", + "Masking Rose_Breasted_Grosbeak_0108_39000.jpg train completed - 16\n", + "Masking Rose_Breasted_Grosbeak_0092_39367.jpg train completed - 17\n", + "Masking Rose_Breasted_Grosbeak_0019_39274.jpg train completed - 18\n", + "Masking Rose_Breasted_Grosbeak_0026_39191.jpg train completed - 19\n", + "Masking Rose_Breasted_Grosbeak_0017_39161.jpg train completed - 20\n", + "Masking Rose_Breasted_Grosbeak_0047_39407.jpg train completed - 21\n", + "Masking Rose_Breasted_Grosbeak_0106_39714.jpg train completed - 22\n", + "Masking Rose_Breasted_Grosbeak_0055_39154.jpg train completed - 23\n", + "Masking Rose_Breasted_Grosbeak_0018_39731.jpg train completed - 24\n", + "Masking Rose_Breasted_Grosbeak_0028_39271.jpg train completed - 25\n", + "Masking Rose_Breasted_Grosbeak_0062_39234.jpg train completed - 26\n", + "Masking Rose_Breasted_Grosbeak_0051_39798.jpg train completed - 27\n", + "Masking Rose_Breasted_Grosbeak_0135_39227.jpg train completed - 28\n", + "Masking Rose_Breasted_Grosbeak_0097_39514.jpg train completed - 29\n", + "Masking Rose_Breasted_Grosbeak_0042_39158.jpg train completed - 30\n", + "Masking Rose_Breasted_Grosbeak_0081_39136.jpg train completed - 31\n", + "Masking Rose_Breasted_Grosbeak_0095_39178.jpg train completed - 32\n", + "Masking Rose_Breasted_Grosbeak_0114_39770.jpg train completed - 33\n", + "Masking Rose_Breasted_Grosbeak_0075_39795.jpg train completed - 34\n", + "Masking Rose_Breasted_Grosbeak_0040_39689.jpg train completed - 35\n", + "Masking Rose_Breasted_Grosbeak_0003_39223.jpg train completed - 36\n", + "Masking Rose_Breasted_Grosbeak_0077_39613.jpg valid completed - 1\n", + "Masking Rose_Breasted_Grosbeak_0101_39495.jpg valid completed - 2\n", + "Masking Rose_Breasted_Grosbeak_0020_39152.jpg valid completed - 3\n", + "Masking Rose_Breasted_Grosbeak_0011_39207.jpg valid completed - 4\n", + "Masking Rose_Breasted_Grosbeak_0122_39284.jpg valid completed - 5\n", + "Masking Rose_Breasted_Grosbeak_0010_39238.jpg valid completed - 6\n", + "Masking Rose_Breasted_Grosbeak_0069_39254.jpg valid completed - 7\n", + "Masking Rose_Breasted_Grosbeak_0012_39149.jpg valid completed - 8\n", + "Masking Rose_Breasted_Grosbeak_0089_39211.jpg valid completed - 9\n", + "Masking Rose_Breasted_Grosbeak_0014_39037.jpg valid completed - 10\n", + "Masking Rose_Breasted_Grosbeak_0087_39280.jpg valid completed - 11\n", + "Masking Rose_Breasted_Grosbeak_0050_39695.jpg valid completed - 12\n", + "Masking Rose_Breasted_Grosbeak_0125_39597.jpg test completed - 1\n", + "Masking Rose_Breasted_Grosbeak_0086_39658.jpg test completed - 2\n", + "Masking Rose_Breasted_Grosbeak_0045_39489.jpg test completed - 3\n", + "Masking Rose_Breasted_Grosbeak_0088_39035.jpg test completed - 4\n", + "Masking Rose_Breasted_Grosbeak_0067_39592.jpg test completed - 5\n", + "Masking Rose_Breasted_Grosbeak_0059_39045.jpg test completed - 6\n", + "Masking Rose_Breasted_Grosbeak_0033_39290.jpg test completed - 7\n", + "Masking Rose_Breasted_Grosbeak_0084_39053.jpg test completed - 8\n", + "Masking Rose_Breasted_Grosbeak_0052_39044.jpg test completed - 9\n", + "Masking Rose_Breasted_Grosbeak_0025_39252.jpg test completed - 10\n", + "Masking Rose_Breasted_Grosbeak_0063_39802.jpg test completed - 11\n", + "Masking Rose_Breasted_Grosbeak_0068_39094.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/058.Pigeon_Guillemot\n", + "Train indexes: [57 1 18 41 40 11 30 36 47 21 16 25 20 10 5 13 44 12 8 55 51 7 27 0\n", + " 22 53 46 54 37 33 17 34 15 45]\n", + "Validation indexes: [43 26 9 23 28 31 2 42 50 24 39 35]\n", + "Test indexes: [49 38 19 29 4 3 32 6 52 48 56 14]\n", + "Masking Pigeon_Guillemot_0109_39872.jpg train completed - 1\n", + "Masking Pigeon_Guillemot_0005_40375.jpg train completed - 2\n", + "Masking Pigeon_Guillemot_0030_40089.jpg train completed - 3\n", + "Masking Pigeon_Guillemot_0079_40389.jpg train completed - 4\n", + "Masking Pigeon_Guillemot_0077_39885.jpg train completed - 5\n", + "Masking Pigeon_Guillemot_0020_40088.jpg train completed - 6\n", + "Masking Pigeon_Guillemot_0058_40184.jpg train completed - 7\n", + "Masking Pigeon_Guillemot_0071_40216.jpg train completed - 8\n", + "Masking Pigeon_Guillemot_0089_40008.jpg train completed - 9\n", + "Masking Pigeon_Guillemot_0038_40035.jpg train completed - 10\n", + "Masking Pigeon_Guillemot_0028_40046.jpg train completed - 11\n", + "Masking Pigeon_Guillemot_0043_39861.jpg train completed - 12\n", + "Masking Pigeon_Guillemot_0037_40149.jpg train completed - 13\n", + "Masking Pigeon_Guillemot_0019_40515.jpg train completed - 14\n", + "Masking Pigeon_Guillemot_0012_40236.jpg train completed - 15\n", + "Masking Pigeon_Guillemot_0024_39814.jpg train completed - 16\n", + "Masking Pigeon_Guillemot_0083_39980.jpg train completed - 17\n", + "Masking Pigeon_Guillemot_0023_39829.jpg train completed - 18\n", + "Masking Pigeon_Guillemot_0015_40232.jpg train completed - 19\n", + "Masking Pigeon_Guillemot_0105_40078.jpg train completed - 20\n", + "Masking Pigeon_Guillemot_0096_39995.jpg train completed - 21\n", + "Masking Pigeon_Guillemot_0014_40040.jpg train completed - 22\n", + "Masking Pigeon_Guillemot_0053_39876.jpg train completed - 23\n", + "Masking Pigeon_Guillemot_0003_39986.jpg train completed - 24\n", + "Masking Pigeon_Guillemot_0040_40270.jpg train completed - 25\n", + "Masking Pigeon_Guillemot_0100_40179.jpg train completed - 26\n", + "Masking Pigeon_Guillemot_0087_39897.jpg train completed - 27\n", + "Masking Pigeon_Guillemot_0103_39882.jpg train completed - 28\n", + "Masking Pigeon_Guillemot_0072_39905.jpg train completed - 29\n", + "Masking Pigeon_Guillemot_0064_40044.jpg train completed - 30\n", + "Masking Pigeon_Guillemot_0029_39889.jpg train completed - 31\n", + "Masking Pigeon_Guillemot_0068_40039.jpg train completed - 32\n", + "Masking Pigeon_Guillemot_0026_40126.jpg train completed - 33\n", + "Masking Pigeon_Guillemot_0084_40217.jpg train completed - 34\n", + "Masking Pigeon_Guillemot_0082_40290.jpg valid completed - 1\n", + "Masking Pigeon_Guillemot_0052_40137.jpg valid completed - 2\n", + "Masking Pigeon_Guillemot_0018_40195.jpg valid completed - 3\n", + "Masking Pigeon_Guillemot_0041_39807.jpg valid completed - 4\n", + "Masking Pigeon_Guillemot_0055_40171.jpg valid completed - 5\n", + "Masking Pigeon_Guillemot_0059_39929.jpg valid completed - 6\n", + "Masking Pigeon_Guillemot_0007_40313.jpg valid completed - 7\n", + "Masking Pigeon_Guillemot_0081_40339.jpg valid completed - 8\n", + "Masking Pigeon_Guillemot_0095_39867.jpg valid completed - 9\n", + "Masking Pigeon_Guillemot_0042_40281.jpg valid completed - 10\n", + "Masking Pigeon_Guillemot_0075_40159.jpg valid completed - 11\n", + "Masking Pigeon_Guillemot_0069_40278.jpg valid completed - 12\n", + "Masking Pigeon_Guillemot_0092_39864.jpg test completed - 1\n", + "Masking Pigeon_Guillemot_0073_40209.jpg test completed - 2\n", + "Masking Pigeon_Guillemot_0034_40074.jpg test completed - 3\n", + "Masking Pigeon_Guillemot_0057_40130.jpg test completed - 4\n", + "Masking Pigeon_Guillemot_0011_39935.jpg test completed - 5\n", + "Masking Pigeon_Guillemot_0009_40218.jpg test completed - 6\n", + "Masking Pigeon_Guillemot_0062_39853.jpg test completed - 7\n", + "Masking Pigeon_Guillemot_0013_40253.jpg test completed - 8\n", + "Masking Pigeon_Guillemot_0098_39902.jpg test completed - 9\n", + "Masking Pigeon_Guillemot_0090_40090.jpg test completed - 10\n", + "Masking Pigeon_Guillemot_0108_40235.jpg test completed - 11\n", + "Masking Pigeon_Guillemot_0025_40511.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/059.California_Gull\n", + "Train indexes: [47 7 18 27 11 50 32 15 21 41 1 53 4 43 37 38 55 5 40 23 17 35 52 44\n", + " 6 29 46 56 26 14 59 20 24 13 36 57]\n", + "Validation indexes: [54 42 28 39 16 12 3 48 8 49 33 9]\n", + "Test indexes: [58 2 19 22 34 30 45 51 31 25 10 0]\n", + "Masking California_Gull_0102_40986.jpg train completed - 1\n", + "Masking California_Gull_0019_41377.jpg train completed - 2\n", + "Masking California_Gull_0037_41066.jpg train completed - 3\n", + "Masking California_Gull_0055_41218.jpg train completed - 4\n", + "Masking California_Gull_0027_41083.jpg train completed - 5\n", + "Masking California_Gull_0109_41465.jpg train completed - 6\n", + "Masking California_Gull_0067_40973.jpg train completed - 7\n", + "Masking California_Gull_0031_41469.jpg train completed - 8\n", + "Masking California_Gull_0043_41326.jpg train completed - 9\n", + "Masking California_Gull_0091_41276.jpg train completed - 10\n", + "Masking California_Gull_0006_41079.jpg train completed - 11\n", + "Masking California_Gull_0114_41267.jpg train completed - 12\n", + 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6\n", + "Masking California_Gull_0009_40662.jpg valid completed - 7\n", + "Masking California_Gull_0103_41044.jpg valid completed - 8\n", + "Masking California_Gull_0021_41471.jpg valid completed - 9\n", + "Masking California_Gull_0105_41116.jpg valid completed - 10\n", + "Masking California_Gull_0074_41358.jpg valid completed - 11\n", + "Masking California_Gull_0025_41479.jpg valid completed - 12\n", + "Masking California_Gull_0132_40836.jpg test completed - 1\n", + "Masking California_Gull_0008_40942.jpg test completed - 2\n", + "Masking California_Gull_0038_40865.jpg test completed - 3\n", + "Masking California_Gull_0046_41209.jpg test completed - 4\n", + "Masking California_Gull_0076_40788.jpg test completed - 5\n", + "Masking California_Gull_0064_41562.jpg test completed - 6\n", + "Masking California_Gull_0100_41088.jpg test completed - 7\n", + "Masking California_Gull_0110_41063.jpg test completed - 8\n", + "Masking California_Gull_0066_41188.jpg test completed - 9\n", + "Masking 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train completed - 35\n", + "Masking Glaucous_Winged_Gull_0093_44724.jpg valid completed - 1\n", + "Masking Glaucous_Winged_Gull_0012_44264.jpg valid completed - 2\n", + "Masking Glaucous_Winged_Gull_0015_44198.jpg valid completed - 3\n", + "Masking Glaucous_Winged_Gull_0028_44628.jpg valid completed - 4\n", + "Masking Glaucous_Winged_Gull_0130_45210.jpg valid completed - 5\n", + "Masking Glaucous_Winged_Gull_0004_44361.jpg valid completed - 6\n", + "Masking Glaucous_Winged_Gull_0081_45223.jpg valid completed - 7\n", + "Masking Glaucous_Winged_Gull_0061_44852.jpg valid completed - 8\n", + "Masking Glaucous_Winged_Gull_0138_44694.jpg valid completed - 9\n", + "Masking Glaucous_Winged_Gull_0101_44718.jpg valid completed - 10\n", + "Masking Glaucous_Winged_Gull_0056_44658.jpg valid completed - 11\n", + "Masking Glaucous_Winged_Gull_0014_44832.jpg valid completed - 12\n", + "Masking Glaucous_Winged_Gull_0102_44579.jpg test completed - 1\n", + "Masking Glaucous_Winged_Gull_0045_44906.jpg test completed - 2\n", + "Masking Glaucous_Winged_Gull_0057_44807.jpg test completed - 3\n", + "Masking Glaucous_Winged_Gull_0076_44744.jpg test completed - 4\n", + "Masking Glaucous_Winged_Gull_0134_44743.jpg test completed - 5\n", + "Masking Glaucous_Winged_Gull_0075_44455.jpg test completed - 6\n", + "Masking Glaucous_Winged_Gull_0023_45090.jpg test completed - 7\n", + "Masking Glaucous_Winged_Gull_0011_44827.jpg test completed - 8\n", + "Masking Glaucous_Winged_Gull_0034_44625.jpg test completed - 9\n", + "Masking Glaucous_Winged_Gull_0087_44550.jpg test completed - 10\n", + "Masking Glaucous_Winged_Gull_0022_44733.jpg test completed - 11\n", + "Masking Glaucous_Winged_Gull_0002_44612.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/061.Heermann_Gull\n", + "Train indexes: [ 2 23 16 12 18 48 4 49 27 45 54 33 44 58 32 20 59 15 1 7 22 8 38 52\n", + " 17 19 6 42 53 40 47 9 21 24 0 28]\n", + "Validation indexes: [30 25 39 3 14 5 55 31 43 37 35 56]\n", + "Test indexes: 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Heermann_Gull_0118_45626.jpg train completed - 31\n", + "Masking Heermann_Gull_0024_45486.jpg train completed - 32\n", + "Masking Heermann_Gull_0055_45901.jpg train completed - 33\n", + "Masking Heermann_Gull_0071_45735.jpg train completed - 34\n", + "Masking Heermann_Gull_0001_45472.jpg train completed - 35\n", + "Masking Heermann_Gull_0076_45597.jpg train completed - 36\n", + "Masking Heermann_Gull_0079_45468.jpg valid completed - 1\n", + "Masking Heermann_Gull_0073_45714.jpg valid completed - 2\n", + "Masking Heermann_Gull_0098_45753.jpg valid completed - 3\n", + "Masking Heermann_Gull_0009_45905.jpg valid completed - 4\n", + "Masking Heermann_Gull_0034_45693.jpg valid completed - 5\n", + "Masking Heermann_Gull_0015_41392.jpg valid completed - 6\n", + "Masking Heermann_Gull_0136_45416.jpg valid completed - 7\n", + "Masking Heermann_Gull_0083_45879.jpg valid completed - 8\n", + "Masking Heermann_Gull_0110_45968.jpg valid completed - 9\n", + "Masking Heermann_Gull_0093_45576.jpg valid completed - 10\n", + "Masking Heermann_Gull_0090_45834.jpg valid completed - 11\n", + "Masking Heermann_Gull_0138_45269.jpg valid completed - 12\n", + "Masking Heermann_Gull_0128_45663.jpg test completed - 1\n", + "Masking Heermann_Gull_0102_45904.jpg test completed - 2\n", + "Masking Heermann_Gull_0077_45711.jpg test completed - 3\n", + "Masking Heermann_Gull_0091_45363.jpg test completed - 4\n", + "Masking Heermann_Gull_0031_45822.jpg test completed - 5\n", + "Masking Heermann_Gull_0115_45797.jpg test completed - 6\n", + "Masking Heermann_Gull_0139_45749.jpg test completed - 7\n", + "Masking Heermann_Gull_0033_45792.jpg test completed - 8\n", + "Masking Heermann_Gull_0130_45700.jpg test completed - 9\n", + "Masking Heermann_Gull_0089_45679.jpg test completed - 10\n", + "Masking Heermann_Gull_0074_45351.jpg test completed - 11\n", + "Masking Heermann_Gull_0027_45864.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/062.Herring_Gull\n", + "Train indexes: [20 38 5 59 30 57 56 3 41 34 25 15 18 8 7 13 24 4 45 9 43 26 39 17\n", + " 50 12 32 11 40 2 46 27 21 48 53 6]\n", + "Validation indexes: [35 36 49 52 14 1 51 54 28 58 42 33]\n", + "Test indexes: [10 22 55 29 37 31 0 19 44 23 16 47]\n", + "Masking Herring_Gull_0054_46164.jpg train completed - 1\n", + "Masking Herring_Gull_0093_46029.jpg train completed - 2\n", + "Masking Herring_Gull_0015_46353.jpg train completed - 3\n", + "Masking Herring_Gull_0145_46220.jpg train completed - 4\n", + "Masking Herring_Gull_0075_48935.jpg train completed - 5\n", + "Masking Herring_Gull_0141_47184.jpg train completed - 6\n", + "Masking Herring_Gull_0140_46455.jpg train completed - 7\n", + "Masking Herring_Gull_0012_46654.jpg train completed - 8\n", + "Masking Herring_Gull_0098_46971.jpg train completed - 9\n", + "Masking Herring_Gull_0082_47540.jpg train completed - 10\n", + "Masking Herring_Gull_0064_47832.jpg train completed - 11\n", + "Masking Herring_Gull_0047_46218.jpg train completed - 12\n", + "Masking 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"Masking Herring_Gull_0114_46956.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/063.Ivory_Gull\n", + "Train indexes: [ 9 48 52 53 43 20 41 22 30 24 12 23 8 14 13 25 35 34 6 2 7 49 18 27\n", + " 54 3 56 55 46 58 19 39 45 1 36 42]\n", + "Validation indexes: [37 4 33 57 28 32 11 5 59 21 26 44]\n", + "Test indexes: [ 0 10 15 31 47 38 29 17 16 51 40 50]\n", + "Masking Ivory_Gull_0023_49385.jpg train completed - 1\n", + "Masking Ivory_Gull_0098_49810.jpg train completed - 2\n", + "Masking Ivory_Gull_0105_49559.jpg train completed - 3\n", + "Masking Ivory_Gull_0107_49186.jpg train completed - 4\n", + "Masking Ivory_Gull_0089_49699.jpg train completed - 5\n", + "Masking Ivory_Gull_0041_49172.jpg train completed - 6\n", + "Masking Ivory_Gull_0087_49202.jpg train completed - 7\n", + "Masking Ivory_Gull_0045_49696.jpg train completed - 8\n", + "Masking Ivory_Gull_0065_49566.jpg train completed - 9\n", + "Masking Ivory_Gull_0052_50013.jpg train completed - 10\n", + "Masking 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Ring_Billed_Gull_0132_51552.jpg valid completed - 3\n", + "Masking Ring_Billed_Gull_0089_51348.jpg valid completed - 4\n", + "Masking Ring_Billed_Gull_0039_50191.jpg valid completed - 5\n", + "Masking Ring_Billed_Gull_0107_51306.jpg valid completed - 6\n", + "Masking Ring_Billed_Gull_0044_50239.jpg valid completed - 7\n", + "Masking Ring_Billed_Gull_0092_51521.jpg valid completed - 8\n", + "Masking Ring_Billed_Gull_0106_52729.jpg valid completed - 9\n", + "Masking Ring_Billed_Gull_0033_51288.jpg valid completed - 10\n", + "Masking Ring_Billed_Gull_0021_51300.jpg valid completed - 11\n", + "Masking Ring_Billed_Gull_0029_52613.jpg valid completed - 12\n", + "Masking Ring_Billed_Gull_0027_51266.jpg test completed - 1\n", + "Masking Ring_Billed_Gull_0078_51494.jpg test completed - 2\n", + "Masking Ring_Billed_Gull_0003_51480.jpg test completed - 3\n", + "Masking Ring_Billed_Gull_0063_51256.jpg test completed - 4\n", + "Masking Ring_Billed_Gull_0118_51322.jpg test completed - 5\n", + "Masking Ring_Billed_Gull_0001_51416.jpg test completed - 6\n", + "Masking Ring_Billed_Gull_0013_50180.jpg test completed - 7\n", + "Masking Ring_Billed_Gull_0055_51156.jpg test completed - 8\n", + "Masking Ring_Billed_Gull_0059_51554.jpg test completed - 9\n", + "Masking Ring_Billed_Gull_0052_51357.jpg test completed - 10\n", + "Masking Ring_Billed_Gull_0128_51403.jpg test completed - 11\n", + "Masking Ring_Billed_Gull_0131_51370.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/065.Slaty_backed_Gull\n", + "Train indexes: [27 5 10 8 40 34 15 7 41 36 47 13 45 49 39 21 3 9 38 28 31 48 26 4\n", + " 42 44 46 12 14 25]\n", + "Validation indexes: [ 6 37 19 22 1 2 24 33 0 32]\n", + "Test indexes: [20 18 29 43 17 11 30 23 16 35]\n", + "Masking Slaty_Backed_Gull_0050_796045.jpg train completed - 1\n", + "Masking Slaty_Backed_Gull_0015_796004.jpg train completed - 2\n", + "Masking Slaty_Backed_Gull_0023_796030.jpg train completed - 3\n", + "Masking 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completed - 6\n", + "Masking Slaty_Backed_Gull_0056_796013.jpg test completed - 7\n", + "Masking Slaty_Backed_Gull_0043_796009.jpg test completed - 8\n", + "Masking Slaty_Backed_Gull_0030_796003.jpg test completed - 9\n", + "Masking Slaty_Backed_Gull_0066_796016.jpg test completed - 10\n", + "/kaggle/working/CUB_200_2011/images/066.Western_Gull\n", + "Train indexes: [57 46 24 26 6 45 41 9 17 55 40 59 54 29 56 49 50 4 0 13 22 18 47 52\n", + " 7 25 32 42 21 5 33 35 3 12 31 37]\n", + "Validation indexes: [53 38 44 27 2 36 19 39 58 48 16 34]\n", + "Test indexes: [43 1 15 30 8 10 23 20 14 28 11 51]\n", + "Masking Western_Gull_0131_53349.jpg train completed - 1\n", + "Masking Western_Gull_0099_53670.jpg train completed - 2\n", + "Masking Western_Gull_0050_54425.jpg train completed - 3\n", + "Masking Western_Gull_0052_53485.jpg train completed - 4\n", + "Masking Western_Gull_0012_53605.jpg train completed - 5\n", + "Masking Western_Gull_0098_53925.jpg train completed - 6\n", + "Masking 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Western_Gull_0087_54193.jpg valid completed - 2\n", + "Masking Western_Gull_0097_54508.jpg valid completed - 3\n", + "Masking Western_Gull_0057_55312.jpg valid completed - 4\n", + "Masking Western_Gull_0006_53504.jpg valid completed - 5\n", + "Masking Western_Gull_0078_53595.jpg valid completed - 6\n", + "Masking Western_Gull_0042_53843.jpg valid completed - 7\n", + "Masking Western_Gull_0089_55306.jpg valid completed - 8\n", + "Masking Western_Gull_0133_55639.jpg valid completed - 9\n", + "Masking Western_Gull_0104_53816.jpg valid completed - 10\n", + "Masking Western_Gull_0035_55703.jpg valid completed - 11\n", + "Masking Western_Gull_0071_54207.jpg valid completed - 12\n", + "Masking Western_Gull_0094_55156.jpg test completed - 1\n", + "Masking Western_Gull_0004_55605.jpg test completed - 2\n", + "Masking Western_Gull_0030_54083.jpg test completed - 3\n", + "Masking Western_Gull_0065_55728.jpg test completed - 4\n", + "Masking Western_Gull_0021_54649.jpg test completed - 5\n", + "Masking Western_Gull_0024_53631.jpg test completed - 6\n", + "Masking Western_Gull_0049_53748.jpg test completed - 7\n", + "Masking Western_Gull_0043_54706.jpg test completed - 8\n", + "Masking Western_Gull_0029_54143.jpg test completed - 9\n", + "Masking Western_Gull_0058_53882.jpg test completed - 10\n", + "Masking Western_Gull_0025_55421.jpg test completed - 11\n", + "Masking Western_Gull_0115_54950.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/067.Anna_Hummingbird\n", + "Train indexes: [45 24 18 53 15 38 49 2 19 48 12 29 44 1 40 27 50 55 3 23 35 8 11 41\n", + " 14 57 25 0 42 47 10 36 34 58 4 31]\n", + "Validation indexes: [39 33 56 7 26 21 32 30 17 59 46 20]\n", + "Test indexes: [ 5 22 9 13 37 51 43 52 28 54 16 6]\n", + "Masking Anna_Hummingbird_0102_56087.jpg train completed - 1\n", + "Masking Anna_Hummingbird_0052_56344.jpg train completed - 2\n", + "Masking Anna_Hummingbird_0040_56293.jpg train completed - 3\n", + "Masking Anna_Hummingbird_0119_56309.jpg 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completed - 12\n", + "Masking Anna_Hummingbird_0021_55763.jpg test completed - 1\n", + "Masking Anna_Hummingbird_0047_56049.jpg test completed - 2\n", + "Masking Anna_Hummingbird_0027_55873.jpg test completed - 3\n", + "Masking Anna_Hummingbird_0034_56614.jpg test completed - 4\n", + "Masking Anna_Hummingbird_0087_56435.jpg test completed - 5\n", + "Masking Anna_Hummingbird_0117_55785.jpg test completed - 6\n", + "Masking Anna_Hummingbird_0099_55916.jpg test completed - 7\n", + "Masking Anna_Hummingbird_0118_55773.jpg test completed - 8\n", + "Masking Anna_Hummingbird_0059_56674.jpg test completed - 9\n", + "Masking Anna_Hummingbird_0121_56436.jpg test completed - 10\n", + "Masking Anna_Hummingbird_0037_56587.jpg test completed - 11\n", + "Masking Anna_Hummingbird_0022_55779.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/068.Ruby_throated_Hummingbird\n", + "Train indexes: [ 9 16 50 38 13 15 31 41 7 0 20 8 25 11 27 40 24 36 17 26 1 45 59 2\n", + " 4 29 14 5 28 23 18 19 57 49 22 44]\n", + "Validation indexes: [46 6 35 56 51 43 34 47 48 10 54 33]\n", + "Test indexes: [42 39 58 55 21 37 3 32 53 12 52 30]\n", + "Masking Ruby_Throated_Hummingbird_0024_57969.jpg train completed - 1\n", + "Masking Ruby_Throated_Hummingbird_0042_57431.jpg train completed - 2\n", + "Masking Ruby_Throated_Hummingbird_0116_58031.jpg train completed - 3\n", + "Masking Ruby_Throated_Hummingbird_0088_57366.jpg train completed - 4\n", + "Masking Ruby_Throated_Hummingbird_0032_58168.jpg train completed - 5\n", + "Masking Ruby_Throated_Hummingbird_0040_57982.jpg train completed - 6\n", + "Masking Ruby_Throated_Hummingbird_0075_57537.jpg train completed - 7\n", + "Masking Ruby_Throated_Hummingbird_0096_57505.jpg train completed - 8\n", + "Masking Ruby_Throated_Hummingbird_0013_57212.jpg train completed - 9\n", + "Masking Ruby_Throated_Hummingbird_0001_58162.jpg train completed - 10\n", + "Masking Ruby_Throated_Hummingbird_0049_57891.jpg train completed - 11\n", + "Masking 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Ruby_Throated_Hummingbird_0010_58285.jpg valid completed - 2\n", + "Masking Ruby_Throated_Hummingbird_0079_58075.jpg valid completed - 3\n", + "Masking Ruby_Throated_Hummingbird_0126_57371.jpg valid completed - 4\n", + "Masking Ruby_Throated_Hummingbird_0117_58092.jpg valid completed - 5\n", + "Masking Ruby_Throated_Hummingbird_0103_57573.jpg valid completed - 6\n", + "Masking Ruby_Throated_Hummingbird_0078_57208.jpg valid completed - 7\n", + "Masking Ruby_Throated_Hummingbird_0110_57851.jpg valid completed - 8\n", + "Masking Ruby_Throated_Hummingbird_0111_58141.jpg valid completed - 9\n", + "Masking Ruby_Throated_Hummingbird_0025_57835.jpg valid completed - 10\n", + "Masking Ruby_Throated_Hummingbird_0120_58316.jpg valid completed - 11\n", + "Masking Ruby_Throated_Hummingbird_0077_57858.jpg valid completed - 12\n", + "Masking Ruby_Throated_Hummingbird_0098_57514.jpg test completed - 1\n", + "Masking Ruby_Throated_Hummingbird_0090_57411.jpg test completed - 2\n", + "Masking Ruby_Throated_Hummingbird_0129_57666.jpg test completed - 3\n", + "Masking Ruby_Throated_Hummingbird_0123_57745.jpg test completed - 4\n", + "Masking Ruby_Throated_Hummingbird_0050_57510.jpg test completed - 5\n", + "Masking Ruby_Throated_Hummingbird_0084_58311.jpg test completed - 6\n", + "Masking Ruby_Throated_Hummingbird_0007_57388.jpg test completed - 7\n", + "Masking Ruby_Throated_Hummingbird_0076_57649.jpg test completed - 8\n", + "Masking Ruby_Throated_Hummingbird_0119_57575.jpg test completed - 9\n", + "Masking Ruby_Throated_Hummingbird_0030_57422.jpg test completed - 10\n", + "Masking Ruby_Throated_Hummingbird_0118_57536.jpg test completed - 11\n", + "Masking Ruby_Throated_Hummingbird_0072_57391.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/069.Rufous_Hummingbird\n", + "Train indexes: [25 21 3 42 13 58 20 27 16 32 10 30 51 5 19 53 17 23 7 14 18 59 6 22\n", + " 11 34 49 37 52 54 40 47 55 15 38 35]\n", + "Validation indexes: [41 2 12 9 44 31 36 45 29 56 0 48]\n", + "Test indexes: [28 4 46 43 33 8 39 1 26 57 24 50]\n", + "Masking Rufous_Hummingbird_0057_59489.jpg train completed - 1\n", + "Masking Rufous_Hummingbird_0047_59448.jpg train completed - 2\n", + "Masking Rufous_Hummingbird_0009_59405.jpg train completed - 3\n", + "Masking Rufous_Hummingbird_0102_59414.jpg train completed - 4\n", + "Masking Rufous_Hummingbird_0027_59456.jpg train completed - 5\n", + "Masking Rufous_Hummingbird_0130_59500.jpg train completed - 6\n", + "Masking Rufous_Hummingbird_0046_59647.jpg train completed - 7\n", + "Masking Rufous_Hummingbird_0064_58509.jpg train completed - 8\n", + "Masking Rufous_Hummingbird_0036_59562.jpg train completed - 9\n", + "Masking Rufous_Hummingbird_0075_59619.jpg train completed - 10\n", + "Masking Rufous_Hummingbird_0022_58725.jpg train completed - 11\n", + "Masking Rufous_Hummingbird_0071_59505.jpg train completed - 12\n", + "Masking Rufous_Hummingbird_0120_59900.jpg train completed - 13\n", + "Masking 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Rufous_Hummingbird_0090_58598.jpg train completed - 28\n", + "Masking Rufous_Hummingbird_0121_59376.jpg train completed - 29\n", + "Masking Rufous_Hummingbird_0124_58465.jpg train completed - 30\n", + "Masking Rufous_Hummingbird_0100_59541.jpg train completed - 31\n", + "Masking Rufous_Hummingbird_0115_59202.jpg train completed - 32\n", + "Masking Rufous_Hummingbird_0125_58932.jpg train completed - 33\n", + "Masking Rufous_Hummingbird_0035_59434.jpg train completed - 34\n", + "Masking Rufous_Hummingbird_0091_60551.jpg train completed - 35\n", + "Masking Rufous_Hummingbird_0081_59592.jpg train completed - 36\n", + "Masking Rufous_Hummingbird_0101_59420.jpg valid completed - 1\n", + "Masking Rufous_Hummingbird_0007_58433.jpg valid completed - 2\n", + "Masking Rufous_Hummingbird_0025_59461.jpg valid completed - 3\n", + "Masking Rufous_Hummingbird_0021_58408.jpg valid completed - 4\n", + "Masking Rufous_Hummingbird_0109_60021.jpg valid completed - 5\n", + "Masking Rufous_Hummingbird_0074_59231.jpg valid completed - 6\n", + "Masking Rufous_Hummingbird_0089_59524.jpg valid completed - 7\n", + "Masking Rufous_Hummingbird_0111_59408.jpg valid completed - 8\n", + "Masking Rufous_Hummingbird_0067_59510.jpg valid completed - 9\n", + "Masking Rufous_Hummingbird_0127_59587.jpg valid completed - 10\n", + "Masking Rufous_Hummingbird_0002_58387.jpg valid completed - 11\n", + "Masking Rufous_Hummingbird_0116_58568.jpg valid completed - 12\n", + "Masking Rufous_Hummingbird_0065_58497.jpg test completed - 1\n", + "Masking Rufous_Hummingbird_0011_59480.jpg test completed - 2\n", + "Masking Rufous_Hummingbird_0113_59444.jpg test completed - 3\n", + "Masking Rufous_Hummingbird_0106_60465.jpg test completed - 4\n", + "Masking Rufous_Hummingbird_0076_59563.jpg test completed - 5\n", + "Masking Rufous_Hummingbird_0018_59447.jpg test completed - 6\n", + "Masking Rufous_Hummingbird_0095_60360.jpg test completed - 7\n", + "Masking Rufous_Hummingbird_0006_59621.jpg test completed - 8\n", + "Masking Rufous_Hummingbird_0060_58986.jpg test completed - 9\n", + "Masking Rufous_Hummingbird_0128_60398.jpg test completed - 10\n", + "Masking Rufous_Hummingbird_0055_59935.jpg test completed - 11\n", + "Masking Rufous_Hummingbird_0119_59681.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/070.Green_Violetear\n", + "Train indexes: [24 37 3 47 32 39 57 4 26 30 40 48 46 8 29 54 14 18 5 59 51 42 35 22\n", + " 31 15 27 17 23 20 7 41 49 16 10 53]\n", + "Validation indexes: [33 9 21 52 50 55 56 38 12 43 1 11]\n", + "Test indexes: [25 0 36 28 2 34 13 19 45 6 44 58]\n", + "Masking Green_Violetear_0048_60789.jpg train completed - 1\n", + "Masking Green_Violetear_0074_795720.jpg train completed - 2\n", + "Masking Green_Violetear_0008_795703.jpg train completed - 3\n", + "Masking Green_Violetear_0092_795704.jpg train completed - 4\n", + "Masking Green_Violetear_0066_795682.jpg train completed - 5\n", + "Masking Green_Violetear_0078_60844.jpg train 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35\n", + "Masking Green_Violetear_0106_795643.jpg train completed - 36\n", + "Masking Green_Violetear_0067_795674.jpg valid completed - 1\n", + "Masking Green_Violetear_0024_795705.jpg valid completed - 2\n", + "Masking Green_Violetear_0044_795717.jpg valid completed - 3\n", + "Masking Green_Violetear_0105_795694.jpg valid completed - 4\n", + "Masking Green_Violetear_0102_795702.jpg valid completed - 5\n", + "Masking Green_Violetear_0108_795711.jpg valid completed - 6\n", + "Masking Green_Violetear_0110_60866.jpg valid completed - 7\n", + "Masking Green_Violetear_0077_795696.jpg valid completed - 8\n", + "Masking Green_Violetear_0028_60800.jpg valid completed - 9\n", + "Masking Green_Violetear_0082_795706.jpg valid completed - 10\n", + "Masking Green_Violetear_0003_795662.jpg valid completed - 11\n", + "Masking Green_Violetear_0027_60841.jpg valid completed - 12\n", + "Masking Green_Violetear_0050_795633.jpg test completed - 1\n", + "Masking Green_Violetear_0002_795699.jpg test completed - 2\n", + "Masking Green_Violetear_0073_795680.jpg test completed - 3\n", + "Masking Green_Violetear_0059_60873.jpg test completed - 4\n", + "Masking Green_Violetear_0005_795666.jpg test completed - 5\n", + "Masking Green_Violetear_0069_795723.jpg test completed - 6\n", + "Masking Green_Violetear_0029_60808.jpg test completed - 7\n", + "Masking Green_Violetear_0041_795648.jpg test completed - 8\n", + "Masking Green_Violetear_0086_795639.jpg test completed - 9\n", + "Masking Green_Violetear_0017_795652.jpg test completed - 10\n", + "Masking Green_Violetear_0084_795641.jpg test completed - 11\n", + "Masking Green_Violetear_0114_60809.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/071.Long_tailed_Jaeger\n", + "Train indexes: [50 40 7 2 39 41 29 14 5 25 43 0 17 53 22 59 30 47 58 42 48 4 49 27\n", + " 57 37 36 15 34 44 12 6 31 20 24 10]\n", + "Validation indexes: [19 11 52 45 1 46 8 35 26 21 28 38]\n", + "Test indexes: [ 9 51 3 32 13 54 23 33 18 16 56 55]\n", + 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Long_Tailed_Jaeger_0027_60950.jpg train completed - 15\n", + "Masking Long_Tailed_Jaeger_0070_797090.jpg train completed - 16\n", + "Masking Long_Tailed_Jaeger_0036_797076.jpg train completed - 17\n", + "Masking Long_Tailed_Jaeger_0057_797094.jpg train completed - 18\n", + "Masking Long_Tailed_Jaeger_0069_61060.jpg train completed - 19\n", + "Masking Long_Tailed_Jaeger_0051_797095.jpg train completed - 20\n", + "Masking Long_Tailed_Jaeger_0058_60900.jpg train completed - 21\n", + "Masking Long_Tailed_Jaeger_0008_797066.jpg train completed - 22\n", + "Masking Long_Tailed_Jaeger_0059_797079.jpg train completed - 23\n", + "Masking Long_Tailed_Jaeger_0033_60955.jpg train completed - 24\n", + "Masking Long_Tailed_Jaeger_0068_797093.jpg train completed - 25\n", + "Masking Long_Tailed_Jaeger_0046_797103.jpg train completed - 26\n", + "Masking Long_Tailed_Jaeger_0045_61007.jpg train completed - 27\n", + "Masking Long_Tailed_Jaeger_0020_61084.jpg train completed - 28\n", + "Masking Long_Tailed_Jaeger_0041_60891.jpg train completed - 29\n", + "Masking Long_Tailed_Jaeger_0054_797088.jpg train completed - 30\n", + "Masking Long_Tailed_Jaeger_0017_797081.jpg train completed - 31\n", + "Masking Long_Tailed_Jaeger_0010_60895.jpg train completed - 32\n", + "Masking Long_Tailed_Jaeger_0037_797099.jpg train completed - 33\n", + "Masking Long_Tailed_Jaeger_0025_60937.jpg train completed - 34\n", + "Masking Long_Tailed_Jaeger_0029_61101.jpg train completed - 35\n", + "Masking Long_Tailed_Jaeger_0014_797086.jpg train completed - 36\n", + "Masking Long_Tailed_Jaeger_0024_60915.jpg valid completed - 1\n", + "Masking Long_Tailed_Jaeger_0016_797084.jpg valid completed - 2\n", + "Masking Long_Tailed_Jaeger_0062_797104.jpg valid completed - 3\n", + "Masking Long_Tailed_Jaeger_0055_61046.jpg valid completed - 4\n", + "Masking Long_Tailed_Jaeger_0003_61082.jpg valid completed - 5\n", + "Masking Long_Tailed_Jaeger_0056_797092.jpg valid completed - 6\n", + "Masking Long_Tailed_Jaeger_0012_797101.jpg valid completed - 7\n", + "Masking Long_Tailed_Jaeger_0044_60968.jpg valid completed - 8\n", + "Masking Long_Tailed_Jaeger_0032_61177.jpg valid completed - 9\n", + "Masking Long_Tailed_Jaeger_0026_797098.jpg valid completed - 10\n", + "Masking Long_Tailed_Jaeger_0034_797102.jpg valid completed - 11\n", + "Masking Long_Tailed_Jaeger_0047_797075.jpg valid completed - 12\n", + "Masking Long_Tailed_Jaeger_0013_60887.jpg test completed - 1\n", + "Masking Long_Tailed_Jaeger_0061_61049.jpg test completed - 2\n", + "Masking Long_Tailed_Jaeger_0006_797065.jpg test completed - 3\n", + "Masking Long_Tailed_Jaeger_0038_797077.jpg test completed - 4\n", + "Masking Long_Tailed_Jaeger_0018_61081.jpg test completed - 5\n", + "Masking Long_Tailed_Jaeger_0064_60999.jpg test completed - 6\n", + "Masking Long_Tailed_Jaeger_0028_797082.jpg test completed - 7\n", + "Masking Long_Tailed_Jaeger_0039_797089.jpg test completed - 8\n", + "Masking 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"Masking Green_Jay_0111_65869.jpg valid completed - 3\n", + "Masking Green_Jay_0006_65788.jpg valid completed - 4\n", + "Masking Green_Jay_0059_65586.jpg valid completed - 5\n", + "Masking Green_Jay_0115_65745.jpg valid completed - 6\n", + "Masking Green_Jay_0074_65889.jpg valid completed - 7\n", + "Masking Green_Jay_0094_65775.jpg valid completed - 8\n", + "Masking Green_Jay_0043_65805.jpg valid completed - 9\n", + "Masking Green_Jay_0130_65885.jpg valid completed - 10\n", + "Masking Green_Jay_0106_65811.jpg valid completed - 11\n", + "Masking Green_Jay_0126_65716.jpg valid completed - 12\n", + "Masking Green_Jay_0086_65847.jpg test completed - 1\n", + "Masking Green_Jay_0100_65786.jpg test completed - 2\n", + "Masking Green_Jay_0029_65637.jpg test completed - 3\n", + "Masking Green_Jay_0027_65783.jpg test completed - 4\n", + "Masking Green_Jay_0118_66251.jpg test completed - 5\n", + "Masking Green_Jay_0077_65736.jpg test completed - 6\n", + "Masking Green_Jay_0020_66168.jpg test 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completed - 11\n", + "Masking Ringed_Kingfisher_0042_72913.jpg valid completed - 12\n", + "Masking Ringed_Kingfisher_0027_72948.jpg test completed - 1\n", + "Masking Ringed_Kingfisher_0039_71052.jpg test completed - 2\n", + "Masking Ringed_Kingfisher_0098_73110.jpg test completed - 3\n", + "Masking Ringed_Kingfisher_0108_73169.jpg test completed - 4\n", + "Masking Ringed_Kingfisher_0035_73012.jpg test completed - 5\n", + "Masking Ringed_Kingfisher_0041_72853.jpg test completed - 6\n", + "Masking Ringed_Kingfisher_0089_72947.jpg test completed - 7\n", + "Masking Ringed_Kingfisher_0025_72795.jpg test completed - 8\n", + "Masking Ringed_Kingfisher_0038_73041.jpg test completed - 9\n", + "Masking Ringed_Kingfisher_0029_73165.jpg test completed - 10\n", + "Masking Ringed_Kingfisher_0091_72839.jpg test completed - 11\n", + "Masking Ringed_Kingfisher_0009_72786.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/083.White_breasted_Kingfisher\n", + "Train indexes: [52 55 9 15 47 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32]\n", + "Validation indexes: [47 0 19 1 50 2 15 21 51 27 28]\n", + "Test indexes: [34 41 17 25 8 20 3 42 26 22 14]\n", + "Masking Red_Legged_Kittiwake_0058_795417.jpg train completed - 1\n", + "Masking Red_Legged_Kittiwake_0016_795460.jpg train completed - 2\n", + "Masking Red_Legged_Kittiwake_0009_795459.jpg train completed - 3\n", + "Masking Red_Legged_Kittiwake_0044_795388.jpg train completed - 4\n", + "Masking Red_Legged_Kittiwake_0054_795396.jpg train completed - 5\n", + "Masking Red_Legged_Kittiwake_0010_73789.jpg train completed - 6\n", + "Masking Red_Legged_Kittiwake_0015_73792.jpg train completed - 7\n", + "Masking Red_Legged_Kittiwake_0062_795434.jpg train completed - 8\n", + "Masking Red_Legged_Kittiwake_0042_795457.jpg train completed - 9\n", + "Masking Red_Legged_Kittiwake_0007_795402.jpg train completed - 10\n", + "Masking Red_Legged_Kittiwake_0032_795399.jpg train completed - 11\n", + "Masking Red_Legged_Kittiwake_0064_795422.jpg train completed - 12\n", + "Masking 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"/kaggle/working/CUB_200_2011/images/085.Horned_Lark\n", + "Train indexes: [27 0 34 46 29 12 16 21 2 4 58 48 37 1 45 31 7 24 43 32 10 23 15 8\n", + " 44 40 56 26 35 55 33 57 50 41 38 14]\n", + "Validation indexes: [53 28 25 20 9 11 54 5 52 39 6 36]\n", + "Test indexes: [17 3 51 13 59 47 49 42 22 19 30 18]\n", + "Masking Horned_Lark_0066_74796.jpg train completed - 1\n", + "Masking Horned_Lark_0001_73835.jpg train completed - 2\n", + "Masking Horned_Lark_0082_73833.jpg train completed - 3\n", + "Masking Horned_Lark_0110_73826.jpg train completed - 4\n", + "Masking Horned_Lark_0069_74093.jpg train completed - 5\n", + "Masking Horned_Lark_0026_74910.jpg train completed - 6\n", + "Masking Horned_Lark_0035_74555.jpg train completed - 7\n", + "Masking Horned_Lark_0048_73894.jpg train completed - 8\n", + "Masking Horned_Lark_0006_73836.jpg train completed - 9\n", + "Masking Horned_Lark_0010_75291.jpg train completed - 10\n", + "Masking Horned_Lark_0139_74492.jpg train completed - 11\n", + 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- 7\n", + "Masking Horned_Lark_0012_74511.jpg valid completed - 8\n", + "Masking Horned_Lark_0126_74354.jpg valid completed - 9\n", + "Masking Horned_Lark_0091_74087.jpg valid completed - 10\n", + "Masking Horned_Lark_0013_74374.jpg valid completed - 11\n", + "Masking Horned_Lark_0087_73989.jpg valid completed - 12\n", + "Masking Horned_Lark_0037_74696.jpg test completed - 1\n", + "Masking Horned_Lark_0009_74646.jpg test completed - 2\n", + "Masking Horned_Lark_0125_73910.jpg test completed - 3\n", + "Masking Horned_Lark_0028_74408.jpg test completed - 4\n", + "Masking Horned_Lark_0141_74396.jpg test completed - 5\n", + "Masking Horned_Lark_0112_73971.jpg test completed - 6\n", + "Masking Horned_Lark_0115_74271.jpg test completed - 7\n", + "Masking Horned_Lark_0096_74075.jpg test completed - 8\n", + "Masking Horned_Lark_0049_74574.jpg test completed - 9\n", + "Masking Horned_Lark_0043_74450.jpg test completed - 10\n", + "Masking Horned_Lark_0072_74067.jpg test completed - 11\n", + 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"Masking Mallard_0004_76958.jpg valid completed - 7\n", + "Masking Mallard_0052_76946.jpg valid completed - 8\n", + "Masking Mallard_0016_76315.jpg valid completed - 9\n", + "Masking Mallard_0025_76465.jpg valid completed - 10\n", + "Masking Mallard_0107_76498.jpg valid completed - 11\n", + "Masking Mallard_0064_76654.jpg valid completed - 12\n", + "Masking Mallard_0112_77046.jpg test completed - 1\n", + "Masking Mallard_0046_76165.jpg test completed - 2\n", + "Masking Mallard_0005_76026.jpg test completed - 3\n", + "Masking Mallard_0086_76567.jpg test completed - 4\n", + "Masking Mallard_0081_76266.jpg test completed - 5\n", + "Masking Mallard_0077_75911.jpg test completed - 6\n", + "Masking Mallard_0018_76511.jpg test completed - 7\n", + "Masking Mallard_0013_77619.jpg test completed - 8\n", + "Masking Mallard_0111_76722.jpg test completed - 9\n", + "Masking Mallard_0043_77038.jpg test completed - 10\n", + "Masking Mallard_0055_77102.jpg test completed - 11\n", + "Masking 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Western_Meadowlark_0107_78608.jpg valid completed - 2\n", + "Masking Western_Meadowlark_0083_78197.jpg valid completed - 3\n", + "Masking Western_Meadowlark_0022_78410.jpg valid completed - 4\n", + "Masking Western_Meadowlark_0097_78239.jpg valid completed - 5\n", + "Masking Western_Meadowlark_0050_77864.jpg valid completed - 6\n", + "Masking Western_Meadowlark_0103_78500.jpg valid completed - 7\n", + "Masking Western_Meadowlark_0040_77823.jpg valid completed - 8\n", + "Masking Western_Meadowlark_0098_78382.jpg valid completed - 9\n", + "Masking Western_Meadowlark_0060_78368.jpg valid completed - 10\n", + "Masking Western_Meadowlark_0121_78402.jpg valid completed - 11\n", + "Masking Western_Meadowlark_0086_78636.jpg valid completed - 12\n", + "Masking Western_Meadowlark_0100_78037.jpg test completed - 1\n", + "Masking Western_Meadowlark_0076_77811.jpg test completed - 2\n", + "Masking Western_Meadowlark_0064_78027.jpg test completed - 3\n", + "Masking Western_Meadowlark_0052_77781.jpg test completed - 4\n", + "Masking Western_Meadowlark_0120_77834.jpg test completed - 5\n", + "Masking Western_Meadowlark_0038_77785.jpg test completed - 6\n", + "Masking Western_Meadowlark_0024_78432.jpg test completed - 7\n", + "Masking Western_Meadowlark_0014_78421.jpg test completed - 8\n", + "Masking Western_Meadowlark_0095_78568.jpg test completed - 9\n", + "Masking Western_Meadowlark_0053_77774.jpg test completed - 10\n", + "Masking Western_Meadowlark_0013_77712.jpg test completed - 11\n", + "Masking Western_Meadowlark_0125_77850.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/089.Hooded_Merganser\n", + "Train indexes: [47 10 8 22 18 17 16 39 41 13 31 23 38 30 36 29 50 26 3 14 25 45 2 4\n", + " 28 6 34 58 46 53 1 12 48 37 56 27]\n", + "Validation indexes: [57 24 55 32 33 44 59 11 35 43 52 42]\n", + "Test indexes: [19 21 51 20 40 15 7 9 54 0 49 5]\n", + "Masking Hooded_Merganser_0076_796756.jpg train completed - 1\n", + "Masking Hooded_Merganser_0016_79079.jpg 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Hooded_Merganser_0072_78973.jpg valid completed - 10\n", + "Masking Hooded_Merganser_0085_79146.jpg valid completed - 11\n", + "Masking Hooded_Merganser_0071_796760.jpg valid completed - 12\n", + "Masking Hooded_Merganser_0028_79061.jpg test completed - 1\n", + "Masking Hooded_Merganser_0034_796762.jpg test completed - 2\n", + "Masking Hooded_Merganser_0084_78954.jpg test completed - 3\n", + "Masking Hooded_Merganser_0032_79032.jpg test completed - 4\n", + "Masking Hooded_Merganser_0068_79057.jpg test completed - 5\n", + "Masking Hooded_Merganser_0023_796784.jpg test completed - 6\n", + "Masking Hooded_Merganser_0013_796754.jpg test completed - 7\n", + "Masking Hooded_Merganser_0015_79132.jpg test completed - 8\n", + "Masking Hooded_Merganser_0087_78972.jpg test completed - 9\n", + "Masking Hooded_Merganser_0002_796781.jpg test completed - 10\n", + "Masking Hooded_Merganser_0079_796768.jpg test completed - 11\n", + "Masking Hooded_Merganser_0011_796750.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/090.Red_breasted_Merganser\n", + "Train indexes: [11 28 51 56 24 13 14 55 39 15 46 29 59 25 38 26 41 54 50 34 8 5 36 17\n", + " 37 4 48 47 19 40 6 22 35 3 53 32]\n", + "Validation indexes: [ 9 20 0 2 21 42 7 30 44 31 58 43]\n", + "Test indexes: [27 10 18 1 12 33 49 16 52 57 23 45]\n", + "Masking Red_Breasted_Merganser_0016_79476.jpg train completed - 1\n", + "Masking Red_Breasted_Merganser_0044_79321.jpg train completed - 2\n", + "Masking Red_Breasted_Merganser_0076_79312.jpg train completed - 3\n", + "Masking Red_Breasted_Merganser_0081_79396.jpg train completed - 4\n", + "Masking Red_Breasted_Merganser_0040_79207.jpg train completed - 5\n", + "Masking Red_Breasted_Merganser_0021_79168.jpg train completed - 6\n", + "Masking Red_Breasted_Merganser_0022_79274.jpg train completed - 7\n", + "Masking Red_Breasted_Merganser_0080_79488.jpg train completed - 8\n", + "Masking Red_Breasted_Merganser_0061_79453.jpg train completed - 9\n", + "Masking 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Orchard_Oriole_0046_91646.jpg valid completed - 12\n", + "Masking Orchard_Oriole_0019_91338.jpg test completed - 1\n", + "Masking Orchard_Oriole_0075_91847.jpg test completed - 2\n", + "Masking Orchard_Oriole_0026_91444.jpg test completed - 3\n", + "Masking Orchard_Oriole_0060_91536.jpg test completed - 4\n", + "Masking Orchard_Oriole_0070_91383.jpg test completed - 5\n", + "Masking Orchard_Oriole_0082_91654.jpg test completed - 6\n", + "Masking Orchard_Oriole_0107_91472.jpg test completed - 7\n", + "Masking Orchard_Oriole_0041_91258.jpg test completed - 8\n", + "Masking Orchard_Oriole_0048_91393.jpg test completed - 9\n", + "Masking Orchard_Oriole_0059_92046.jpg test completed - 10\n", + "Masking Orchard_Oriole_0055_91515.jpg test completed - 11\n", + "Masking Orchard_Oriole_0061_91941.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/098.Scott_Oriole\n", + "Train indexes: [16 42 30 59 3 4 8 17 48 11 52 49 37 33 23 34 7 22 36 54 18 53 26 39\n", + " 46 43 41 24 14 1 13 5 19 9 28 45]\n", + "Validation indexes: [20 31 55 21 15 2 27 57 47 35 6 25]\n", + "Test indexes: [58 0 40 29 12 56 51 44 50 32 10 38]\n", + "Masking Scott_Oriole_0023_795835.jpg train completed - 1\n", + "Masking Scott_Oriole_0063_795812.jpg train completed - 2\n", + "Masking Scott_Oriole_0043_795828.jpg train completed - 3\n", + "Masking Scott_Oriole_0085_92206.jpg train completed - 4\n", + "Masking Scott_Oriole_0005_92362.jpg train completed - 5\n", + "Masking Scott_Oriole_0007_92439.jpg train completed - 6\n", + "Masking Scott_Oriole_0012_795837.jpg train completed - 7\n", + "Masking Scott_Oriole_0024_92302.jpg train completed - 8\n", + "Masking Scott_Oriole_0070_795808.jpg train completed - 9\n", + "Masking Scott_Oriole_0016_92398.jpg train completed - 10\n", + "Masking Scott_Oriole_0075_795817.jpg train completed - 11\n", + "Masking Scott_Oriole_0071_795848.jpg train completed - 12\n", + "Masking Scott_Oriole_0054_92310.jpg train completed - 13\n", + "Masking 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12\n", + "/kaggle/working/CUB_200_2011/images/099.Ovenbird\n", + "Train indexes: [30 52 31 37 13 18 45 28 33 17 47 34 51 20 4 41 29 50 48 15 36 59 38 32\n", + " 11 58 1 5 25 10 54 56 2 7 8 3]\n", + "Validation indexes: [46 35 9 53 14 57 40 49 6 16 19 24]\n", + "Test indexes: [42 39 22 21 23 55 0 44 43 27 12 26]\n", + "Masking Ovenbird_0070_93140.jpg train completed - 1\n", + "Masking Ovenbird_0124_93103.jpg train completed - 2\n", + "Masking Ovenbird_0077_92590.jpg train completed - 3\n", + "Masking Ovenbird_0090_93375.jpg train completed - 4\n", + "Masking Ovenbird_0027_93086.jpg train completed - 5\n", + "Masking Ovenbird_0043_93374.jpg train completed - 6\n", + "Masking Ovenbird_0101_93104.jpg train completed - 7\n", + "Masking Ovenbird_0059_92470.jpg train completed - 8\n", + "Masking Ovenbird_0081_93115.jpg train completed - 9\n", + "Masking Ovenbird_0035_92785.jpg train completed - 10\n", + "Masking Ovenbird_0104_92763.jpg train completed - 11\n", + "Masking 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51 31 5 25 47 52 50 57 17 41 21 26 43 6 24 13 37 56\n", + " 59 20 30 0 3 45 36 53 23 32 16 8]\n", + "Validation indexes: [ 9 39 35 38 7 12 15 29 28 46 2 22]\n", + "Test indexes: [55 44 49 48 27 34 40 4 1 11 14 19]\n", + "Masking Brown_Pelican_0102_93727.jpg train completed - 1\n", + "Masking Brown_Pelican_0019_95158.jpg train completed - 2\n", + "Masking Brown_Pelican_0043_94506.jpg train completed - 3\n", + "Masking Brown_Pelican_0128_94059.jpg train completed - 4\n", + "Masking Brown_Pelican_0140_93438.jpg train completed - 5\n", + "Masking Brown_Pelican_0080_93439.jpg train completed - 6\n", + "Masking Brown_Pelican_0124_93684.jpg train completed - 7\n", + "Masking Brown_Pelican_0075_95357.jpg train completed - 8\n", + "Masking Brown_Pelican_0011_94683.jpg train completed - 9\n", + "Masking Brown_Pelican_0068_94430.jpg train completed - 10\n", + "Masking Brown_Pelican_0118_93475.jpg train completed - 11\n", + "Masking Brown_Pelican_0125_93461.jpg train completed - 12\n", + "Masking 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completed - 10\n", + "Masking White_Pelican_0072_96975.jpg test completed - 1\n", + "Masking White_Pelican_0035_96312.jpg test completed - 2\n", + "Masking White_Pelican_0003_96691.jpg test completed - 3\n", + "Masking White_Pelican_0042_97250.jpg test completed - 4\n", + "Masking White_Pelican_0037_95570.jpg test completed - 5\n", + "Masking White_Pelican_0086_95538.jpg test completed - 6\n", + "Masking White_Pelican_0022_95897.jpg test completed - 7\n", + "Masking White_Pelican_0066_95495.jpg test completed - 8\n", + "Masking White_Pelican_0073_96260.jpg test completed - 9\n", + "Masking White_Pelican_0046_96888.jpg test completed - 10\n", + "/kaggle/working/CUB_200_2011/images/102.Western_Wood_Pewee\n", + "Train indexes: [39 46 25 23 15 49 3 35 40 38 26 14 34 11 27 47 12 7 29 58 59 54 19 31\n", + " 16 1 18 44 24 28 10 41 50 0 36 57]\n", + "Validation indexes: [21 37 17 20 9 55 22 13 32 8 53 52]\n", + "Test indexes: [51 5 56 48 4 30 2 33 45 43 42 6]\n", + "Masking 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5\n", + "Masking Sayornis_0099_98593.jpg valid completed - 6\n", + "Masking Sayornis_0007_99117.jpg valid completed - 7\n", + "Masking Sayornis_0129_98924.jpg valid completed - 8\n", + "Masking Sayornis_0079_98434.jpg valid completed - 9\n", + "Masking Sayornis_0038_98441.jpg valid completed - 10\n", + "Masking Sayornis_0127_98865.jpg valid completed - 11\n", + "Masking Sayornis_0115_99335.jpg valid completed - 12\n", + "Masking Sayornis_0039_98420.jpg test completed - 1\n", + "Masking Sayornis_0075_98350.jpg test completed - 2\n", + "Masking Sayornis_0058_98798.jpg test completed - 3\n", + "Masking Sayornis_0067_98930.jpg test completed - 4\n", + "Masking Sayornis_0085_99503.jpg test completed - 5\n", + "Masking Sayornis_0005_98958.jpg test completed - 6\n", + "Masking Sayornis_0081_99508.jpg test completed - 7\n", + "Masking Sayornis_0011_98610.jpg test completed - 8\n", + "Masking Sayornis_0108_98553.jpg test completed - 9\n", + "Masking Sayornis_0094_98512.jpg test completed - 10\n", + "Masking Sayornis_0021_98710.jpg test completed - 11\n", + "Masking Sayornis_0042_98874.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/104.American_Pipit\n", + "Train indexes: [33 9 41 13 43 0 54 5 12 30 39 42 15 50 32 31 38 48 55 16 45 36 19 25\n", + " 26 17 37 35 34 6 51 47 18 7 22 52]\n", + "Validation indexes: [27 56 53 20 21 14 24 23 28 44 40 11]\n", + "Test indexes: [ 4 46 49 57 29 1 3 10 8 59 2 58]\n", + "Masking American_Pipit_0073_99642.jpg train completed - 1\n", + "Masking American_Pipit_0019_99810.jpg train completed - 2\n", + "Masking American_Pipit_0088_100213.jpg train completed - 3\n", + "Masking American_Pipit_0027_100189.jpg train completed - 4\n", + "Masking American_Pipit_0090_99651.jpg train completed - 5\n", + "Masking American_Pipit_0001_100225.jpg train completed - 6\n", + "Masking American_Pipit_0114_100380.jpg train completed - 7\n", + "Masking American_Pipit_0010_99843.jpg train completed - 8\n", + "Masking American_Pipit_0024_99813.jpg train completed - 9\n", + "Masking American_Pipit_0067_100237.jpg train completed - 10\n", + "Masking American_Pipit_0085_100246.jpg train completed - 11\n", + "Masking American_Pipit_0089_100260.jpg train completed - 12\n", + "Masking American_Pipit_0031_99607.jpg train completed - 13\n", + "Masking American_Pipit_0104_100147.jpg train completed - 14\n", + "Masking American_Pipit_0072_99631.jpg train completed - 15\n", + "Masking American_Pipit_0070_100061.jpg train completed - 16\n", + "Masking American_Pipit_0082_99867.jpg train completed - 17\n", + "Masking American_Pipit_0101_100053.jpg train completed - 18\n", + "Masking American_Pipit_0116_100015.jpg train completed - 19\n", + "Masking American_Pipit_0034_99946.jpg train completed - 20\n", + "Masking American_Pipit_0095_99959.jpg train completed - 21\n", + "Masking American_Pipit_0080_100055.jpg train completed - 22\n", + "Masking American_Pipit_0037_99954.jpg train completed - 23\n", + "Masking 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Black_Throated_Sparrow_0059_107060.jpg valid completed - 10\n", + "Masking Black_Throated_Sparrow_0097_106935.jpg valid completed - 11\n", + "Masking Black_Throated_Sparrow_0055_107213.jpg valid completed - 12\n", + "Masking Black_Throated_Sparrow_0081_107111.jpg test completed - 1\n", + "Masking Black_Throated_Sparrow_0074_107113.jpg test completed - 2\n", + "Masking Black_Throated_Sparrow_0017_107355.jpg test completed - 3\n", + "Masking Black_Throated_Sparrow_0007_106999.jpg test completed - 4\n", + "Masking Black_Throated_Sparrow_0066_106974.jpg test completed - 5\n", + "Masking Black_Throated_Sparrow_0021_107021.jpg test completed - 6\n", + "Masking Black_Throated_Sparrow_0080_107050.jpg test completed - 7\n", + "Masking Black_Throated_Sparrow_0015_107079.jpg test completed - 8\n", + "Masking Black_Throated_Sparrow_0027_107278.jpg test completed - 9\n", + "Masking Black_Throated_Sparrow_0010_107375.jpg test completed - 10\n", + "Masking Black_Throated_Sparrow_0050_107033.jpg test completed - 11\n", + "Masking Black_Throated_Sparrow_0068_106960.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/115.Brewer_Sparrow\n", + "Train indexes: [19 31 0 49 53 10 43 42 46 18 50 7 34 48 30 4 2 47 23 37 3 16 24 36\n", + " 57 11 14 27 39 9 55 17 1 41 26]\n", + "Validation indexes: [25 15 45 29 12 40 38 28 51 58 8 35]\n", + "Test indexes: [56 21 6 52 22 44 5 33 20 13 32 54]\n", + "Masking Brewer_Sparrow_0027_796713.jpg train completed - 1\n", + "Masking Brewer_Sparrow_0043_107479.jpg train completed - 2\n", + "Masking Brewer_Sparrow_0001_796718.jpg train completed - 3\n", + "Masking Brewer_Sparrow_0066_107510.jpg train completed - 4\n", + "Masking Brewer_Sparrow_0070_796717.jpg train completed - 5\n", + "Masking Brewer_Sparrow_0012_107411.jpg train completed - 6\n", + "Masking Brewer_Sparrow_0060_107391.jpg train completed - 7\n", + "Masking Brewer_Sparrow_0058_796701.jpg train completed - 8\n", + "Masking Brewer_Sparrow_0063_796714.jpg train completed - 9\n", + "Masking Brewer_Sparrow_0026_107432.jpg train completed - 10\n", + "Masking Brewer_Sparrow_0067_107480.jpg train completed - 11\n", + "Masking Brewer_Sparrow_0009_107481.jpg train completed - 12\n", + "Masking Brewer_Sparrow_0047_107428.jpg train completed - 13\n", + "Masking Brewer_Sparrow_0065_107419.jpg train completed - 14\n", + "Masking Brewer_Sparrow_0042_796721.jpg train completed - 15\n", + "Masking Brewer_Sparrow_0006_107463.jpg train completed - 16\n", + "Masking Brewer_Sparrow_0004_107496.jpg train completed - 17\n", + "Masking Brewer_Sparrow_0064_107502.jpg train completed - 18\n", + "Masking Brewer_Sparrow_0032_796702.jpg train completed - 19\n", + "Masking Brewer_Sparrow_0051_796710.jpg train completed - 20\n", + "Masking Brewer_Sparrow_0005_107443.jpg train completed - 21\n", + "Masking Brewer_Sparrow_0023_107489.jpg train completed - 22\n", + "Masking Brewer_Sparrow_0034_796693.jpg train completed - 23\n", + "Masking Brewer_Sparrow_0050_107460.jpg train completed - 24\n", + "Masking Brewer_Sparrow_0074_107408.jpg train completed - 25\n", + "Masking Brewer_Sparrow_0014_107435.jpg train completed - 26\n", + "Masking Brewer_Sparrow_0020_796697.jpg train completed - 27\n", + "Masking Brewer_Sparrow_0037_107442.jpg train completed - 28\n", + "Masking Brewer_Sparrow_0053_796694.jpg train completed - 29\n", + "Masking Brewer_Sparrow_0011_796708.jpg train completed - 30\n", + "Masking Brewer_Sparrow_0072_796715.jpg train completed - 31\n", + "Masking Brewer_Sparrow_0024_107439.jpg train completed - 32\n", + "Masking Brewer_Sparrow_0003_107459.jpg train completed - 33\n", + "Masking Brewer_Sparrow_0056_107458.jpg train completed - 34\n", + "Masking Brewer_Sparrow_0036_107451.jpg train completed - 35\n", + "Masking Brewer_Sparrow_0035_107509.jpg valid completed - 1\n", + "Masking Brewer_Sparrow_0022_107440.jpg valid completed - 2\n", + "Masking Brewer_Sparrow_0062_107456.jpg valid completed - 3\n", + "Masking Brewer_Sparrow_0041_796711.jpg valid completed - 4\n", + "Masking Brewer_Sparrow_0018_107437.jpg valid completed - 5\n", + "Masking Brewer_Sparrow_0055_796720.jpg valid completed - 6\n", + "Masking Brewer_Sparrow_0052_107478.jpg valid completed - 7\n", + "Masking Brewer_Sparrow_0039_107431.jpg valid completed - 8\n", + "Masking Brewer_Sparrow_0068_107422.jpg valid completed - 9\n", + "Masking Brewer_Sparrow_0076_107393.jpg valid completed - 10\n", + "Masking Brewer_Sparrow_0010_796716.jpg valid completed - 11\n", + "Masking Brewer_Sparrow_0049_796705.jpg valid completed - 12\n", + "Masking Brewer_Sparrow_0073_107518.jpg test completed - 1\n", + "Masking Brewer_Sparrow_0029_796699.jpg test completed - 2\n", + "Masking Brewer_Sparrow_0008_796703.jpg test completed - 3\n", + "Masking Brewer_Sparrow_0069_796709.jpg test completed - 4\n", + "Masking Brewer_Sparrow_0031_796712.jpg test completed - 5\n", + "Masking Brewer_Sparrow_0061_107438.jpg test completed - 6\n", + "Masking Brewer_Sparrow_0007_796700.jpg test completed - 7\n", + "Masking Brewer_Sparrow_0046_796696.jpg test completed - 8\n", + "Masking Brewer_Sparrow_0028_107467.jpg test completed - 9\n", + "Masking Brewer_Sparrow_0019_107452.jpg test completed - 10\n", + "Masking Brewer_Sparrow_0044_796698.jpg test completed - 11\n", + "Masking Brewer_Sparrow_0071_107446.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/116.Chipping_Sparrow\n", + "Train indexes: [47 49 42 9 50 55 14 15 41 29 31 45 20 11 35 25 39 22 53 38 12 21 58 43\n", + " 13 7 2 34 40 56 44 33 8 54 26 37]\n", + "Validation indexes: [27 18 32 17 28 5 6 1 46 52 3 57]\n", + "Test indexes: [16 30 4 10 36 24 48 23 51 59 0 19]\n", + "Masking Chipping_Sparrow_0085_109506.jpg train completed - 1\n", + "Masking Chipping_Sparrow_0088_107562.jpg train completed - 2\n", + "Masking Chipping_Sparrow_0074_108401.jpg train completed - 3\n", + "Masking Chipping_Sparrow_0016_109051.jpg train completed - 4\n", + "Masking Chipping_Sparrow_0089_107535.jpg train completed - 5\n", + "Masking Chipping_Sparrow_0098_108644.jpg train completed - 6\n", + "Masking Chipping_Sparrow_0023_108684.jpg train completed - 7\n", + "Masking Chipping_Sparrow_0024_109445.jpg train completed - 8\n", + "Masking Chipping_Sparrow_0073_108881.jpg train completed - 9\n", + "Masking Chipping_Sparrow_0045_109985.jpg train completed - 10\n", + "Masking Chipping_Sparrow_0052_109874.jpg train completed - 11\n", + "Masking Chipping_Sparrow_0079_110449.jpg train completed - 12\n", + "Masking Chipping_Sparrow_0033_109069.jpg train completed - 13\n", + "Masking Chipping_Sparrow_0018_108295.jpg train completed - 14\n", + "Masking Chipping_Sparrow_0063_108139.jpg train completed - 15\n", + "Masking Chipping_Sparrow_0039_107864.jpg train completed - 16\n", + "Masking Chipping_Sparrow_0070_108281.jpg train completed - 17\n", + "Masking Chipping_Sparrow_0036_107695.jpg train completed - 18\n", + "Masking Chipping_Sparrow_0096_109853.jpg train completed - 19\n", + "Masking Chipping_Sparrow_0067_107508.jpg train completed - 20\n", + "Masking Chipping_Sparrow_0021_109588.jpg train completed - 21\n", + "Masking Chipping_Sparrow_0035_110138.jpg train completed - 22\n", + "Masking Chipping_Sparrow_0110_108974.jpg train completed - 23\n", + "Masking Chipping_Sparrow_0075_108348.jpg train completed - 24\n", + "Masking Chipping_Sparrow_0022_107825.jpg train completed - 25\n", + "Masking Chipping_Sparrow_0012_108576.jpg train completed - 26\n", + "Masking Chipping_Sparrow_0005_108931.jpg train completed - 27\n", + "Masking Chipping_Sparrow_0062_110187.jpg train completed - 28\n", + "Masking Chipping_Sparrow_0071_108735.jpg train completed - 29\n", + "Masking Chipping_Sparrow_0103_109529.jpg train completed - 30\n", + "Masking Chipping_Sparrow_0076_108919.jpg train completed - 31\n", + "Masking Chipping_Sparrow_0061_109113.jpg train completed - 32\n", + "Masking Chipping_Sparrow_0015_108462.jpg train completed - 33\n", + "Masking Chipping_Sparrow_0097_107678.jpg train completed - 34\n", + "Masking Chipping_Sparrow_0041_108370.jpg train completed - 35\n", + "Masking Chipping_Sparrow_0066_109187.jpg train completed - 36\n", + "Masking Chipping_Sparrow_0042_107791.jpg valid completed - 1\n", + "Masking Chipping_Sparrow_0030_109741.jpg valid completed - 2\n", + "Masking Chipping_Sparrow_0053_109774.jpg valid completed - 3\n", + "Masking Chipping_Sparrow_0026_109010.jpg valid completed - 4\n", + "Masking Chipping_Sparrow_0043_109509.jpg valid completed - 5\n", + "Masking Chipping_Sparrow_0010_109760.jpg valid completed - 6\n", + "Masking Chipping_Sparrow_0011_108081.jpg valid completed - 7\n", + "Masking Chipping_Sparrow_0004_108430.jpg valid completed - 8\n", + "Masking Chipping_Sparrow_0080_108685.jpg valid completed - 9\n", + "Masking Chipping_Sparrow_0091_108308.jpg valid completed - 10\n", + "Masking Chipping_Sparrow_0006_107849.jpg valid completed - 11\n", + "Masking Chipping_Sparrow_0109_108162.jpg valid completed - 12\n", + "Masking Chipping_Sparrow_0025_108653.jpg test completed - 1\n", + "Masking Chipping_Sparrow_0050_108441.jpg test completed - 2\n", + "Masking Chipping_Sparrow_0008_107810.jpg test completed - 3\n", + "Masking Chipping_Sparrow_0017_107552.jpg test completed - 4\n", + "Masking Chipping_Sparrow_0064_108204.jpg test completed - 5\n", + "Masking Chipping_Sparrow_0038_109234.jpg test completed - 6\n", + "Masking Chipping_Sparrow_0086_109022.jpg test completed - 7\n", + "Masking Chipping_Sparrow_0037_109851.jpg test completed - 8\n", + "Masking Chipping_Sparrow_0090_14718.jpg test completed - 9\n", + "Masking Chipping_Sparrow_0111_108515.jpg test completed - 10\n", + "Masking Chipping_Sparrow_0001_108638.jpg test completed - 11\n", + "Masking Chipping_Sparrow_0032_108882.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/117.Clay_colored_Sparrow\n", + "Train indexes: [24 15 1 10 53 49 40 9 32 54 48 26 51 39 30 42 41 3 21 46 31 5 36 6\n", + " 25 27 20 0 37 56 2 33 4 47 45]\n", + "Validation indexes: [34 29 44 7 43 35 17 55 8 38 12 19]\n", + "Test indexes: [22 14 58 50 57 16 28 18 52 11 13 23]\n", + "Masking Clay_Colored_Sparrow_0044_110942.jpg train completed - 1\n", + "Masking Clay_Colored_Sparrow_0031_110769.jpg train completed - 2\n", + "Masking Clay_Colored_Sparrow_0002_110606.jpg train completed - 3\n", + "Masking Clay_Colored_Sparrow_0022_110518.jpg train completed - 4\n", + "Masking Clay_Colored_Sparrow_0099_110561.jpg train completed - 5\n", + "Masking Clay_Colored_Sparrow_0094_797250.jpg train completed - 6\n", + "Masking Clay_Colored_Sparrow_0078_110912.jpg train completed - 7\n", + "Masking Clay_Colored_Sparrow_0020_110609.jpg train completed - 8\n", + "Masking Clay_Colored_Sparrow_0062_110908.jpg train completed - 9\n", + "Masking Clay_Colored_Sparrow_0100_110545.jpg train completed - 10\n", + "Masking Clay_Colored_Sparrow_0093_110677.jpg train completed - 11\n", + "Masking Clay_Colored_Sparrow_0048_110653.jpg train completed - 12\n", + "Masking Clay_Colored_Sparrow_0097_110516.jpg train completed - 13\n", + "Masking Clay_Colored_Sparrow_0075_797251.jpg train completed - 14\n", + "Masking Clay_Colored_Sparrow_0056_110848.jpg train completed - 15\n", + "Masking Clay_Colored_Sparrow_0081_110682.jpg train completed - 16\n", + "Masking Clay_Colored_Sparrow_0080_797253.jpg train completed - 17\n", + "Masking Clay_Colored_Sparrow_0005_110911.jpg train completed - 18\n", + "Masking Clay_Colored_Sparrow_0041_110726.jpg train completed - 19\n", + "Masking Clay_Colored_Sparrow_0090_110669.jpg train completed - 20\n", + "Masking Clay_Colored_Sparrow_0061_110830.jpg train completed - 21\n", + "Masking Clay_Colored_Sparrow_0009_797256.jpg train completed - 22\n", + "Masking Clay_Colored_Sparrow_0071_110656.jpg train completed - 23\n", + "Masking Clay_Colored_Sparrow_0010_797262.jpg train completed - 24\n", + "Masking Clay_Colored_Sparrow_0046_110728.jpg train completed - 25\n", + "Masking Clay_Colored_Sparrow_0049_110736.jpg train completed - 26\n", + "Masking Clay_Colored_Sparrow_0040_110717.jpg train completed - 27\n", + "Masking Clay_Colored_Sparrow_0001_110632.jpg train completed - 28\n", + "Masking Clay_Colored_Sparrow_0072_110851.jpg train completed - 29\n", + "Masking Clay_Colored_Sparrow_0105_110547.jpg train completed - 30\n", + "Masking Clay_Colored_Sparrow_0003_110672.jpg train completed - 31\n", + "Masking Clay_Colored_Sparrow_0064_110664.jpg train completed - 32\n", + "Masking Clay_Colored_Sparrow_0008_110536.jpg train completed - 33\n", + "Masking Clay_Colored_Sparrow_0091_110768.jpg train completed - 34\n", + "Masking Clay_Colored_Sparrow_0087_110946.jpg train completed - 35\n", + "Masking Clay_Colored_Sparrow_0066_110819.jpg valid completed - 1\n", + "Masking Clay_Colored_Sparrow_0055_110647.jpg valid completed - 2\n", + "Masking Clay_Colored_Sparrow_0085_797260.jpg valid completed - 3\n", + "Masking Clay_Colored_Sparrow_0014_797248.jpg valid completed - 4\n", + "Masking Clay_Colored_Sparrow_0083_110734.jpg valid completed - 5\n", + "Masking Clay_Colored_Sparrow_0068_110706.jpg valid completed - 6\n", + "Masking Clay_Colored_Sparrow_0034_797252.jpg valid completed - 7\n", + "Masking Clay_Colored_Sparrow_0104_110699.jpg valid completed - 8\n", + "Masking Clay_Colored_Sparrow_0018_797246.jpg valid completed - 9\n", + "Masking Clay_Colored_Sparrow_0073_110718.jpg valid completed - 10\n", + "Masking Clay_Colored_Sparrow_0026_110774.jpg valid completed - 11\n", + "Masking Clay_Colored_Sparrow_0038_110799.jpg valid completed - 12\n", + "Masking Clay_Colored_Sparrow_0042_797243.jpg test completed - 1\n", + "Masking Clay_Colored_Sparrow_0029_110720.jpg test completed - 2\n", + "Masking Clay_Colored_Sparrow_0107_110825.jpg test completed - 3\n", + "Masking Clay_Colored_Sparrow_0095_110635.jpg test completed - 4\n", + "Masking Clay_Colored_Sparrow_0106_797247.jpg test completed - 5\n", + "Masking Clay_Colored_Sparrow_0033_797255.jpg test completed - 6\n", + "Masking Clay_Colored_Sparrow_0054_110948.jpg test completed - 7\n", + "Masking Clay_Colored_Sparrow_0036_110924.jpg test completed - 8\n", + "Masking Clay_Colored_Sparrow_0098_110735.jpg test completed - 9\n", + "Masking Clay_Colored_Sparrow_0023_797258.jpg test completed - 10\n", + "Masking Clay_Colored_Sparrow_0028_797241.jpg test completed - 11\n", + "Masking Clay_Colored_Sparrow_0043_110685.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/118.House_Sparrow\n", + "Train indexes: [28 59 23 17 48 49 22 27 3 16 35 12 15 26 57 29 54 11 38 32 50 19 1 41\n", + " 46 30 34 31 36 40 14 21 20 2 43 42]\n", + "Validation indexes: [ 4 44 58 10 33 7 53 0 24 56 5 52]\n", + "Test indexes: [47 6 51 45 13 55 39 25 37 9 8 18]\n", + "Masking House_Sparrow_0073_112745.jpg train completed - 1\n", + "Masking House_Sparrow_0145_112703.jpg train completed - 2\n", + "Masking House_Sparrow_0059_111164.jpg train completed - 3\n", + "Masking House_Sparrow_0046_112845.jpg train completed - 4\n", + "Masking House_Sparrow_0125_111130.jpg train completed - 5\n", + "Masking House_Sparrow_0126_110959.jpg train completed - 6\n", + "Masking House_Sparrow_0055_111393.jpg train completed - 7\n", + "Masking House_Sparrow_0068_113247.jpg train completed - 8\n", + "Masking House_Sparrow_0006_111034.jpg train completed - 9\n", + "Masking House_Sparrow_0041_112706.jpg train completed - 10\n", + "Masking House_Sparrow_0086_111385.jpg train completed - 11\n", + "Masking House_Sparrow_0028_113217.jpg train completed - 12\n", + "Masking House_Sparrow_0036_112847.jpg train completed - 13\n", + "Masking House_Sparrow_0067_112913.jpg train completed - 14\n", + "Masking House_Sparrow_0143_111400.jpg train completed - 15\n", + "Masking House_Sparrow_0074_111997.jpg train completed - 16\n", + "Masking House_Sparrow_0134_112644.jpg train completed - 17\n", + "Masking House_Sparrow_0025_111669.jpg train completed - 18\n", + "Masking House_Sparrow_0094_112773.jpg train completed - 19\n", + "Masking House_Sparrow_0082_112478.jpg train completed - 20\n", + "Masking House_Sparrow_0127_111935.jpg train completed - 21\n", + "Masking House_Sparrow_0050_111087.jpg train completed - 22\n", + "Masking House_Sparrow_0004_111989.jpg train completed - 23\n", + "Masking House_Sparrow_0104_113105.jpg train completed - 24\n", + "Masking House_Sparrow_0120_113001.jpg train completed - 25\n", + "Masking House_Sparrow_0079_113288.jpg train completed - 26\n", + "Masking House_Sparrow_0084_111300.jpg train completed - 27\n", + "Masking House_Sparrow_0080_111099.jpg train completed - 28\n", + "Masking House_Sparrow_0092_111413.jpg train completed - 29\n", + "Masking House_Sparrow_0098_111073.jpg train completed - 30\n", + "Masking House_Sparrow_0033_112590.jpg train completed - 31\n", + "Masking House_Sparrow_0053_111388.jpg train completed - 32\n", + "Masking House_Sparrow_0052_112252.jpg train completed - 33\n", + "Masking House_Sparrow_0005_111967.jpg train completed - 34\n", + "Masking House_Sparrow_0108_112963.jpg train completed - 35\n", + "Masking House_Sparrow_0106_111564.jpg train completed - 36\n", + "Masking House_Sparrow_0007_111029.jpg valid completed - 1\n", + "Masking House_Sparrow_0111_112968.jpg valid completed - 2\n", + "Masking House_Sparrow_0144_113216.jpg valid completed - 3\n", + "Masking House_Sparrow_0019_112645.jpg valid completed - 4\n", + "Masking House_Sparrow_0083_111470.jpg valid completed - 5\n", + "Masking House_Sparrow_0011_112099.jpg valid completed - 6\n", + "Masking House_Sparrow_0133_112368.jpg valid completed - 7\n", + "Masking House_Sparrow_0002_112905.jpg valid completed - 8\n", + "Masking House_Sparrow_0061_112795.jpg valid completed - 9\n", + "Masking House_Sparrow_0139_112438.jpg valid completed - 10\n", + "Masking House_Sparrow_0008_111204.jpg valid completed - 11\n", + "Masking House_Sparrow_0130_110985.jpg valid completed - 12\n", + "Masking House_Sparrow_0121_113182.jpg test completed - 1\n", + "Masking House_Sparrow_0010_112678.jpg test completed - 2\n", + "Masking House_Sparrow_0128_110971.jpg test completed - 3\n", + "Masking House_Sparrow_0119_111471.jpg test completed - 4\n", + "Masking House_Sparrow_0030_111387.jpg test completed - 5\n", + "Masking House_Sparrow_0137_111219.jpg test completed - 6\n", + "Masking House_Sparrow_0096_111519.jpg test completed - 7\n", + "Masking House_Sparrow_0063_111460.jpg test completed - 8\n", + "Masking House_Sparrow_0093_110990.jpg test completed - 9\n", + "Masking House_Sparrow_0015_111085.jpg test completed - 10\n", + "Masking House_Sparrow_0014_112947.jpg test completed - 11\n", + "Masking House_Sparrow_0049_110976.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/119.Field_Sparrow\n", + "Train indexes: [47 7 0 37 1 29 33 32 17 4 23 36 43 10 2 46 41 11 19 34 13 21 55 9\n", + " 31 42 49 51 22 5 20 45 50 30 15]\n", + "Validation indexes: [26 12 39 44 52 14 54 40 38 48 35 8]\n", + "Test indexes: [24 6 3 53 57 18 16 58 28 25 56 27]\n", + "Masking Field_Sparrow_0118_113416.jpg train completed 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Field_Sparrow_0025_113683.jpg valid completed - 12\n", + "Masking Field_Sparrow_0067_113448.jpg test completed - 1\n", + "Masking Field_Sparrow_0021_113461.jpg test completed - 2\n", + "Masking Field_Sparrow_0011_113420.jpg test completed - 3\n", + "Masking Field_Sparrow_0126_113426.jpg test completed - 4\n", + "Masking Field_Sparrow_0130_113846.jpg test completed - 5\n", + "Masking Field_Sparrow_0051_114107.jpg test completed - 6\n", + "Masking Field_Sparrow_0047_113801.jpg test completed - 7\n", + "Masking Field_Sparrow_0131_113582.jpg test completed - 8\n", + "Masking Field_Sparrow_0078_113575.jpg test completed - 9\n", + "Masking Field_Sparrow_0069_113827.jpg test completed - 10\n", + "Masking Field_Sparrow_0129_113748.jpg test completed - 11\n", + "Masking Field_Sparrow_0074_113504.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/120.Fox_Sparrow\n", + "Train indexes: [12 17 19 46 34 41 51 32 50 6 3 45 7 59 27 38 55 2 58 29 39 57 16 26\n", + " 11 8 23 52 9 13 10 0 5 49 36 42]\n", + "Validation indexes: [ 1 22 25 47 18 56 4 20 48 30 44 28]\n", + "Test indexes: [15 31 54 14 43 40 35 53 33 24 37 21]\n", + "Masking Fox_Sparrow_0036_115581.jpg train completed - 1\n", + "Masking Fox_Sparrow_0047_115022.jpg train completed - 2\n", + "Masking Fox_Sparrow_0053_114780.jpg train completed - 3\n", + "Masking Fox_Sparrow_0113_114389.jpg train completed - 4\n", + "Masking Fox_Sparrow_0086_115484.jpg train completed - 5\n", + "Masking Fox_Sparrow_0104_114908.jpg train completed - 6\n", + "Masking Fox_Sparrow_0119_115512.jpg train completed - 7\n", + "Masking Fox_Sparrow_0081_115630.jpg train completed - 8\n", + "Masking Fox_Sparrow_0118_114884.jpg train completed - 9\n", + "Masking Fox_Sparrow_0018_114468.jpg train completed - 10\n", + "Masking Fox_Sparrow_0013_114344.jpg train completed - 11\n", + "Masking Fox_Sparrow_0111_114527.jpg train completed - 12\n", + "Masking Fox_Sparrow_0020_114744.jpg train completed - 13\n", + "Masking Fox_Sparrow_0138_114586.jpg 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Fox_Sparrow_0039_114816.jpg train completed - 30\n", + "Masking Fox_Sparrow_0026_115281.jpg train completed - 31\n", + "Masking Fox_Sparrow_0009_114796.jpg train completed - 32\n", + "Masking Fox_Sparrow_0016_114213.jpg train completed - 33\n", + "Masking Fox_Sparrow_0116_115311.jpg train completed - 34\n", + "Masking Fox_Sparrow_0092_114774.jpg train completed - 35\n", + "Masking Fox_Sparrow_0106_114720.jpg train completed - 36\n", + "Masking Fox_Sparrow_0010_114728.jpg valid completed - 1\n", + "Masking Fox_Sparrow_0057_114355.jpg valid completed - 2\n", + "Masking Fox_Sparrow_0063_114350.jpg valid completed - 3\n", + "Masking Fox_Sparrow_0114_114481.jpg valid completed - 4\n", + "Masking Fox_Sparrow_0052_114878.jpg valid completed - 5\n", + "Masking Fox_Sparrow_0125_114557.jpg valid completed - 6\n", + "Masking Fox_Sparrow_0015_114650.jpg valid completed - 7\n", + "Masking Fox_Sparrow_0054_114541.jpg valid completed - 8\n", + "Masking Fox_Sparrow_0115_114855.jpg valid completed - 9\n", + "Masking Fox_Sparrow_0077_114944.jpg valid completed - 10\n", + "Masking Fox_Sparrow_0110_115172.jpg valid completed - 11\n", + "Masking Fox_Sparrow_0068_114967.jpg valid completed - 12\n", + "Masking Fox_Sparrow_0041_115218.jpg test completed - 1\n", + "Masking Fox_Sparrow_0078_114582.jpg test completed - 2\n", + "Masking Fox_Sparrow_0123_114488.jpg test completed - 3\n", + "Masking Fox_Sparrow_0040_115034.jpg test completed - 4\n", + "Masking Fox_Sparrow_0109_114859.jpg test completed - 5\n", + "Masking Fox_Sparrow_0103_115038.jpg test completed - 6\n", + "Masking Fox_Sparrow_0091_115550.jpg test completed - 7\n", + "Masking Fox_Sparrow_0122_114776.jpg test completed - 8\n", + "Masking Fox_Sparrow_0083_114496.jpg test completed - 9\n", + "Masking Fox_Sparrow_0062_115056.jpg test completed - 10\n", + "Masking Fox_Sparrow_0093_114757.jpg test completed - 11\n", + "Masking Fox_Sparrow_0055_114809.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/121.Grasshopper_Sparrow\n", + "Train indexes: [16 32 19 31 36 56 34 30 38 22 4 51 15 25 21 14 12 2 54 33 10 52 48 0\n", + " 5 11 46 45 18 28 37 58 24 17 55 35]\n", + "Validation indexes: [47 39 26 57 41 59 53 6 44 49 23 3]\n", + "Test indexes: [13 1 7 43 42 9 8 29 40 50 27 20]\n", + "Masking Grasshopper_Sparrow_0035_116049.jpg train completed - 1\n", + "Masking Grasshopper_Sparrow_0068_115799.jpg train completed - 2\n", + "Masking Grasshopper_Sparrow_0040_115696.jpg train completed - 3\n", + "Masking Grasshopper_Sparrow_0067_115979.jpg train completed - 4\n", + "Masking Grasshopper_Sparrow_0073_115996.jpg train completed - 5\n", + "Masking Grasshopper_Sparrow_0120_116021.jpg train completed - 6\n", + "Masking Grasshopper_Sparrow_0070_115645.jpg train completed - 7\n", + "Masking Grasshopper_Sparrow_0064_116106.jpg train completed - 8\n", + "Masking Grasshopper_Sparrow_0077_116127.jpg train completed - 9\n", + "Masking 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1\n", + "Masking Grasshopper_Sparrow_0078_116052.jpg valid completed - 2\n", + "Masking Grasshopper_Sparrow_0051_115923.jpg valid completed - 3\n", + "Masking Grasshopper_Sparrow_0121_116110.jpg valid completed - 4\n", + "Masking Grasshopper_Sparrow_0082_116300.jpg valid completed - 5\n", + "Masking Grasshopper_Sparrow_0126_116029.jpg valid completed - 6\n", + "Masking Grasshopper_Sparrow_0116_116066.jpg valid completed - 7\n", + "Masking Grasshopper_Sparrow_0014_116129.jpg valid completed - 8\n", + "Masking Grasshopper_Sparrow_0101_116094.jpg valid completed - 9\n", + "Masking Grasshopper_Sparrow_0109_115750.jpg valid completed - 10\n", + "Masking Grasshopper_Sparrow_0044_115681.jpg valid completed - 11\n", + "Masking Grasshopper_Sparrow_0009_115984.jpg valid completed - 12\n", + "Masking Grasshopper_Sparrow_0021_116107.jpg test completed - 1\n", + "Masking Grasshopper_Sparrow_0003_115676.jpg test completed - 2\n", + "Masking Grasshopper_Sparrow_0015_116352.jpg test completed - 3\n", + "Masking Grasshopper_Sparrow_0099_116069.jpg test completed - 4\n", + "Masking Grasshopper_Sparrow_0098_116027.jpg test completed - 5\n", + "Masking Grasshopper_Sparrow_0017_115908.jpg test completed - 6\n", + "Masking Grasshopper_Sparrow_0016_115695.jpg test completed - 7\n", + "Masking Grasshopper_Sparrow_0060_15159.jpg test completed - 8\n", + "Masking Grasshopper_Sparrow_0081_116326.jpg test completed - 9\n", + "Masking Grasshopper_Sparrow_0110_115644.jpg test completed - 10\n", + "Masking Grasshopper_Sparrow_0053_115991.jpg test completed - 11\n", + "Masking Grasshopper_Sparrow_0041_116288.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/122.Harris_Sparrow\n", + "Train indexes: [36 7 43 33 56 54 50 41 5 27 9 30 35 39 14 48 1 51 20 42 38 11 44 3\n", + " 46 59 12 53 10 2 8 19 31 17 0 22]\n", + "Validation indexes: [45 21 29 32 23 18 26 52 13 37 47 58]\n", + "Test indexes: [55 57 15 49 24 16 6 34 4 28 40 25]\n", + "Masking Harris_Sparrow_0057_116374.jpg train completed - 1\n", + "Masking Harris_Sparrow_0011_116597.jpg train completed - 2\n", + "Masking Harris_Sparrow_0070_116400.jpg train completed - 3\n", + "Masking Harris_Sparrow_0053_116595.jpg train completed - 4\n", + "Masking Harris_Sparrow_0087_116490.jpg train completed - 5\n", + "Masking Harris_Sparrow_0085_116534.jpg train completed - 6\n", + "Masking Harris_Sparrow_0081_116574.jpg train completed - 7\n", + "Masking Harris_Sparrow_0066_116362.jpg train completed - 8\n", + "Masking Harris_Sparrow_0007_116484.jpg train completed - 9\n", + "Masking Harris_Sparrow_0042_116499.jpg train completed - 10\n", + "Masking Harris_Sparrow_0016_116661.jpg train completed - 11\n", + "Masking Harris_Sparrow_0048_116405.jpg train completed - 12\n", + "Masking Harris_Sparrow_0056_116532.jpg train completed - 13\n", + "Masking Harris_Sparrow_0063_116383.jpg train completed - 14\n", + "Masking Harris_Sparrow_0023_116363.jpg train completed - 15\n", + "Masking Harris_Sparrow_0075_116404.jpg train 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train completed - 31\n", + "Masking Harris_Sparrow_0028_116656.jpg train completed - 32\n", + "Masking Harris_Sparrow_0050_116369.jpg train completed - 33\n", + "Masking Harris_Sparrow_0026_116620.jpg train completed - 34\n", + "Masking Harris_Sparrow_0001_116398.jpg train completed - 35\n", + "Masking Harris_Sparrow_0034_116439.jpg train completed - 36\n", + "Masking Harris_Sparrow_0072_116662.jpg valid completed - 1\n", + "Masking Harris_Sparrow_0033_116380.jpg valid completed - 2\n", + "Masking Harris_Sparrow_0046_116425.jpg valid completed - 3\n", + "Masking Harris_Sparrow_0052_116544.jpg valid completed - 4\n", + "Masking Harris_Sparrow_0035_116529.jpg valid completed - 5\n", + "Masking Harris_Sparrow_0027_116687.jpg valid completed - 6\n", + "Masking Harris_Sparrow_0041_116611.jpg valid completed - 7\n", + "Masking Harris_Sparrow_0083_116588.jpg valid completed - 8\n", + "Masking Harris_Sparrow_0021_116399.jpg valid completed - 9\n", + "Masking Harris_Sparrow_0059_116608.jpg valid completed - 10\n", + "Masking Harris_Sparrow_0074_116539.jpg valid completed - 11\n", + "Masking Harris_Sparrow_0089_116531.jpg valid completed - 12\n", + "Masking Harris_Sparrow_0086_116373.jpg test completed - 1\n", + "Masking Harris_Sparrow_0088_116445.jpg test completed - 2\n", + "Masking Harris_Sparrow_0024_116480.jpg test completed - 3\n", + "Masking Harris_Sparrow_0076_116509.jpg test completed - 4\n", + "Masking Harris_Sparrow_0038_116610.jpg test completed - 5\n", + "Masking Harris_Sparrow_0025_116648.jpg test completed - 6\n", + "Masking Harris_Sparrow_0010_116376.jpg test completed - 7\n", + "Masking Harris_Sparrow_0055_116512.jpg test completed - 8\n", + "Masking Harris_Sparrow_0006_116364.jpg test completed - 9\n", + "Masking Harris_Sparrow_0045_116603.jpg test completed - 10\n", + "Masking Harris_Sparrow_0065_116435.jpg test completed - 11\n", + "Masking Harris_Sparrow_0039_116409.jpg test completed - 12\n", + 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completed - 1\n", + "Masking Le_Conte_Sparrow_0006_795146.jpg test completed - 2\n", + "Masking Le_Conte_Sparrow_0012_795211.jpg test completed - 3\n", + "Masking Le_Conte_Sparrow_0045_795162.jpg test completed - 4\n", + "Masking Le_Conte_Sparrow_0063_795149.jpg test completed - 5\n", + "Masking Le_Conte_Sparrow_0084_795189.jpg test completed - 6\n", + "Masking Le_Conte_Sparrow_0015_795152.jpg test completed - 7\n", + "Masking Le_Conte_Sparrow_0053_795187.jpg test completed - 8\n", + "Masking Le_Conte_Sparrow_0022_117039.jpg test completed - 9\n", + "Masking Le_Conte_Sparrow_0061_795194.jpg test completed - 10\n", + "Masking Le_Conte_Sparrow_0039_117054.jpg test completed - 11\n", + "Masking Le_Conte_Sparrow_0035_795214.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/125.Lincoln_Sparrow\n", + "Train indexes: [56 28 12 48 0 8 18 34 15 16 31 43 23 25 7 26 9 19 27 10 33 42 21 44\n", + " 49 13 30 5 29 1 40 22 58 14 32]\n", + "Validation indexes: [11 53 47 4 41 37 3 2 36 38 20 6]\n", + "Test indexes: [52 45 46 55 39 54 24 50 35 57 51 17]\n", + "Masking Lincoln_Sparrow_0128_117851.jpg train completed - 1\n", + "Masking Lincoln_Sparrow_0061_117529.jpg train completed - 2\n", + "Masking Lincoln_Sparrow_0037_117837.jpg train completed - 3\n", + "Masking Lincoln_Sparrow_0108_117773.jpg train completed - 4\n", + "Masking Lincoln_Sparrow_0009_117535.jpg train completed - 5\n", + "Masking Lincoln_Sparrow_0032_117747.jpg train completed - 6\n", + "Masking Lincoln_Sparrow_0045_117547.jpg train completed - 7\n", + "Masking Lincoln_Sparrow_0072_117951.jpg train completed - 8\n", + "Masking Lincoln_Sparrow_0041_117636.jpg train completed - 9\n", + "Masking Lincoln_Sparrow_0042_117507.jpg train completed - 10\n", + "Masking Lincoln_Sparrow_0066_117875.jpg train completed - 11\n", + "Masking Lincoln_Sparrow_0090_117857.jpg train completed - 12\n", + "Masking Lincoln_Sparrow_0052_117521.jpg train completed - 13\n", + "Masking Lincoln_Sparrow_0057_117334.jpg train 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+ "Masking Lincoln_Sparrow_0078_117483.jpg valid completed - 9\n", + "Masking Lincoln_Sparrow_0083_117272.jpg valid completed - 10\n", + "Masking Lincoln_Sparrow_0047_117442.jpg valid completed - 11\n", + "Masking Lincoln_Sparrow_0022_117759.jpg valid completed - 12\n", + "Masking Lincoln_Sparrow_0113_117603.jpg test completed - 1\n", + "Masking Lincoln_Sparrow_0094_117762.jpg test completed - 2\n", + "Masking Lincoln_Sparrow_0099_117482.jpg test completed - 3\n", + "Masking Lincoln_Sparrow_0119_117270.jpg test completed - 4\n", + "Masking Lincoln_Sparrow_0084_117492.jpg test completed - 5\n", + "Masking Lincoln_Sparrow_0116_117372.jpg test completed - 6\n", + "Masking Lincoln_Sparrow_0055_117506.jpg test completed - 7\n", + "Masking Lincoln_Sparrow_0110_117264.jpg test completed - 8\n", + "Masking Lincoln_Sparrow_0074_117584.jpg test completed - 9\n", + "Masking Lincoln_Sparrow_0129_117898.jpg test completed - 10\n", + "Masking Lincoln_Sparrow_0112_117631.jpg test completed - 11\n", + "Masking Lincoln_Sparrow_0044_117687.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/126.Nelson_Sharp_tailed_Sparrow\n", + "Train indexes: [57 44 42 3 51 9 50 41 47 55 32 23 30 12 34 54 20 39 40 56 48 7 29 22\n", + " 18 1 4 46 6 5 25 45 0 53 43]\n", + "Validation indexes: [58 10 38 16 33 36 14 49 11 52 8 15]\n", + "Test indexes: [27 37 35 2 17 28 31 24 13 26 19 21]\n", + "Masking Nelson_Sharp_Tailed_Sparrow_0077_796913.jpg train completed - 1\n", + "Masking Nelson_Sharp_Tailed_Sparrow_0058_118023.jpg train completed - 2\n", + "Masking Nelson_Sharp_Tailed_Sparrow_0056_117974.jpg train completed - 3\n", + "Masking Nelson_Sharp_Tailed_Sparrow_0004_796941.jpg train completed - 4\n", + "Masking Nelson_Sharp_Tailed_Sparrow_0068_796930.jpg train completed - 5\n", + "Masking Nelson_Sharp_Tailed_Sparrow_0014_796906.jpg train completed - 6\n", + "Masking Nelson_Sharp_Tailed_Sparrow_0067_796912.jpg train completed - 7\n", + "Masking Nelson_Sharp_Tailed_Sparrow_0055_796937.jpg 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completed - 10\n", + "Masking Nelson_Sharp_Tailed_Sparrow_0013_796942.jpg valid completed - 11\n", + "Masking Nelson_Sharp_Tailed_Sparrow_0022_796905.jpg valid completed - 12\n", + "Masking Nelson_Sharp_Tailed_Sparrow_0037_117986.jpg test completed - 1\n", + "Masking Nelson_Sharp_Tailed_Sparrow_0051_796902.jpg test completed - 2\n", + "Masking Nelson_Sharp_Tailed_Sparrow_0047_117991.jpg test completed - 3\n", + "Masking Nelson_Sharp_Tailed_Sparrow_0003_796917.jpg test completed - 4\n", + "Masking Nelson_Sharp_Tailed_Sparrow_0024_118010.jpg test completed - 5\n", + "Masking Nelson_Sharp_Tailed_Sparrow_0038_796920.jpg test completed - 6\n", + "Masking Nelson_Sharp_Tailed_Sparrow_0042_796911.jpg test completed - 7\n", + "Masking Nelson_Sharp_Tailed_Sparrow_0034_796950.jpg test completed - 8\n", + "Masking Nelson_Sharp_Tailed_Sparrow_0018_118011.jpg test completed - 9\n", + "Masking Nelson_Sharp_Tailed_Sparrow_0036_118075.jpg test completed - 10\n", + "Masking 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valid completed - 1\n", + "Masking Savannah_Sparrow_0032_120109.jpg valid completed - 2\n", + "Masking Savannah_Sparrow_0025_119124.jpg valid completed - 3\n", + "Masking Savannah_Sparrow_0075_116305.jpg valid completed - 4\n", + "Masking Savannah_Sparrow_0062_120185.jpg valid completed - 5\n", + "Masking Savannah_Sparrow_0008_118929.jpg valid completed - 6\n", + "Masking Savannah_Sparrow_0114_119750.jpg valid completed - 7\n", + "Masking Savannah_Sparrow_0084_120063.jpg valid completed - 8\n", + "Masking Savannah_Sparrow_0109_119674.jpg valid completed - 9\n", + "Masking Savannah_Sparrow_0096_120376.jpg valid completed - 10\n", + "Masking Savannah_Sparrow_0001_118956.jpg valid completed - 11\n", + "Masking Savannah_Sparrow_0048_120321.jpg valid completed - 12\n", + "Masking Savannah_Sparrow_0080_118120.jpg test completed - 1\n", + "Masking Savannah_Sparrow_0014_120072.jpg test completed - 2\n", + "Masking Savannah_Sparrow_0116_118108.jpg test completed - 3\n", + "Masking Savannah_Sparrow_0028_119982.jpg test completed - 4\n", + "Masking Savannah_Sparrow_0054_120057.jpg test completed - 5\n", + "Masking Savannah_Sparrow_0009_120205.jpg test completed - 6\n", + "Masking Savannah_Sparrow_0033_118871.jpg test completed - 7\n", + "Masking Savannah_Sparrow_0139_119444.jpg test completed - 8\n", + "Masking Savannah_Sparrow_0017_119171.jpg test completed - 9\n", + "Masking Savannah_Sparrow_0051_118574.jpg test completed - 10\n", + "Masking Savannah_Sparrow_0061_119783.jpg test completed - 11\n", + "Masking Savannah_Sparrow_0058_118323.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/128.Seaside_Sparrow\n", + "Train indexes: [25 43 32 38 58 45 29 54 30 51 21 49 3 28 33 5 12 53 18 22 24 46 15 56\n", + " 48 35 2 57 23 1 0 40 26 34 37 50]\n", + "Validation indexes: [ 7 16 10 13 14 42 47 52 8 55 11 39]\n", + "Test indexes: [ 6 31 19 4 9 44 59 36 17 27 20 41]\n", + "Masking Seaside_Sparrow_0031_796526.jpg train completed - 1\n", + "Masking 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+ "Masking Seaside_Sparrow_0018_120716.jpg train completed - 17\n", + "Masking Seaside_Sparrow_0065_796504.jpg train completed - 18\n", + "Masking Seaside_Sparrow_0024_120751.jpg train completed - 19\n", + "Masking Seaside_Sparrow_0028_120766.jpg train completed - 20\n", + "Masking Seaside_Sparrow_0030_120780.jpg train completed - 21\n", + "Masking Seaside_Sparrow_0054_796507.jpg train completed - 22\n", + "Masking Seaside_Sparrow_0021_120699.jpg train completed - 23\n", + "Masking Seaside_Sparrow_0068_796515.jpg train completed - 24\n", + "Masking Seaside_Sparrow_0058_120744.jpg train completed - 25\n", + "Masking Seaside_Sparrow_0042_796528.jpg train completed - 26\n", + "Masking Seaside_Sparrow_0003_796539.jpg train completed - 27\n", + "Masking Seaside_Sparrow_0069_120700.jpg train completed - 28\n", + "Masking Seaside_Sparrow_0029_120828.jpg train completed - 29\n", + "Masking Seaside_Sparrow_0002_796536.jpg train completed - 30\n", + "Masking Seaside_Sparrow_0001_120720.jpg train completed - 31\n", + "Masking Seaside_Sparrow_0047_796520.jpg train completed - 32\n", + "Masking Seaside_Sparrow_0032_120767.jpg train completed - 33\n", + "Masking Seaside_Sparrow_0041_796529.jpg train completed - 34\n", + "Masking Seaside_Sparrow_0044_119287.jpg train completed - 35\n", + "Masking Seaside_Sparrow_0062_796503.jpg train completed - 36\n", + "Masking Seaside_Sparrow_0011_120820.jpg valid completed - 1\n", + "Masking Seaside_Sparrow_0022_120721.jpg valid completed - 2\n", + "Masking Seaside_Sparrow_0015_796500.jpg valid completed - 3\n", + "Masking Seaside_Sparrow_0019_120826.jpg valid completed - 4\n", + "Masking Seaside_Sparrow_0020_120743.jpg valid completed - 5\n", + "Masking Seaside_Sparrow_0049_120735.jpg valid completed - 6\n", + "Masking Seaside_Sparrow_0056_120710.jpg valid completed - 7\n", + "Masking Seaside_Sparrow_0064_120813.jpg valid completed - 8\n", + "Masking Seaside_Sparrow_0012_120732.jpg valid completed - 9\n", + "Masking Seaside_Sparrow_0067_796532.jpg valid completed - 10\n", + "Masking Seaside_Sparrow_0017_796513.jpg valid completed - 11\n", + "Masking Seaside_Sparrow_0046_120768.jpg valid completed - 12\n", + "Masking Seaside_Sparrow_0008_796538.jpg test completed - 1\n", + "Masking Seaside_Sparrow_0037_796531.jpg test completed - 2\n", + "Masking Seaside_Sparrow_0025_796518.jpg test completed - 3\n", + "Masking Seaside_Sparrow_0005_796516.jpg test completed - 4\n", + "Masking Seaside_Sparrow_0013_796535.jpg test completed - 5\n", + "Masking Seaside_Sparrow_0051_796522.jpg test completed - 6\n", + "Masking Seaside_Sparrow_0071_796519.jpg test completed - 7\n", + "Masking Seaside_Sparrow_0043_796510.jpg test completed - 8\n", + "Masking Seaside_Sparrow_0023_796525.jpg test completed - 9\n", + "Masking Seaside_Sparrow_0033_796506.jpg test completed - 10\n", + "Masking Seaside_Sparrow_0026_120798.jpg test completed - 11\n", + "Masking Seaside_Sparrow_0048_120758.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/129.Song_Sparrow\n", + "Train indexes: [ 4 0 17 9 21 50 7 53 49 54 39 23 12 44 31 25 1 48 3 51 40 41 33 26\n", + " 29 58 55 16 46 28 52 47 43 2 6 19]\n", + "Validation indexes: [18 14 32 57 38 11 27 30 34 20 24 5]\n", + "Test indexes: [45 15 37 10 13 36 56 35 59 8 42 22]\n", + "Masking Song_Sparrow_0010_121331.jpg train completed - 1\n", + "Masking Song_Sparrow_0001_122169.jpg train completed - 2\n", + "Masking Song_Sparrow_0045_121951.jpg train completed - 3\n", + "Masking Song_Sparrow_0034_121255.jpg train completed - 4\n", + "Masking Song_Sparrow_0055_121158.jpg train completed - 5\n", + "Masking Song_Sparrow_0114_121233.jpg train completed - 6\n", + "Masking Song_Sparrow_0020_121490.jpg train completed - 7\n", + "Masking Song_Sparrow_0121_121203.jpg train completed - 8\n", + "Masking Song_Sparrow_0113_121683.jpg train completed - 9\n", + "Masking Song_Sparrow_0123_121249.jpg train completed - 10\n", + "Masking Song_Sparrow_0096_121313.jpg train completed - 11\n", + "Masking Song_Sparrow_0058_121832.jpg train completed - 12\n", + "Masking Song_Sparrow_0037_121078.jpg train completed - 13\n", + "Masking Song_Sparrow_0106_121154.jpg train completed - 14\n", + "Masking Song_Sparrow_0080_121033.jpg train completed - 15\n", + "Masking Song_Sparrow_0060_122371.jpg train completed - 16\n", + "Masking Song_Sparrow_0003_122425.jpg train completed - 17\n", + "Masking Song_Sparrow_0112_121027.jpg train completed - 18\n", + "Masking Song_Sparrow_0009_121025.jpg train completed - 19\n", + "Masking Song_Sparrow_0116_121211.jpg train completed - 20\n", + "Masking Song_Sparrow_0097_121438.jpg train completed - 21\n", + "Masking Song_Sparrow_0099_121193.jpg train completed - 22\n", + "Masking Song_Sparrow_0087_121062.jpg train completed - 23\n", + "Masking Song_Sparrow_0061_120891.jpg train completed - 24\n", + "Masking Song_Sparrow_0071_122446.jpg train completed - 25\n", + "Masking Song_Sparrow_0132_121153.jpg train completed - 26\n", + "Masking Song_Sparrow_0125_122435.jpg train completed - 27\n", + "Masking Song_Sparrow_0044_121931.jpg train completed - 28\n", + "Masking Song_Sparrow_0108_121327.jpg train completed - 29\n", + "Masking Song_Sparrow_0069_122065.jpg train completed - 30\n", + "Masking Song_Sparrow_0118_121905.jpg train completed - 31\n", + "Masking Song_Sparrow_0110_120872.jpg train completed - 32\n", + "Masking Song_Sparrow_0102_122090.jpg train completed - 33\n", + "Masking Song_Sparrow_0007_121133.jpg train completed - 34\n", + "Masking Song_Sparrow_0015_122081.jpg train completed - 35\n", + "Masking Song_Sparrow_0050_121514.jpg train completed - 36\n", + "Masking Song_Sparrow_0046_121903.jpg valid completed - 1\n", + "Masking Song_Sparrow_0040_121617.jpg valid completed - 2\n", + "Masking Song_Sparrow_0086_121999.jpg valid completed - 3\n", + "Masking Song_Sparrow_0130_121583.jpg valid completed - 4\n", + "Masking Song_Sparrow_0092_121969.jpg valid completed - 5\n", + "Masking Song_Sparrow_0036_121679.jpg valid completed - 6\n", + "Masking Song_Sparrow_0067_122424.jpg valid completed - 7\n", + "Masking Song_Sparrow_0077_121196.jpg valid completed - 8\n", + "Masking Song_Sparrow_0088_121615.jpg valid completed - 9\n", + "Masking Song_Sparrow_0053_121554.jpg valid completed - 10\n", + "Masking Song_Sparrow_0059_120885.jpg valid completed - 11\n", + "Masking Song_Sparrow_0013_121056.jpg valid completed - 12\n", + "Masking Song_Sparrow_0107_120990.jpg test completed - 1\n", + "Masking Song_Sparrow_0042_121314.jpg test completed - 2\n", + "Masking Song_Sparrow_0091_121651.jpg test completed - 3\n", + "Masking Song_Sparrow_0035_120986.jpg test completed - 4\n", + "Masking Song_Sparrow_0038_121666.jpg test completed - 5\n", + "Masking Song_Sparrow_0090_121057.jpg test completed - 6\n", + "Masking Song_Sparrow_0126_120901.jpg test completed - 7\n", + "Masking Song_Sparrow_0089_120894.jpg test completed - 8\n", + "Masking Song_Sparrow_0135_121261.jpg test completed - 9\n", + "Masking Song_Sparrow_0029_120989.jpg test completed - 10\n", + "Masking Song_Sparrow_0101_120920.jpg test completed - 11\n", + "Masking Song_Sparrow_0057_121090.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/130.Tree_Sparrow\n", + "Train indexes: [51 55 10 46 20 53 33 28 19 36 22 59 41 40 0 45 12 35 58 42 7 13 1 31\n", + " 44 21 9 25 30 39 26 18 57 34 8 11]\n", + "Validation indexes: [37 50 16 23 6 56 43 3 32 2 54 17]\n", + "Test indexes: [49 27 52 14 29 47 48 15 5 24 4 38]\n", + "Masking Tree_Sparrow_0119_124114.jpg train completed - 1\n", + "Masking Tree_Sparrow_0125_123078.jpg train completed - 2\n", + "Masking Tree_Sparrow_0028_122829.jpg train completed - 3\n", + "Masking Tree_Sparrow_0107_123822.jpg train completed - 4\n", + "Masking Tree_Sparrow_0044_122946.jpg train completed - 5\n", + "Masking Tree_Sparrow_0122_123927.jpg train completed - 6\n", + "Masking Tree_Sparrow_0076_123669.jpg train completed - 7\n", + "Masking Tree_Sparrow_0066_123569.jpg train completed - 8\n", + "Masking Tree_Sparrow_0043_124034.jpg train completed - 9\n", + "Masking Tree_Sparrow_0081_124348.jpg train completed - 10\n", + "Masking Tree_Sparrow_0052_123869.jpg train completed - 11\n", + "Masking Tree_Sparrow_0130_124932.jpg train completed - 12\n", + "Masking Tree_Sparrow_0095_124090.jpg train completed - 13\n", + "Masking Tree_Sparrow_0094_124974.jpg train completed - 14\n", + "Masking Tree_Sparrow_0005_122949.jpg train completed - 15\n", + "Masking Tree_Sparrow_0105_123227.jpg train completed - 16\n", + "Masking Tree_Sparrow_0032_123489.jpg train completed - 17\n", + "Masking Tree_Sparrow_0080_124120.jpg train completed - 18\n", + "Masking Tree_Sparrow_0129_124960.jpg train completed - 19\n", + "Masking Tree_Sparrow_0097_123462.jpg train completed - 20\n", + "Masking Tree_Sparrow_0023_124956.jpg train completed - 21\n", + "Masking Tree_Sparrow_0034_123799.jpg train completed - 22\n", + "Masking Tree_Sparrow_0007_122911.jpg train completed - 23\n", + "Masking Tree_Sparrow_0073_123871.jpg train completed - 24\n", + "Masking Tree_Sparrow_0101_124104.jpg train completed - 25\n", + "Masking Tree_Sparrow_0046_125344.jpg train completed - 26\n", + "Masking Tree_Sparrow_0025_124233.jpg train completed - 27\n", + "Masking Tree_Sparrow_0058_123591.jpg train completed - 28\n", + "Masking Tree_Sparrow_0072_123991.jpg train completed - 29\n", + "Masking Tree_Sparrow_0090_110145.jpg train completed - 30\n", + "Masking Tree_Sparrow_0060_123743.jpg train completed - 31\n", + "Masking Tree_Sparrow_0042_124512.jpg train completed - 32\n", + "Masking Tree_Sparrow_0128_123979.jpg train completed - 33\n", + "Masking Tree_Sparrow_0077_123417.jpg train completed - 34\n", + "Masking Tree_Sparrow_0024_123805.jpg train completed - 35\n", + "Masking Tree_Sparrow_0030_122850.jpg train completed - 36\n", + "Masking Tree_Sparrow_0086_123751.jpg valid completed - 1\n", + "Masking Tree_Sparrow_0113_123613.jpg valid completed - 2\n", + "Masking Tree_Sparrow_0039_124140.jpg valid completed - 3\n", + "Masking Tree_Sparrow_0053_122933.jpg valid completed - 4\n", + "Masking Tree_Sparrow_0022_123496.jpg valid completed - 5\n", + "Masking Tree_Sparrow_0127_125322.jpg valid completed - 6\n", + "Masking Tree_Sparrow_0100_122945.jpg valid completed - 7\n", + "Masking Tree_Sparrow_0011_122964.jpg valid completed - 8\n", + "Masking Tree_Sparrow_0074_124408.jpg valid completed - 9\n", + "Masking Tree_Sparrow_0009_123294.jpg valid completed - 10\n", + "Masking Tree_Sparrow_0123_125324.jpg valid completed - 11\n", + "Masking Tree_Sparrow_0041_123497.jpg valid completed - 12\n", + "Masking Tree_Sparrow_0112_125014.jpg test completed - 1\n", + "Masking Tree_Sparrow_0062_123000.jpg test completed - 2\n", + "Masking Tree_Sparrow_0121_124296.jpg test completed - 3\n", + "Masking Tree_Sparrow_0035_123211.jpg test completed - 4\n", + "Masking Tree_Sparrow_0068_125230.jpg test completed - 5\n", + "Masking Tree_Sparrow_0108_124754.jpg test completed - 6\n", + "Masking Tree_Sparrow_0109_123802.jpg test completed - 7\n", + "Masking Tree_Sparrow_0036_122772.jpg test completed - 8\n", + "Masking Tree_Sparrow_0018_123574.jpg test completed - 9\n", + "Masking Tree_Sparrow_0057_123665.jpg test completed - 10\n", + "Masking Tree_Sparrow_0015_125105.jpg test completed - 11\n", + "Masking Tree_Sparrow_0088_125305.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/131.Vesper_Sparrow\n", + "Train indexes: [32 21 50 29 10 8 11 39 54 59 14 1 47 38 16 5 51 13 28 18 27 23 52 48\n", + " 6 7 30 53 57 42 25 36 3 46 4 20]\n", + "Validation indexes: [56 9 40 49 33 44 22 17 41 12 58 35]\n", + "Test indexes: [43 0 24 19 45 37 26 34 31 15 2 55]\n", + "Masking Vesper_Sparrow_0052_125444.jpg train completed - 1\n", + "Masking Vesper_Sparrow_0033_125568.jpg train completed - 2\n", + "Masking Vesper_Sparrow_0082_125503.jpg train completed - 3\n", + "Masking Vesper_Sparrow_0047_125788.jpg train completed - 4\n", + "Masking 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"Masking Vesper_Sparrow_0007_125630.jpg train completed - 35\n", + "Masking Vesper_Sparrow_0032_125564.jpg train completed - 36\n", + "Masking Vesper_Sparrow_0090_125690.jpg valid completed - 1\n", + "Masking Vesper_Sparrow_0016_125615.jpg valid completed - 2\n", + "Masking Vesper_Sparrow_0066_125619.jpg valid completed - 3\n", + "Masking Vesper_Sparrow_0081_125541.jpg valid completed - 4\n", + "Masking Vesper_Sparrow_0053_125641.jpg valid completed - 5\n", + "Masking Vesper_Sparrow_0073_125605.jpg valid completed - 6\n", + "Masking Vesper_Sparrow_0037_125648.jpg valid completed - 7\n", + "Masking Vesper_Sparrow_0029_125498.jpg valid completed - 8\n", + "Masking Vesper_Sparrow_0067_125429.jpg valid completed - 9\n", + "Masking Vesper_Sparrow_0019_125558.jpg valid completed - 10\n", + "Masking Vesper_Sparrow_0094_125602.jpg valid completed - 11\n", + "Masking Vesper_Sparrow_0056_125657.jpg valid completed - 12\n", + "Masking Vesper_Sparrow_0070_125555.jpg test completed - 1\n", + "Masking Vesper_Sparrow_0002_125454.jpg test completed - 2\n", + "Masking Vesper_Sparrow_0041_125458.jpg test completed - 3\n", + "Masking Vesper_Sparrow_0031_125631.jpg test completed - 4\n", + "Masking Vesper_Sparrow_0076_125737.jpg test completed - 5\n", + "Masking Vesper_Sparrow_0058_125661.jpg test completed - 6\n", + "Masking Vesper_Sparrow_0044_125470.jpg test completed - 7\n", + "Masking Vesper_Sparrow_0055_125611.jpg test completed - 8\n", + "Masking Vesper_Sparrow_0051_125587.jpg test completed - 9\n", + "Masking Vesper_Sparrow_0023_125465.jpg test completed - 10\n", + "Masking Vesper_Sparrow_0004_125787.jpg test completed - 11\n", + "Masking Vesper_Sparrow_0089_125705.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/132.White_crowned_Sparrow\n", + "Train indexes: [21 9 22 30 4 13 43 1 6 25 27 44 3 52 35 56 23 40 5 59 10 55 50 57\n", + " 8 32 46 11 29 58 19 37 51 36 47 2]\n", + "Validation indexes: [31 14 24 39 16 15 49 28 38 18 48 42]\n", + "Test indexes: [54 26 34 7 33 53 45 0 17 41 12 20]\n", + "Masking White_Crowned_Sparrow_0061_126315.jpg train completed - 1\n", + "Masking White_Crowned_Sparrow_0031_126457.jpg train completed - 2\n", + "Masking White_Crowned_Sparrow_0064_126467.jpg train completed - 3\n", + "Masking White_Crowned_Sparrow_0085_127194.jpg train completed - 4\n", + "Masking White_Crowned_Sparrow_0012_127853.jpg train completed - 5\n", + "Masking White_Crowned_Sparrow_0040_127313.jpg train completed - 6\n", + "Masking White_Crowned_Sparrow_0105_126818.jpg train completed - 7\n", + "Masking White_Crowned_Sparrow_0002_127774.jpg train completed - 8\n", + "Masking White_Crowned_Sparrow_0019_127652.jpg train completed - 9\n", + "Masking White_Crowned_Sparrow_0071_127922.jpg train completed - 10\n", + "Masking White_Crowned_Sparrow_0076_127604.jpg train completed - 11\n", + "Masking White_Crowned_Sparrow_0107_128662.jpg train completed - 12\n", + "Masking White_Crowned_Sparrow_0010_127651.jpg train completed - 13\n", + 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White_Crowned_Sparrow_0122_128577.jpg test completed - 6\n", + "Masking White_Crowned_Sparrow_0109_128529.jpg test completed - 7\n", + "Masking White_Crowned_Sparrow_0001_127115.jpg test completed - 8\n", + "Masking White_Crowned_Sparrow_0047_127575.jpg test completed - 9\n", + "Masking White_Crowned_Sparrow_0101_127058.jpg test completed - 10\n", + "Masking White_Crowned_Sparrow_0037_126969.jpg test completed - 11\n", + "Masking White_Crowned_Sparrow_0057_127927.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/133.White_throated_Sparrow\n", + "Train indexes: [16 8 57 42 17 7 36 25 13 24 20 41 50 37 43 52 26 53 39 54 9 23 15 55\n", + " 27 28 30 49 45 59 33 4 44 19 1 31]\n", + "Validation indexes: [21 51 0 34 6 22 48 58 18 47 5 29]\n", + "Test indexes: [56 14 10 40 46 32 3 2 12 35 38 11]\n", + "Masking White_Throated_Sparrow_0033_129016.jpg train completed - 1\n", + "Masking White_Throated_Sparrow_0021_128804.jpg train completed - 2\n", + "Masking 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White_Throated_Sparrow_0108_129184.jpg train completed - 29\n", + "Masking White_Throated_Sparrow_0135_128819.jpg train completed - 30\n", + "Masking White_Throated_Sparrow_0073_129110.jpg train completed - 31\n", + "Masking White_Throated_Sparrow_0015_129138.jpg train completed - 32\n", + "Masking White_Throated_Sparrow_0107_129046.jpg train completed - 33\n", + "Masking White_Throated_Sparrow_0039_128859.jpg train completed - 34\n", + "Masking White_Throated_Sparrow_0004_128944.jpg train completed - 35\n", + "Masking White_Throated_Sparrow_0071_128915.jpg train completed - 36\n", + "Masking White_Throated_Sparrow_0043_128818.jpg valid completed - 1\n", + "Masking White_Throated_Sparrow_0119_128827.jpg valid completed - 2\n", + "Masking White_Throated_Sparrow_0002_129057.jpg valid completed - 3\n", + "Masking White_Throated_Sparrow_0075_128990.jpg valid completed - 4\n", + "Masking White_Throated_Sparrow_0017_128982.jpg valid completed - 5\n", + "Masking White_Throated_Sparrow_0047_129014.jpg valid completed - 6\n", + "Masking White_Throated_Sparrow_0112_129101.jpg valid completed - 7\n", + "Masking White_Throated_Sparrow_0134_129190.jpg valid completed - 8\n", + "Masking White_Throated_Sparrow_0038_128853.jpg valid completed - 9\n", + "Masking White_Throated_Sparrow_0110_128838.jpg valid completed - 10\n", + "Masking White_Throated_Sparrow_0016_129158.jpg valid completed - 11\n", + "Masking White_Throated_Sparrow_0063_128803.jpg valid completed - 12\n", + "Masking White_Throated_Sparrow_0128_128956.jpg test completed - 1\n", + "Masking White_Throated_Sparrow_0031_128808.jpg test completed - 2\n", + "Masking White_Throated_Sparrow_0026_128843.jpg test completed - 3\n", + "Masking White_Throated_Sparrow_0098_129089.jpg test completed - 4\n", + "Masking White_Throated_Sparrow_0109_129066.jpg test completed - 5\n", + "Masking White_Throated_Sparrow_0072_129203.jpg test completed - 6\n", + "Masking White_Throated_Sparrow_0014_129148.jpg test completed - 7\n", + "Masking White_Throated_Sparrow_0007_128918.jpg test completed - 8\n", + "Masking White_Throated_Sparrow_0028_129118.jpg test completed - 9\n", + "Masking White_Throated_Sparrow_0076_129088.jpg test completed - 10\n", + "Masking White_Throated_Sparrow_0085_129180.jpg test completed - 11\n", + "Masking White_Throated_Sparrow_0027_128847.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/134.Cape_Glossy_Starling\n", + "Train indexes: [ 9 48 14 12 2 59 42 3 18 44 22 55 20 28 16 51 53 26 21 35 4 1 52 49\n", + " 39 10 29 27 38 11 50 34 46 15 56 6]\n", + "Validation indexes: [ 8 57 31 0 43 32 58 17 25 45 33 30]\n", + "Test indexes: [54 23 36 41 13 37 24 47 5 40 19 7]\n", + "Masking Cape_Glossy_Starling_0017_129337.jpg train completed - 1\n", + "Masking Cape_Glossy_Starling_0081_129220.jpg train completed - 2\n", + "Masking Cape_Glossy_Starling_0024_129384.jpg train completed - 3\n", + "Masking Cape_Glossy_Starling_0020_129328.jpg train completed - 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9\n", + "Masking Cape_Glossy_Starling_0077_129378.jpg valid completed - 10\n", + "Masking Cape_Glossy_Starling_0060_129222.jpg valid completed - 11\n", + "Masking Cape_Glossy_Starling_0053_129219.jpg valid completed - 12\n", + "Masking Cape_Glossy_Starling_0092_129349.jpg test completed - 1\n", + "Masking Cape_Glossy_Starling_0039_129469.jpg test completed - 2\n", + "Masking Cape_Glossy_Starling_0067_129380.jpg test completed - 3\n", + "Masking Cape_Glossy_Starling_0073_129465.jpg test completed - 4\n", + "Masking Cape_Glossy_Starling_0022_129414.jpg test completed - 5\n", + "Masking Cape_Glossy_Starling_0068_129446.jpg test completed - 6\n", + "Masking Cape_Glossy_Starling_0042_129385.jpg test completed - 7\n", + "Masking Cape_Glossy_Starling_0080_129362.jpg test completed - 8\n", + "Masking Cape_Glossy_Starling_0012_129225.jpg test completed - 9\n", + "Masking Cape_Glossy_Starling_0072_129386.jpg test completed - 10\n", + "Masking Cape_Glossy_Starling_0030_129443.jpg test completed - 11\n", + "Masking Cape_Glossy_Starling_0015_129266.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/135.Bank_Swallow\n", + "Train indexes: [ 7 20 53 48 13 21 6 24 46 0 10 14 25 11 37 12 28 17 58 38 36 35 39 43\n", + " 49 42 54 31 2 5 55 3 8 51 30]\n", + "Validation indexes: [26 56 34 29 33 1 57 32 22 40 18 50]\n", + "Test indexes: [23 4 15 41 19 52 9 44 16 27 45 47]\n", + "Masking Bank_Swallow_0010_129592.jpg train completed - 1\n", + "Masking Bank_Swallow_0025_129521.jpg train completed - 2\n", + "Masking Bank_Swallow_0063_129774.jpg train completed - 3\n", + "Masking Bank_Swallow_0057_129499.jpg train completed - 4\n", + "Masking Bank_Swallow_0017_129755.jpg train completed - 5\n", + "Masking Bank_Swallow_0026_129870.jpg train completed - 6\n", + "Masking Bank_Swallow_0008_129590.jpg train completed - 7\n", + "Masking Bank_Swallow_0029_129751.jpg train completed - 8\n", + "Masking Bank_Swallow_0053_129501.jpg train completed - 9\n", + "Masking 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Bank_Swallow_0058_129756.jpg train completed - 25\n", + "Masking Bank_Swallow_0048_129546.jpg train completed - 26\n", + "Masking Bank_Swallow_0064_129816.jpg train completed - 27\n", + "Masking Bank_Swallow_0036_129567.jpg train completed - 28\n", + "Masking Bank_Swallow_0003_129623.jpg train completed - 29\n", + "Masking Bank_Swallow_0007_129514.jpg train completed - 30\n", + "Masking Bank_Swallow_0066_129559.jpg train completed - 31\n", + "Masking Bank_Swallow_0004_129549.jpg train completed - 32\n", + "Masking Bank_Swallow_0012_129518.jpg train completed - 33\n", + "Masking Bank_Swallow_0061_129484.jpg train completed - 34\n", + "Masking Bank_Swallow_0035_129528.jpg train completed - 35\n", + "Masking Bank_Swallow_0031_129507.jpg valid completed - 1\n", + "Masking Bank_Swallow_0067_129959.jpg valid completed - 2\n", + "Masking Bank_Swallow_0039_129599.jpg valid completed - 3\n", + "Masking Bank_Swallow_0034_129496.jpg valid completed - 4\n", + "Masking Bank_Swallow_0038_129830.jpg valid completed - 5\n", + "Masking Bank_Swallow_0002_129654.jpg valid completed - 6\n", + "Masking Bank_Swallow_0068_129806.jpg valid completed - 7\n", + "Masking Bank_Swallow_0037_129903.jpg valid completed - 8\n", + "Masking Bank_Swallow_0027_129503.jpg valid completed - 9\n", + "Masking Bank_Swallow_0046_129742.jpg valid completed - 10\n", + "Masking Bank_Swallow_0023_129878.jpg valid completed - 11\n", + "Masking Bank_Swallow_0059_129896.jpg valid completed - 12\n", + "Masking Bank_Swallow_0028_129754.jpg test completed - 1\n", + "Masking Bank_Swallow_0006_129652.jpg test completed - 2\n", + "Masking Bank_Swallow_0019_129788.jpg test completed - 3\n", + "Masking Bank_Swallow_0047_129520.jpg test completed - 4\n", + "Masking Bank_Swallow_0024_129779.jpg test completed - 5\n", + "Masking Bank_Swallow_0062_129548.jpg test completed - 6\n", + "Masking Bank_Swallow_0013_129563.jpg test completed - 7\n", + "Masking Bank_Swallow_0050_129780.jpg test completed - 8\n", + "Masking Bank_Swallow_0020_129747.jpg test completed - 9\n", + "Masking Bank_Swallow_0032_129491.jpg test completed - 10\n", + "Masking Bank_Swallow_0052_129575.jpg test completed - 11\n", + "Masking Bank_Swallow_0054_129743.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/136.Barn_Swallow\n", + "Train indexes: [ 6 54 41 45 33 19 32 27 56 3 0 12 17 18 46 52 55 40 44 2 49 59 30 37\n", + " 11 4 58 43 36 10 42 51 23 15 25 28]\n", + "Validation indexes: [29 9 14 13 35 39 8 53 21 50 22 31]\n", + "Test indexes: [ 1 57 47 7 26 34 16 24 38 20 5 48]\n", + "Masking Barn_Swallow_0014_130403.jpg train completed - 1\n", + "Masking Barn_Swallow_0084_130800.jpg train completed - 2\n", + "Masking Barn_Swallow_0065_131555.jpg train completed - 3\n", + "Masking Barn_Swallow_0072_130474.jpg train completed - 4\n", + "Masking Barn_Swallow_0053_130585.jpg train completed - 5\n", + "Masking Barn_Swallow_0030_130191.jpg train completed - 6\n", + "Masking Barn_Swallow_0052_131539.jpg train completed 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Barn_Swallow_0017_132951.jpg valid completed - 2\n", + "Masking Barn_Swallow_0022_130631.jpg valid completed - 3\n", + "Masking Barn_Swallow_0021_130367.jpg valid completed - 4\n", + "Masking Barn_Swallow_0055_131933.jpg valid completed - 5\n", + "Masking Barn_Swallow_0063_132572.jpg valid completed - 6\n", + "Masking Barn_Swallow_0016_130678.jpg valid completed - 7\n", + "Masking Barn_Swallow_0083_132949.jpg valid completed - 8\n", + "Masking Barn_Swallow_0035_131832.jpg valid completed - 9\n", + "Masking Barn_Swallow_0077_130707.jpg valid completed - 10\n", + "Masking Barn_Swallow_0038_132780.jpg valid completed - 11\n", + "Masking Barn_Swallow_0050_130095.jpg valid completed - 12\n", + "Masking Barn_Swallow_0003_130086.jpg test completed - 1\n", + "Masking Barn_Swallow_0089_131934.jpg test completed - 2\n", + "Masking Barn_Swallow_0074_132094.jpg test completed - 3\n", + "Masking Barn_Swallow_0015_132757.jpg test completed - 4\n", + "Masking Barn_Swallow_0044_132542.jpg test completed - 5\n", + "Masking Barn_Swallow_0054_131146.jpg test completed - 6\n", + "Masking Barn_Swallow_0026_132386.jpg test completed - 7\n", + "Masking Barn_Swallow_0042_132043.jpg test completed - 8\n", + "Masking Barn_Swallow_0062_132755.jpg test completed - 9\n", + "Masking Barn_Swallow_0034_130099.jpg test completed - 10\n", + "Masking Barn_Swallow_0013_131812.jpg test completed - 11\n", + "Masking Barn_Swallow_0075_130014.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/137.Cliff_Swallow\n", + "Train indexes: [30 39 23 46 15 56 18 11 5 1 36 8 45 34 32 27 49 0 2 29 9 19 48 42\n", + " 20 58 40 12 35 26 10 6 4 53 28 13]\n", + "Validation indexes: [17 16 43 41 37 55 21 50 47 33 31 14]\n", + "Test indexes: [ 7 51 59 54 57 3 25 38 24 52 44 22]\n", + "Masking Cliff_Swallow_0056_133921.jpg train completed - 1\n", + "Masking Cliff_Swallow_0069_133521.jpg train completed - 2\n", + "Masking Cliff_Swallow_0046_133165.jpg train completed - 3\n", + "Masking 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Cliff_Swallow_0006_133489.jpg train completed - 19\n", + "Masking Cliff_Swallow_0055_133624.jpg train completed - 20\n", + "Masking Cliff_Swallow_0022_133786.jpg train completed - 21\n", + "Masking Cliff_Swallow_0041_134111.jpg train completed - 22\n", + "Masking Cliff_Swallow_0088_134188.jpg train completed - 23\n", + "Masking Cliff_Swallow_0075_134516.jpg train completed - 24\n", + "Masking Cliff_Swallow_0042_133646.jpg train completed - 25\n", + "Masking Cliff_Swallow_0103_133882.jpg train completed - 26\n", + "Masking Cliff_Swallow_0071_133742.jpg train completed - 27\n", + "Masking Cliff_Swallow_0028_132365.jpg train completed - 28\n", + "Masking Cliff_Swallow_0063_133852.jpg train completed - 29\n", + "Masking Cliff_Swallow_0050_134054.jpg train completed - 30\n", + "Masking Cliff_Swallow_0023_134314.jpg train completed - 31\n", + "Masking Cliff_Swallow_0015_133176.jpg train completed - 32\n", + "Masking Cliff_Swallow_0012_133527.jpg train completed - 33\n", + "Masking Cliff_Swallow_0094_133114.jpg train completed - 34\n", + "Masking Cliff_Swallow_0054_133287.jpg train completed - 35\n", + "Masking Cliff_Swallow_0031_133164.jpg train completed - 36\n", + "Masking Cliff_Swallow_0038_133701.jpg valid completed - 1\n", + "Masking Cliff_Swallow_0036_133168.jpg valid completed - 2\n", + "Masking Cliff_Swallow_0081_134119.jpg valid completed - 3\n", + "Masking Cliff_Swallow_0073_133116.jpg valid completed - 4\n", + "Masking Cliff_Swallow_0066_133206.jpg valid completed - 5\n", + "Masking Cliff_Swallow_0099_133948.jpg valid completed - 6\n", + "Masking Cliff_Swallow_0044_133927.jpg valid completed - 7\n", + "Masking Cliff_Swallow_0090_133144.jpg valid completed - 8\n", + "Masking Cliff_Swallow_0087_133294.jpg valid completed - 9\n", + "Masking Cliff_Swallow_0061_133259.jpg valid completed - 10\n", + "Masking Cliff_Swallow_0058_133060.jpg valid completed - 11\n", + "Masking Cliff_Swallow_0033_133836.jpg valid completed - 12\n", + "Masking Cliff_Swallow_0017_133806.jpg test completed - 1\n", + "Masking Cliff_Swallow_0092_132971.jpg test completed - 2\n", + "Masking Cliff_Swallow_0108_133902.jpg test completed - 3\n", + "Masking Cliff_Swallow_0098_133130.jpg test completed - 4\n", + "Masking Cliff_Swallow_0101_133069.jpg test completed - 5\n", + "Masking Cliff_Swallow_0011_133033.jpg test completed - 6\n", + "Masking Cliff_Swallow_0049_132965.jpg test completed - 7\n", + "Masking Cliff_Swallow_0068_134236.jpg test completed - 8\n", + "Masking Cliff_Swallow_0047_133955.jpg test completed - 9\n", + "Masking Cliff_Swallow_0093_133052.jpg test completed - 10\n", + "Masking Cliff_Swallow_0083_133771.jpg test completed - 11\n", + "Masking Cliff_Swallow_0045_133591.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/138.Tree_Swallow\n", + "Train indexes: [13 57 37 18 10 25 35 9 53 40 34 42 26 59 0 32 47 21 52 7 45 27 41 17\n", + " 38 39 29 55 46 50 12 58 36 8 22 11]\n", + "Validation indexes: [31 2 15 19 54 49 5 24 3 33 23 1]\n", + "Test indexes: [43 51 6 48 20 28 4 14 30 56 16 44]\n", + "Masking Tree_Swallow_0035_136589.jpg train completed - 1\n", + "Masking Tree_Swallow_0115_135832.jpg train completed - 2\n", + "Masking Tree_Swallow_0076_137232.jpg train completed - 3\n", + "Masking Tree_Swallow_0042_136401.jpg train completed - 4\n", + "Masking Tree_Swallow_0029_134882.jpg train completed - 5\n", + "Masking Tree_Swallow_0058_134987.jpg train completed - 6\n", + "Masking Tree_Swallow_0074_135219.jpg train completed - 7\n", + "Masking Tree_Swallow_0023_135345.jpg train completed - 8\n", + "Masking Tree_Swallow_0110_136921.jpg train completed - 9\n", + "Masking Tree_Swallow_0087_137354.jpg train completed - 10\n", + "Masking Tree_Swallow_0073_134997.jpg train completed - 11\n", + "Masking Tree_Swallow_0091_136870.jpg train completed - 12\n", + "Masking Tree_Swallow_0060_134961.jpg train completed - 13\n", + "Masking Tree_Swallow_0127_135912.jpg train completed - 14\n", + "Masking 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valid completed - 9\n", + "Masking Tree_Swallow_0071_136749.jpg valid completed - 10\n", + "Masking Tree_Swallow_0050_135104.jpg valid completed - 11\n", + "Masking Tree_Swallow_0004_135411.jpg valid completed - 12\n", + "Masking Tree_Swallow_0092_136236.jpg test completed - 1\n", + "Masking Tree_Swallow_0108_135068.jpg test completed - 2\n", + "Masking Tree_Swallow_0017_135062.jpg test completed - 3\n", + "Masking Tree_Swallow_0103_137272.jpg test completed - 4\n", + "Masking Tree_Swallow_0044_135984.jpg test completed - 5\n", + "Masking Tree_Swallow_0062_137426.jpg test completed - 6\n", + "Masking Tree_Swallow_0015_134790.jpg test completed - 7\n", + "Masking Tree_Swallow_0037_134647.jpg test completed - 8\n", + "Masking Tree_Swallow_0066_135788.jpg test completed - 9\n", + "Masking Tree_Swallow_0114_136265.jpg test completed - 10\n", + "Masking Tree_Swallow_0040_135172.jpg test completed - 11\n", + "Masking Tree_Swallow_0095_135829.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/139.Scarlet_Tanager\n", + "Train indexes: [10 14 57 24 29 1 48 22 18 41 4 36 21 58 15 6 35 9 59 5 12 7 11 26\n", + " 56 55 53 0 44 42 25 54 37 33 17 50]\n", + "Validation indexes: [45 46 38 43 39 16 34 40 30 31 52 3]\n", + "Test indexes: [47 27 2 19 13 49 51 20 28 23 8 32]\n", + "Masking Scarlet_Tanager_0024_137712.jpg train completed - 1\n", + "Masking Scarlet_Tanager_0039_138010.jpg train completed - 2\n", + "Masking Scarlet_Tanager_0131_138740.jpg train completed - 3\n", + "Masking Scarlet_Tanager_0063_138227.jpg train completed - 4\n", + "Masking Scarlet_Tanager_0076_137702.jpg train completed - 5\n", + "Masking Scarlet_Tanager_0008_137607.jpg train completed - 6\n", + "Masking Scarlet_Tanager_0112_138695.jpg train completed - 7\n", + "Masking Scarlet_Tanager_0058_137710.jpg train completed - 8\n", + "Masking Scarlet_Tanager_0050_137874.jpg train completed - 9\n", + "Masking Scarlet_Tanager_0096_138022.jpg train completed - 10\n", + "Masking 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valid completed - 4\n", + "Masking Scarlet_Tanager_0093_138250.jpg valid completed - 5\n", + "Masking Scarlet_Tanager_0042_138287.jpg valid completed - 6\n", + "Masking Scarlet_Tanager_0086_138272.jpg valid completed - 7\n", + "Masking Scarlet_Tanager_0095_137618.jpg valid completed - 8\n", + "Masking Scarlet_Tanager_0077_137626.jpg valid completed - 9\n", + "Masking Scarlet_Tanager_0079_138669.jpg valid completed - 10\n", + "Masking Scarlet_Tanager_0120_138344.jpg valid completed - 11\n", + "Masking Scarlet_Tanager_0012_138410.jpg valid completed - 12\n", + "Masking Scarlet_Tanager_0110_138274.jpg test completed - 1\n", + "Masking Scarlet_Tanager_0073_138108.jpg test completed - 2\n", + "Masking Scarlet_Tanager_0009_138076.jpg test completed - 3\n", + "Masking Scarlet_Tanager_0053_138940.jpg test completed - 4\n", + "Masking Scarlet_Tanager_0038_138198.jpg test completed - 5\n", + "Masking Scarlet_Tanager_0113_138262.jpg test completed - 6\n", + "Masking Scarlet_Tanager_0119_138291.jpg test completed - 7\n", + "Masking Scarlet_Tanager_0054_138210.jpg test completed - 8\n", + "Masking Scarlet_Tanager_0075_137602.jpg test completed - 9\n", + "Masking Scarlet_Tanager_0060_138384.jpg test completed - 10\n", + "Masking Scarlet_Tanager_0021_138199.jpg test completed - 11\n", + "Masking Scarlet_Tanager_0082_137978.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/140.Summer_Tanager\n", + "Train indexes: [36 6 48 41 12 22 10 16 8 1 34 29 31 57 9 51 20 0 54 17 32 44 14 58\n", + " 52 35 49 40 50 21 4 37 28 13 3 59]\n", + "Validation indexes: [19 25 55 15 53 33 24 27 2 46 30 7]\n", + "Test indexes: [18 39 43 23 11 38 42 56 5 45 26 47]\n", + "Masking Summer_Tanager_0074_139592.jpg train completed - 1\n", + "Masking Summer_Tanager_0018_139290.jpg train completed - 2\n", + "Masking Summer_Tanager_0106_139801.jpg train completed - 3\n", + "Masking Summer_Tanager_0093_139517.jpg train completed - 4\n", + "Masking 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"/kaggle/working/CUB_200_2011/images/144.Common_Tern\n", + "Train indexes: [35 37 53 46 42 8 47 13 52 49 32 20 16 24 22 57 12 36 25 5 14 51 17 11\n", + " 1 54 40 2 3 27 0 26 39 56 15 41]\n", + "Validation indexes: [19 18 9 45 44 43 29 21 50 55 4 48]\n", + "Test indexes: [23 59 30 6 34 31 7 38 33 28 58 10]\n", + "Masking Common_Tern_0077_149196.jpg train completed - 1\n", + "Masking Common_Tern_0079_148844.jpg train completed - 2\n", + "Masking Common_Tern_0108_149672.jpg train completed - 3\n", + "Masking Common_Tern_0095_149960.jpg train completed - 4\n", + "Masking Common_Tern_0087_147945.jpg train completed - 5\n", + "Masking Common_Tern_0028_150117.jpg train completed - 6\n", + "Masking Common_Tern_0097_148874.jpg train completed - 7\n", + "Masking Common_Tern_0040_147973.jpg train completed - 8\n", + "Masking Common_Tern_0106_149345.jpg train completed - 9\n", + "Masking Common_Tern_0100_149541.jpg train completed - 10\n", + "Masking Common_Tern_0071_148796.jpg train completed - 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Forsters_Tern_0012_151558.jpg train completed - 36\n", + "Masking Forsters_Tern_0021_144734.jpg valid completed - 1\n", + "Masking Forsters_Tern_0114_152768.jpg valid completed - 2\n", + "Masking Forsters_Tern_0001_152174.jpg valid completed - 3\n", + "Masking Forsters_Tern_0127_150418.jpg valid completed - 4\n", + "Masking Forsters_Tern_0118_151564.jpg valid completed - 5\n", + "Masking Forsters_Tern_0075_152258.jpg valid completed - 6\n", + "Masking Forsters_Tern_0041_149586.jpg valid completed - 7\n", + "Masking Forsters_Tern_0088_151893.jpg valid completed - 8\n", + "Masking Forsters_Tern_0080_152521.jpg valid completed - 9\n", + "Masking Forsters_Tern_0126_151257.jpg valid completed - 10\n", + "Masking Forsters_Tern_0034_152667.jpg valid completed - 11\n", + "Masking Forsters_Tern_0069_151229.jpg valid completed - 12\n", + "Masking Forsters_Tern_0045_151227.jpg test completed - 1\n", + "Masking Forsters_Tern_0065_152206.jpg test completed - 2\n", + "Masking Forsters_Tern_0007_152110.jpg test completed - 3\n", + "Masking Forsters_Tern_0095_152067.jpg test completed - 4\n", + "Masking Forsters_Tern_0082_151937.jpg test completed - 5\n", + "Masking Forsters_Tern_0116_151688.jpg test completed - 6\n", + "Masking Forsters_Tern_0091_151895.jpg test completed - 7\n", + "Masking Forsters_Tern_0003_151547.jpg test completed - 8\n", + "Masking Forsters_Tern_0064_152047.jpg test completed - 9\n", + "Masking Forsters_Tern_0029_151228.jpg test completed - 10\n", + "Masking Forsters_Tern_0059_151269.jpg test completed - 11\n", + "Masking Forsters_Tern_0066_151478.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/147.Least_Tern\n", + "Train indexes: [11 8 12 32 15 29 57 56 3 19 53 35 44 31 6 58 30 10 13 34 48 41 36 17\n", + " 9 49 40 16 39 7 25 38 21 47 43 5]\n", + "Validation indexes: [55 59 46 26 45 14 20 54 2 42 51 24]\n", + "Test indexes: [22 4 50 23 0 18 37 52 33 28 27 1]\n", + "Masking Least_Tern_0026_153702.jpg train completed - 1\n", + "Masking Least_Tern_0021_153979.jpg train completed - 2\n", + "Masking Least_Tern_0028_153781.jpg train completed - 3\n", + "Masking Least_Tern_0067_154145.jpg train completed - 4\n", + "Masking Least_Tern_0034_153963.jpg train completed - 5\n", + "Masking Least_Tern_0059_153746.jpg train completed - 6\n", + "Masking Least_Tern_0131_153983.jpg train completed - 7\n", + "Masking Least_Tern_0129_153449.jpg train completed - 8\n", + "Masking Least_Tern_0011_153722.jpg train completed - 9\n", + "Masking Least_Tern_0038_153087.jpg train completed - 10\n", + "Masking Least_Tern_0125_153996.jpg train completed - 11\n", + "Masking Least_Tern_0082_154396.jpg train completed - 12\n", + "Masking Least_Tern_0107_154015.jpg train completed - 13\n", + "Masking Least_Tern_0062_153259.jpg train completed - 14\n", + "Masking Least_Tern_0016_153560.jpg train completed - 15\n", + "Masking Least_Tern_0132_154149.jpg train completed - 16\n", + "Masking Least_Tern_0060_153190.jpg train completed - 17\n", + "Masking Least_Tern_0025_153678.jpg train completed - 18\n", + "Masking Least_Tern_0029_154064.jpg train completed - 19\n", + "Masking Least_Tern_0075_153691.jpg train completed - 20\n", + "Masking Least_Tern_0113_153415.jpg train completed - 21\n", + "Masking Least_Tern_0098_153820.jpg train completed - 22\n", + "Masking Least_Tern_0088_152941.jpg train completed - 23\n", + "Masking Least_Tern_0036_153658.jpg train completed - 24\n", + "Masking Least_Tern_0024_153317.jpg train completed - 25\n", + "Masking Least_Tern_0114_153840.jpg train completed - 26\n", + "Masking Least_Tern_0096_153868.jpg train completed - 27\n", + "Masking Least_Tern_0035_152932.jpg train completed - 28\n", + "Masking Least_Tern_0095_154680.jpg train completed - 29\n", + "Masking Least_Tern_0020_153458.jpg train completed - 30\n", + "Masking Least_Tern_0050_153254.jpg train completed - 31\n", + "Masking Least_Tern_0092_153361.jpg train completed - 32\n", + "Masking Least_Tern_0041_153470.jpg train 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completed - 1\n", + "Masking Least_Tern_0012_153871.jpg test completed - 2\n", + "Masking Least_Tern_0116_153715.jpg test completed - 3\n", + "Masking Least_Tern_0046_153006.jpg test completed - 4\n", + "Masking Least_Tern_0006_153921.jpg test completed - 5\n", + "Masking Least_Tern_0037_153637.jpg test completed - 6\n", + "Masking Least_Tern_0089_152912.jpg test completed - 7\n", + "Masking Least_Tern_0122_153012.jpg test completed - 8\n", + "Masking Least_Tern_0068_153738.jpg test completed - 9\n", + "Masking Least_Tern_0058_153747.jpg test completed - 10\n", + "Masking Least_Tern_0056_153965.jpg test completed - 11\n", + "Masking Least_Tern_0007_153661.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/148.Green_tailed_Towhee\n", + "Train indexes: [45 18 23 40 44 59 22 29 25 4 3 12 1 34 24 13 27 17 55 58 52 51 42 31\n", + " 26 7 30 32 16 6 11 46 39 43 20 56]\n", + "Validation indexes: [28 54 48 2 9 33 15 57 8 35 10 0]\n", + "Test indexes: [19 53 50 37 21 36 38 41 49 14 5 47]\n", + "Masking Green_Tailed_Towhee_0079_154764.jpg train completed - 1\n", + "Masking Green_Tailed_Towhee_0036_154875.jpg train completed - 2\n", + "Masking Green_Tailed_Towhee_0044_154934.jpg train completed - 3\n", + "Masking Green_Tailed_Towhee_0071_154870.jpg train completed - 4\n", + "Masking Green_Tailed_Towhee_0077_797443.jpg train completed - 5\n", + "Masking Green_Tailed_Towhee_0110_154868.jpg train completed - 6\n", + "Masking Green_Tailed_Towhee_0043_797430.jpg train completed - 7\n", + "Masking Green_Tailed_Towhee_0054_154938.jpg train completed - 8\n", + "Masking Green_Tailed_Towhee_0047_154776.jpg train completed - 9\n", + "Masking Green_Tailed_Towhee_0010_797431.jpg train completed - 10\n", + "Masking Green_Tailed_Towhee_0005_154784.jpg train completed - 11\n", + "Masking Green_Tailed_Towhee_0022_154892.jpg train completed - 12\n", + "Masking Green_Tailed_Towhee_0003_797414.jpg train completed - 13\n", + "Masking Green_Tailed_Towhee_0064_154771.jpg train completed - 14\n", + "Masking Green_Tailed_Towhee_0046_154967.jpg train completed - 15\n", + "Masking Green_Tailed_Towhee_0024_154855.jpg train completed - 16\n", + "Masking Green_Tailed_Towhee_0051_154768.jpg train completed - 17\n", + "Masking Green_Tailed_Towhee_0035_154888.jpg train completed - 18\n", + "Masking Green_Tailed_Towhee_0102_797420.jpg train completed - 19\n", + "Masking Green_Tailed_Towhee_0109_797440.jpg train completed - 20\n", + "Masking Green_Tailed_Towhee_0099_154882.jpg train completed - 21\n", + "Masking Green_Tailed_Towhee_0096_154945.jpg train completed - 22\n", + "Masking Green_Tailed_Towhee_0074_154915.jpg train completed - 23\n", + "Masking Green_Tailed_Towhee_0060_154820.jpg train completed - 24\n", + "Masking Green_Tailed_Towhee_0050_154898.jpg train completed - 25\n", + "Masking Green_Tailed_Towhee_0014_797415.jpg train completed - 26\n", + "Masking Green_Tailed_Towhee_0058_797399.jpg train completed - 27\n", + "Masking Green_Tailed_Towhee_0061_154880.jpg train completed - 28\n", + "Masking Green_Tailed_Towhee_0030_797417.jpg train completed - 29\n", + "Masking Green_Tailed_Towhee_0012_797435.jpg train completed - 30\n", + "Masking Green_Tailed_Towhee_0021_797404.jpg train completed - 31\n", + "Masking Green_Tailed_Towhee_0082_797395.jpg train completed - 32\n", + "Masking Green_Tailed_Towhee_0070_154844.jpg train completed - 33\n", + "Masking Green_Tailed_Towhee_0076_154999.jpg train completed - 34\n", + "Masking Green_Tailed_Towhee_0039_154802.jpg train completed - 35\n", + "Masking Green_Tailed_Towhee_0104_797403.jpg train completed - 36\n", + "Masking Green_Tailed_Towhee_0053_154921.jpg valid completed - 1\n", + "Masking Green_Tailed_Towhee_0101_154907.jpg valid completed - 2\n", + "Masking Green_Tailed_Towhee_0090_797434.jpg valid completed - 3\n", + "Masking Green_Tailed_Towhee_0004_154951.jpg valid completed - 4\n", + "Masking Green_Tailed_Towhee_0017_797400.jpg valid completed - 5\n", + "Masking Green_Tailed_Towhee_0062_797424.jpg valid completed - 6\n", + "Masking Green_Tailed_Towhee_0027_154823.jpg valid completed - 7\n", + "Masking Green_Tailed_Towhee_0105_797438.jpg valid completed - 8\n", + "Masking Green_Tailed_Towhee_0015_797425.jpg valid completed - 9\n", + "Masking Green_Tailed_Towhee_0066_797439.jpg valid completed - 10\n", + "Masking Green_Tailed_Towhee_0018_154825.jpg valid completed - 11\n", + "Masking Green_Tailed_Towhee_0001_797412.jpg valid completed - 12\n", + "Masking Green_Tailed_Towhee_0037_797405.jpg test completed - 1\n", + "Masking Green_Tailed_Towhee_0100_154966.jpg test completed - 2\n", + "Masking Green_Tailed_Towhee_0094_797396.jpg test completed - 3\n", + "Masking Green_Tailed_Towhee_0068_154783.jpg test completed - 4\n", + "Masking Green_Tailed_Towhee_0042_154801.jpg test completed - 5\n", + "Masking Green_Tailed_Towhee_0067_797426.jpg test completed - 6\n", + "Masking Green_Tailed_Towhee_0069_797441.jpg test completed - 7\n", + "Masking Green_Tailed_Towhee_0072_154946.jpg test completed - 8\n", + "Masking Green_Tailed_Towhee_0092_797397.jpg test completed - 9\n", + "Masking Green_Tailed_Towhee_0025_797401.jpg test completed - 10\n", + "Masking Green_Tailed_Towhee_0011_797410.jpg test completed - 11\n", + "Masking Green_Tailed_Towhee_0085_154822.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/149.Brown_Thrasher\n", + "Train indexes: [ 4 38 29 16 39 25 51 28 10 40 12 50 31 15 46 41 34 33 23 35 9 0 27 54\n", + " 17 44 58 42 37 43 19 20 30 49 56]\n", + "Validation indexes: [ 7 57 8 2 22 6 36 53 32 18 3 48]\n", + "Test indexes: [47 26 52 5 13 14 45 1 11 55 24 21]\n", + "Masking Brown_Thrasher_0010_155231.jpg train completed - 1\n", + "Masking Brown_Thrasher_0081_155256.jpg train completed - 2\n", + "Masking Brown_Thrasher_0063_155127.jpg train completed - 3\n", + "Masking Brown_Thrasher_0033_155156.jpg train completed - 4\n", + "Masking Brown_Thrasher_0084_155189.jpg train completed - 5\n", + "Masking Brown_Thrasher_0054_155145.jpg train completed - 6\n", + "Masking Brown_Thrasher_0120_155133.jpg train completed - 7\n", + "Masking Brown_Thrasher_0061_155140.jpg train completed - 8\n", + "Masking Brown_Thrasher_0019_155216.jpg train completed - 9\n", + "Masking Brown_Thrasher_0085_155445.jpg train completed - 10\n", + "Masking Brown_Thrasher_0021_155160.jpg train completed - 11\n", + "Masking Brown_Thrasher_0119_155170.jpg train completed - 12\n", + "Masking Brown_Thrasher_0069_155151.jpg train completed - 13\n", + "Masking Brown_Thrasher_0030_155152.jpg train completed - 14\n", + "Masking Brown_Thrasher_0105_155187.jpg train completed - 15\n", + "Masking Brown_Thrasher_0092_155415.jpg train completed - 16\n", + "Masking Brown_Thrasher_0073_155376.jpg train completed - 17\n", + "Masking Brown_Thrasher_0072_155406.jpg train completed - 18\n", + "Masking Brown_Thrasher_0051_155344.jpg train completed - 19\n", + "Masking Brown_Thrasher_0074_155339.jpg train completed - 20\n", + "Masking Brown_Thrasher_0015_155165.jpg train completed - 21\n", + "Masking Brown_Thrasher_0001_155380.jpg train completed - 22\n", + "Masking Brown_Thrasher_0057_155164.jpg train completed - 23\n", + "Masking Brown_Thrasher_0126_155199.jpg train completed - 24\n", + "Masking Brown_Thrasher_0034_155139.jpg train completed - 25\n", + "Masking Brown_Thrasher_0096_155412.jpg train completed - 26\n", + "Masking Brown_Thrasher_0135_155366.jpg train completed - 27\n", + "Masking Brown_Thrasher_0093_155309.jpg train completed - 28\n", + "Masking Brown_Thrasher_0080_155200.jpg train completed - 29\n", + "Masking Brown_Thrasher_0095_155082.jpg train completed - 30\n", + "Masking Brown_Thrasher_0042_155213.jpg train completed - 31\n", + "Masking Brown_Thrasher_0045_155182.jpg train completed - 32\n", + "Masking Brown_Thrasher_0065_155229.jpg train completed - 33\n", + "Masking Brown_Thrasher_0113_155111.jpg train completed - 34\n", + "Masking Brown_Thrasher_0130_155350.jpg train completed - 35\n", + 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Brown_Thrasher_0011_155370.jpg test completed - 4\n", + "Masking Brown_Thrasher_0024_155363.jpg test completed - 5\n", + "Masking Brown_Thrasher_0026_155438.jpg test completed - 6\n", + "Masking Brown_Thrasher_0100_155129.jpg test completed - 7\n", + "Masking Brown_Thrasher_0005_155176.jpg test completed - 8\n", + "Masking Brown_Thrasher_0020_155223.jpg test completed - 9\n", + "Masking Brown_Thrasher_0127_155193.jpg test completed - 10\n", + "Masking Brown_Thrasher_0052_155254.jpg test completed - 11\n", + "Masking Brown_Thrasher_0046_155154.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/150.Sage_Thrasher\n", + "Train indexes: [ 9 32 58 7 51 20 28 37 33 22 21 56 3 24 34 40 5 38 45 6 55 57 50 8\n", + " 52 18 26 53 31 44 14 0 39 48 35 2]\n", + "Validation indexes: [ 1 27 12 49 47 36 41 4 11 43 17 13]\n", + "Test indexes: [19 16 25 30 46 29 54 10 59 15 23 42]\n", + "Masking Sage_Thrasher_0021_155548.jpg train completed - 1\n", + "Masking Sage_Thrasher_0061_155462.jpg 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Sage_Thrasher_0025_155661.jpg valid completed - 12\n", + "Masking Sage_Thrasher_0038_155498.jpg test completed - 1\n", + "Masking Sage_Thrasher_0031_796455.jpg test completed - 2\n", + "Masking Sage_Thrasher_0050_155475.jpg test completed - 3\n", + "Masking Sage_Thrasher_0057_155488.jpg test completed - 4\n", + "Masking Sage_Thrasher_0081_155724.jpg test completed - 5\n", + "Masking Sage_Thrasher_0056_796457.jpg test completed - 6\n", + "Masking Sage_Thrasher_0096_155449.jpg test completed - 7\n", + "Masking Sage_Thrasher_0022_155447.jpg test completed - 8\n", + "Masking Sage_Thrasher_0106_155618.jpg test completed - 9\n", + "Masking Sage_Thrasher_0029_796459.jpg test completed - 10\n", + "Masking Sage_Thrasher_0044_155610.jpg test completed - 11\n", + "Masking Sage_Thrasher_0075_155527.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/151.Black_capped_Vireo\n", + "Train indexes: [ 8 18 19 27 48 41 26 29 13 24 35 32 14 7 17 33 43 34 37 1 16 15 36 20\n", + " 6 45 25 44 30 42]\n", + "Validation indexes: [47 38 50 46 5 10 49 23 21 28]\n", + "Test indexes: [11 12 0 4 40 3 9 31 22 2 39]\n", + "Masking Black_Capped_Vireo_0010_155832.jpg train completed - 1\n", + "Masking Black_Capped_Vireo_0020_797461.jpg train completed - 2\n", + "Masking Black_Capped_Vireo_0021_797480.jpg train completed - 3\n", + "Masking Black_Capped_Vireo_0030_155861.jpg train completed - 4\n", + "Masking Black_Capped_Vireo_0051_797456.jpg train completed - 5\n", + "Masking Black_Capped_Vireo_0044_155819.jpg train completed - 6\n", + "Masking Black_Capped_Vireo_0029_797469.jpg train completed - 7\n", + "Masking Black_Capped_Vireo_0032_797470.jpg train completed - 8\n", + "Masking Black_Capped_Vireo_0015_797450.jpg train completed - 9\n", + "Masking Black_Capped_Vireo_0027_797455.jpg train completed - 10\n", + "Masking Black_Capped_Vireo_0038_797465.jpg train completed - 11\n", + "Masking Black_Capped_Vireo_0035_797471.jpg train completed - 12\n", + "Masking 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Black_Capped_Vireo_0013_155815.jpg test completed - 1\n", + "Masking Black_Capped_Vireo_0014_797462.jpg test completed - 2\n", + "Masking Black_Capped_Vireo_0002_797466.jpg test completed - 3\n", + "Masking Black_Capped_Vireo_0006_797491.jpg test completed - 4\n", + "Masking Black_Capped_Vireo_0043_797458.jpg test completed - 5\n", + "Masking Black_Capped_Vireo_0005_797453.jpg test completed - 6\n", + "Masking Black_Capped_Vireo_0011_797488.jpg test completed - 7\n", + "Masking Black_Capped_Vireo_0034_797448.jpg test completed - 8\n", + "Masking Black_Capped_Vireo_0024_155905.jpg test completed - 9\n", + "Masking Black_Capped_Vireo_0004_797476.jpg test completed - 10\n", + "Masking Black_Capped_Vireo_0042_797483.jpg test completed - 11\n", + "/kaggle/working/CUB_200_2011/images/152.Blue_headed_Vireo\n", + "Train indexes: [29 11 45 52 9 19 59 36 24 37 25 18 6 35 28 27 8 2 57 39 50 54 34 21\n", + " 12 5 10 1 48 53 17 20 30 22 7 0]\n", + "Validation indexes: [42 38 41 51 40 46 56 15 44 4 47 3]\n", + "Test indexes: [16 33 58 32 31 23 55 49 14 43 13 26]\n", + "Masking Blue_Headed_Vireo_0068_156222.jpg train completed - 1\n", + "Masking Blue_Headed_Vireo_0023_156112.jpg train completed - 2\n", + "Masking Blue_Headed_Vireo_0103_156382.jpg train completed - 3\n", + "Masking Blue_Headed_Vireo_0115_156099.jpg train completed - 4\n", + "Masking Blue_Headed_Vireo_0020_155994.jpg train completed - 5\n", + "Masking Blue_Headed_Vireo_0037_155955.jpg train completed - 6\n", + "Masking Blue_Headed_Vireo_0123_156443.jpg train completed - 7\n", + "Masking Blue_Headed_Vireo_0087_156461.jpg train completed - 8\n", + "Masking Blue_Headed_Vireo_0055_156247.jpg train completed - 9\n", + "Masking Blue_Headed_Vireo_0088_156416.jpg train completed - 10\n", + "Masking Blue_Headed_Vireo_0058_156261.jpg train completed - 11\n", + "Masking Blue_Headed_Vireo_0034_156251.jpg train completed - 12\n", + "Masking Blue_Headed_Vireo_0012_156434.jpg train completed - 13\n", + "Masking 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Blue_Headed_Vireo_0005_156007.jpg train completed - 28\n", + "Masking Blue_Headed_Vireo_0110_155952.jpg train completed - 29\n", + "Masking Blue_Headed_Vireo_0116_156049.jpg train completed - 30\n", + "Masking Blue_Headed_Vireo_0032_156239.jpg train completed - 31\n", + "Masking Blue_Headed_Vireo_0039_156397.jpg train completed - 32\n", + "Masking Blue_Headed_Vireo_0072_155987.jpg train completed - 33\n", + "Masking Blue_Headed_Vireo_0049_156082.jpg train completed - 34\n", + "Masking Blue_Headed_Vireo_0017_156063.jpg train completed - 35\n", + "Masking Blue_Headed_Vireo_0002_156241.jpg train completed - 36\n", + "Masking Blue_Headed_Vireo_0097_156272.jpg valid completed - 1\n", + "Masking Blue_Headed_Vireo_0089_156062.jpg valid completed - 2\n", + "Masking Blue_Headed_Vireo_0096_155969.jpg valid completed - 3\n", + "Masking Blue_Headed_Vireo_0113_156332.jpg valid completed - 4\n", + "Masking Blue_Headed_Vireo_0095_156092.jpg valid completed - 5\n", + "Masking Blue_Headed_Vireo_0105_156229.jpg valid completed - 6\n", + "Masking Blue_Headed_Vireo_0119_156259.jpg valid completed - 7\n", + "Masking Blue_Headed_Vireo_0028_156217.jpg valid completed - 8\n", + "Masking Blue_Headed_Vireo_0101_156203.jpg valid completed - 9\n", + "Masking Blue_Headed_Vireo_0010_156344.jpg valid completed - 10\n", + "Masking Blue_Headed_Vireo_0107_156111.jpg valid completed - 11\n", + "Masking Blue_Headed_Vireo_0009_155953.jpg valid completed - 12\n", + "Masking Blue_Headed_Vireo_0031_156056.jpg test completed - 1\n", + "Masking Blue_Headed_Vireo_0079_156086.jpg test completed - 2\n", + "Masking Blue_Headed_Vireo_0122_156017.jpg test completed - 3\n", + "Masking Blue_Headed_Vireo_0076_156209.jpg test completed - 4\n", + "Masking Blue_Headed_Vireo_0075_156181.jpg test completed - 5\n", + "Masking Blue_Headed_Vireo_0054_156455.jpg test completed - 6\n", + "Masking Blue_Headed_Vireo_0118_156193.jpg test completed - 7\n", + "Masking Blue_Headed_Vireo_0111_156258.jpg test completed - 8\n", + "Masking Blue_Headed_Vireo_0027_155999.jpg test completed - 9\n", + "Masking Blue_Headed_Vireo_0098_156348.jpg test completed - 10\n", + "Masking Blue_Headed_Vireo_0026_156245.jpg test completed - 11\n", + "Masking Blue_Headed_Vireo_0060_156171.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/153.Philadelphia_Vireo\n", + "Train indexes: [29 31 6 43 49 14 7 35 40 4 12 55 23 22 10 11 38 51 32 36 48 16 15 17\n", + " 46 54 30 39 34 28 21 0 19 50 56]\n", + "Validation indexes: [33 2 44 5 25 9 20 18 41 53 8 24]\n", + "Test indexes: [26 1 45 13 57 58 27 47 42 3 52 37]\n", + "Masking Philadelphia_Vireo_0042_156528.jpg train completed - 1\n", + "Masking Philadelphia_Vireo_0044_156548.jpg train completed - 2\n", + "Masking Philadelphia_Vireo_0010_794769.jpg train completed - 3\n", + "Masking Philadelphia_Vireo_0060_156555.jpg train completed - 4\n", + "Masking Philadelphia_Vireo_0070_794758.jpg train completed - 5\n", + "Masking 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Philadelphia_Vireo_0039_794794.jpg test completed - 1\n", + "Masking Philadelphia_Vireo_0002_156591.jpg test completed - 2\n", + "Masking Philadelphia_Vireo_0062_794777.jpg test completed - 3\n", + "Masking Philadelphia_Vireo_0021_794765.jpg test completed - 4\n", + "Masking Philadelphia_Vireo_0085_794757.jpg test completed - 5\n", + "Masking Philadelphia_Vireo_0087_794767.jpg test completed - 6\n", + "Masking Philadelphia_Vireo_0040_794764.jpg test completed - 7\n", + "Masking Philadelphia_Vireo_0067_156584.jpg test completed - 8\n", + "Masking Philadelphia_Vireo_0059_794799.jpg test completed - 9\n", + "Masking Philadelphia_Vireo_0005_156599.jpg test completed - 10\n", + "Masking Philadelphia_Vireo_0074_156492.jpg test completed - 11\n", + "Masking Philadelphia_Vireo_0052_794774.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/154.Red_eyed_Vireo\n", + "Train indexes: [38 29 58 59 7 43 32 16 44 47 27 35 39 21 36 24 18 8 17 14 48 1 49 42\n", + " 33 10 40 0 22 28 37 5 2 51 6 9]\n", + "Validation indexes: [25 31 15 3 55 23 50 41 30 53 12 13]\n", + "Test indexes: [26 4 45 56 54 52 46 20 11 19 57 34]\n", + "Masking Red_Eyed_Vireo_0079_158198.jpg train completed - 1\n", + "Masking Red_Eyed_Vireo_0059_156974.jpg train completed - 2\n", + "Masking Red_Eyed_Vireo_0140_157237.jpg train completed - 3\n", + "Masking Red_Eyed_Vireo_0141_157205.jpg train completed - 4\n", + "Masking Red_Eyed_Vireo_0020_156875.jpg train completed - 5\n", + "Masking Red_Eyed_Vireo_0096_157013.jpg train completed - 6\n", + "Masking Red_Eyed_Vireo_0065_157019.jpg train completed - 7\n", + "Masking Red_Eyed_Vireo_0038_156963.jpg train completed - 8\n", + "Masking Red_Eyed_Vireo_0101_156988.jpg train completed - 9\n", + "Masking Red_Eyed_Vireo_0111_157030.jpg train completed - 10\n", + "Masking Red_Eyed_Vireo_0056_156968.jpg train completed - 11\n", + "Masking Red_Eyed_Vireo_0074_157170.jpg train completed - 12\n", + "Masking Red_Eyed_Vireo_0083_157063.jpg train completed - 13\n", + 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Red_Eyed_Vireo_0138_156798.jpg test completed - 11\n", + "Masking Red_Eyed_Vireo_0073_156944.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/155.Warbling_Vireo\n", + "Train indexes: [11 24 26 32 3 9 35 47 20 0 52 25 2 12 16 49 1 46 58 19 50 15 14 57\n", + " 34 55 56 23 29 31 59 22 27 37 4 10]\n", + "Validation indexes: [42 53 28 33 48 13 40 45 5 44 6 43]\n", + "Test indexes: [54 8 21 17 39 7 51 38 41 30 36 18]\n", + "Masking Warbling_Vireo_0019_158313.jpg train completed - 1\n", + "Masking Warbling_Vireo_0058_158539.jpg train completed - 2\n", + "Masking Warbling_Vireo_0064_158437.jpg train completed - 3\n", + "Masking Warbling_Vireo_0075_158480.jpg train completed - 4\n", + "Masking Warbling_Vireo_0004_158376.jpg train completed - 5\n", + "Masking Warbling_Vireo_0017_158271.jpg train completed - 6\n", + "Masking Warbling_Vireo_0079_158791.jpg train completed - 7\n", + "Masking Warbling_Vireo_0102_158704.jpg train completed - 8\n", + "Masking 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Warbling_Vireo_0014_158412.jpg test completed - 6\n", + "Masking Warbling_Vireo_0111_158423.jpg test completed - 7\n", + "Masking Warbling_Vireo_0086_158564.jpg test completed - 8\n", + "Masking Warbling_Vireo_0089_158519.jpg test completed - 9\n", + "Masking Warbling_Vireo_0072_158422.jpg test completed - 10\n", + "Masking Warbling_Vireo_0081_158344.jpg test completed - 11\n", + "Masking Warbling_Vireo_0045_158399.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/156.White_eyed_Vireo\n", + "Train indexes: [50 1 9 35 53 21 41 49 6 38 14 52 15 0 12 2 43 23 42 13 44 34 32 37\n", + " 57 28 20 10 56 3 25 24 58 5 11 36]\n", + "Validation indexes: [33 7 30 18 4 51 29 47 46 59 17 40]\n", + "Test indexes: [27 16 8 54 39 31 26 48 55 19 22 45]\n", + "Masking White_Eyed_Vireo_0122_159284.jpg train completed - 1\n", + "Masking White_Eyed_Vireo_0002_159180.jpg train completed - 2\n", + "Masking White_Eyed_Vireo_0024_159193.jpg train completed - 3\n", + "Masking 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completed - 11\n", + "Masking White_Eyed_Vireo_0099_158933.jpg valid completed - 12\n", + "Masking White_Eyed_Vireo_0071_159072.jpg test completed - 1\n", + "Masking White_Eyed_Vireo_0041_159032.jpg test completed - 2\n", + "Masking White_Eyed_Vireo_0022_159037.jpg test completed - 3\n", + "Masking White_Eyed_Vireo_0129_158823.jpg test completed - 4\n", + "Masking White_Eyed_Vireo_0095_158886.jpg test completed - 5\n", + "Masking White_Eyed_Vireo_0082_159186.jpg test completed - 6\n", + "Masking White_Eyed_Vireo_0066_159477.jpg test completed - 7\n", + "Masking White_Eyed_Vireo_0118_159036.jpg test completed - 8\n", + "Masking White_Eyed_Vireo_0130_159075.jpg test completed - 9\n", + "Masking White_Eyed_Vireo_0046_158849.jpg test completed - 10\n", + "Masking White_Eyed_Vireo_0051_159033.jpg test completed - 11\n", + "Masking White_Eyed_Vireo_0114_159206.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/157.Yellow_throated_Vireo\n", + "Train indexes: [18 34 1 17 2 53 36 16 42 21 26 25 40 58 51 24 7 43 55 13 19 47 37 6\n", + " 3 45 49 28 5 38 32 15 12 31 14]\n", + "Validation indexes: [ 8 39 52 50 54 33 9 4 41 27 20 57]\n", + "Test indexes: [30 46 56 48 10 29 23 0 11 44 35 22]\n", + "Masking Yellow_Throated_Vireo_0025_795009.jpg train completed - 1\n", + "Masking Yellow_Throated_Vireo_0050_794979.jpg train completed - 2\n", + "Masking Yellow_Throated_Vireo_0003_794974.jpg train completed - 3\n", + "Masking Yellow_Throated_Vireo_0023_159584.jpg train completed - 4\n", + "Masking Yellow_Throated_Vireo_0005_159588.jpg train completed - 5\n", + "Masking Yellow_Throated_Vireo_0078_159694.jpg train completed - 6\n", + "Masking Yellow_Throated_Vireo_0054_159635.jpg train completed - 7\n", + "Masking Yellow_Throated_Vireo_0022_795012.jpg train completed - 8\n", + "Masking Yellow_Throated_Vireo_0064_794992.jpg train completed - 9\n", + "Masking Yellow_Throated_Vireo_0028_795008.jpg train completed - 10\n", + "Masking Yellow_Throated_Vireo_0035_794991.jpg train completed - 11\n", + "Masking Yellow_Throated_Vireo_0034_794993.jpg train completed - 12\n", + "Masking Yellow_Throated_Vireo_0059_794970.jpg train completed - 13\n", + "Masking Yellow_Throated_Vireo_0085_159580.jpg train completed - 14\n", + "Masking Yellow_Throated_Vireo_0076_794984.jpg train completed - 15\n", + "Masking Yellow_Throated_Vireo_0032_159632.jpg train completed - 16\n", + "Masking Yellow_Throated_Vireo_0010_795000.jpg train completed - 17\n", + "Masking Yellow_Throated_Vireo_0066_795007.jpg train completed - 18\n", + "Masking Yellow_Throated_Vireo_0080_794990.jpg train completed - 19\n", + "Masking Yellow_Throated_Vireo_0017_794988.jpg train completed - 20\n", + "Masking Yellow_Throated_Vireo_0026_159686.jpg train completed - 21\n", + "Masking Yellow_Throated_Vireo_0072_794969.jpg train completed - 22\n", + "Masking Yellow_Throated_Vireo_0055_159532.jpg train completed - 23\n", + "Masking Yellow_Throated_Vireo_0009_794976.jpg train completed - 24\n", + "Masking 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valid completed - 3\n", + "Masking Yellow_Throated_Vireo_0075_795004.jpg valid completed - 4\n", + "Masking Yellow_Throated_Vireo_0079_159576.jpg valid completed - 5\n", + "Masking Yellow_Throated_Vireo_0048_794982.jpg valid completed - 6\n", + "Masking Yellow_Throated_Vireo_0012_794977.jpg valid completed - 7\n", + "Masking Yellow_Throated_Vireo_0007_794972.jpg valid completed - 8\n", + "Masking Yellow_Throated_Vireo_0063_159677.jpg valid completed - 9\n", + "Masking Yellow_Throated_Vireo_0036_159595.jpg valid completed - 10\n", + "Masking Yellow_Throated_Vireo_0027_794989.jpg valid completed - 11\n", + "Masking Yellow_Throated_Vireo_0084_159639.jpg valid completed - 12\n", + "Masking Yellow_Throated_Vireo_0042_159655.jpg test completed - 1\n", + "Masking Yellow_Throated_Vireo_0071_159707.jpg test completed - 2\n", + "Masking Yellow_Throated_Vireo_0082_159597.jpg test completed - 3\n", + "Masking Yellow_Throated_Vireo_0073_159583.jpg test completed - 4\n", + "Masking Yellow_Throated_Vireo_0013_159531.jpg test completed - 5\n", + "Masking Yellow_Throated_Vireo_0041_794998.jpg test completed - 6\n", + "Masking Yellow_Throated_Vireo_0031_159712.jpg test completed - 7\n", + "Masking Yellow_Throated_Vireo_0002_159625.jpg test completed - 8\n", + "Masking Yellow_Throated_Vireo_0014_159709.jpg test completed - 9\n", + "Masking Yellow_Throated_Vireo_0068_159620.jpg test completed - 10\n", + "Masking Yellow_Throated_Vireo_0052_794973.jpg test completed - 11\n", + "Masking Yellow_Throated_Vireo_0029_794985.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/158.Bay_breasted_Warbler\n", + "Train indexes: [57 37 2 8 49 41 43 44 51 50 26 12 38 33 9 20 15 25 48 45 24 32 11 40\n", + " 7 29 0 17 47 5 19 54 34 30 39 36]\n", + "Validation indexes: [23 55 35 58 53 22 31 27 21 42 6 59]\n", + "Test indexes: [46 28 10 18 16 13 56 14 4 3 1 52]\n", + "Masking Bay_Breasted_Warbler_0109_159883.jpg train completed - 1\n", + "Masking 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completed - 6\n", + "Masking Black_Throated_Blue_Warbler_0090_161507.jpg train completed - 7\n", + "Masking Black_Throated_Blue_Warbler_0085_161621.jpg train completed - 8\n", + "Masking Black_Throated_Blue_Warbler_0126_161724.jpg train completed - 9\n", + "Masking Black_Throated_Blue_Warbler_0129_161399.jpg train completed - 10\n", + "Masking Black_Throated_Blue_Warbler_0133_161539.jpg train completed - 11\n", + "Masking Black_Throated_Blue_Warbler_0080_161681.jpg train completed - 12\n", + "Masking Black_Throated_Blue_Warbler_0121_161418.jpg train completed - 13\n", + "Masking Black_Throated_Blue_Warbler_0119_161416.jpg train completed - 14\n", + "Masking Black_Throated_Blue_Warbler_0010_161169.jpg train completed - 15\n", + "Masking Black_Throated_Blue_Warbler_0100_161622.jpg train completed - 16\n", + "Masking Black_Throated_Blue_Warbler_0106_161523.jpg train completed - 17\n", + "Masking Black_Throated_Blue_Warbler_0060_161644.jpg train completed - 18\n", + "Masking 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valid completed - 8\n", + "Masking Black_Throated_Blue_Warbler_0136_161400.jpg valid completed - 9\n", + "Masking Black_Throated_Blue_Warbler_0027_104004.jpg valid completed - 10\n", + "Masking Black_Throated_Blue_Warbler_0050_161154.jpg valid completed - 11\n", + "Masking Black_Throated_Blue_Warbler_0102_161161.jpg valid completed - 12\n", + "Masking Black_Throated_Blue_Warbler_0037_161707.jpg test completed - 1\n", + "Masking Black_Throated_Blue_Warbler_0054_161158.jpg test completed - 2\n", + "Masking Black_Throated_Blue_Warbler_0110_161726.jpg test completed - 3\n", + "Masking Black_Throated_Blue_Warbler_0107_161214.jpg test completed - 4\n", + "Masking Black_Throated_Blue_Warbler_0113_161407.jpg test completed - 5\n", + "Masking Black_Throated_Blue_Warbler_0017_161220.jpg test completed - 6\n", + "Masking Black_Throated_Blue_Warbler_0064_161656.jpg test completed - 7\n", + "Masking Black_Throated_Blue_Warbler_0086_161716.jpg test completed - 8\n", + "Masking Black_Throated_Blue_Warbler_0072_161636.jpg test completed - 9\n", + "Masking Black_Throated_Blue_Warbler_0076_161162.jpg test completed - 10\n", + "Masking Black_Throated_Blue_Warbler_0022_161520.jpg test completed - 11\n", + "Masking Black_Throated_Blue_Warbler_0016_161216.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/161.Blue_winged_Warbler\n", + "Train indexes: [25 38 0 6 51 32 28 8 40 57 42 52 33 21 9 2 13 31 24 10 5 39 1 50\n", + " 34 17 12 43 48 23 56 37 4 16 58 18]\n", + "Validation indexes: [19 44 29 46 30 15 22 20 55 53 36 26]\n", + "Test indexes: [59 45 41 27 7 54 35 3 49 11 47 14]\n", + "Masking Blue_Winged_Warbler_0042_161869.jpg train completed - 1\n", + "Masking Blue_Winged_Warbler_0059_162064.jpg train completed - 2\n", + "Masking Blue_Winged_Warbler_0002_161890.jpg train completed - 3\n", + "Masking Blue_Winged_Warbler_0013_161816.jpg train completed - 4\n", + "Masking Blue_Winged_Warbler_0079_161909.jpg train completed - 5\n", + "Masking 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"Masking Blue_Winged_Warbler_0020_161875.jpg train completed - 20\n", + "Masking Blue_Winged_Warbler_0012_162086.jpg train completed - 21\n", + "Masking Blue_Winged_Warbler_0060_161888.jpg train completed - 22\n", + "Masking Blue_Winged_Warbler_0004_162005.jpg train completed - 23\n", + "Masking Blue_Winged_Warbler_0078_161889.jpg train completed - 24\n", + "Masking Blue_Winged_Warbler_0054_161862.jpg train completed - 25\n", + "Masking Blue_Winged_Warbler_0030_162088.jpg train completed - 26\n", + "Masking Blue_Winged_Warbler_0023_161774.jpg train completed - 27\n", + "Masking Blue_Winged_Warbler_0066_162062.jpg train completed - 28\n", + "Masking Blue_Winged_Warbler_0076_161894.jpg train completed - 29\n", + "Masking Blue_Winged_Warbler_0040_161883.jpg train completed - 30\n", + "Masking Blue_Winged_Warbler_0091_162051.jpg train completed - 31\n", + "Masking Blue_Winged_Warbler_0057_162085.jpg train completed - 32\n", + "Masking Blue_Winged_Warbler_0009_161880.jpg train completed - 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11\n", + "Masking Blue_Winged_Warbler_0043_161804.jpg valid completed - 12\n", + "Masking Blue_Winged_Warbler_0094_161790.jpg test completed - 1\n", + "Masking Blue_Winged_Warbler_0071_161900.jpg test completed - 2\n", + "Masking Blue_Winged_Warbler_0063_161810.jpg test completed - 3\n", + "Masking Blue_Winged_Warbler_0044_161760.jpg test completed - 4\n", + "Masking Blue_Winged_Warbler_0014_161783.jpg test completed - 5\n", + "Masking Blue_Winged_Warbler_0088_162018.jpg test completed - 6\n", + "Masking Blue_Winged_Warbler_0055_161996.jpg test completed - 7\n", + "Masking Blue_Winged_Warbler_0007_161785.jpg test completed - 8\n", + "Masking Blue_Winged_Warbler_0077_162021.jpg test completed - 9\n", + "Masking Blue_Winged_Warbler_0021_161858.jpg test completed - 10\n", + "Masking Blue_Winged_Warbler_0075_161844.jpg test completed - 11\n", + "Masking Blue_Winged_Warbler_0026_161813.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/162.Canada_Warbler\n", + "Train indexes: [56 40 38 12 42 0 28 3 15 37 29 32 43 1 2 25 19 48 31 35 24 41 36 44\n", + " 55 8 59 39 14 50 53 57 51 46 18 58]\n", + "Validation indexes: [34 49 6 10 9 45 30 11 23 21 4 27]\n", + "Test indexes: [13 33 7 16 52 17 26 5 47 54 22 20]\n", + "Masking Canada_Warbler_0119_162307.jpg train completed - 1\n", + "Masking Canada_Warbler_0090_162432.jpg train completed - 2\n", + "Masking Canada_Warbler_0087_162342.jpg train completed - 3\n", + "Masking Canada_Warbler_0021_162325.jpg train completed - 4\n", + "Masking Canada_Warbler_0093_162356.jpg train completed - 5\n", + "Masking Canada_Warbler_0002_162426.jpg train completed - 6\n", + "Masking Canada_Warbler_0056_162407.jpg train completed - 7\n", + "Masking Canada_Warbler_0006_103959.jpg train completed - 8\n", + "Masking Canada_Warbler_0024_162218.jpg train completed - 9\n", + "Masking Canada_Warbler_0085_162385.jpg train completed - 10\n", + "Masking Canada_Warbler_0063_162324.jpg train completed - 11\n", + "Masking 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"Masking Canada_Warbler_0122_162219.jpg train completed - 27\n", + "Masking Canada_Warbler_0088_162341.jpg train completed - 28\n", + "Masking Canada_Warbler_0023_162383.jpg train completed - 29\n", + "Masking Canada_Warbler_0109_93363.jpg train completed - 30\n", + "Masking Canada_Warbler_0114_162396.jpg train completed - 31\n", + "Masking Canada_Warbler_0120_162415.jpg train completed - 32\n", + "Masking Canada_Warbler_0112_162398.jpg train completed - 33\n", + "Masking Canada_Warbler_0103_162339.jpg train completed - 34\n", + "Masking Canada_Warbler_0035_162377.jpg train completed - 35\n", + "Masking Canada_Warbler_0121_162310.jpg train completed - 36\n", + "Masking Canada_Warbler_0076_162393.jpg valid completed - 1\n", + "Masking Canada_Warbler_0107_162440.jpg valid completed - 2\n", + "Masking Canada_Warbler_0009_162343.jpg valid completed - 3\n", + "Masking Canada_Warbler_0019_162409.jpg valid completed - 4\n", + "Masking Canada_Warbler_0016_162411.jpg valid completed - 5\n", + "Masking Canada_Warbler_0099_162425.jpg valid completed - 6\n", + "Masking Canada_Warbler_0064_162417.jpg valid completed - 7\n", + "Masking Canada_Warbler_0020_162354.jpg valid completed - 8\n", + "Masking Canada_Warbler_0042_162448.jpg valid completed - 9\n", + "Masking Canada_Warbler_0040_162352.jpg valid completed - 10\n", + "Masking Canada_Warbler_0007_162364.jpg valid completed - 11\n", + "Masking Canada_Warbler_0051_162447.jpg valid completed - 12\n", + "Masking Canada_Warbler_0022_157106.jpg test completed - 1\n", + "Masking Canada_Warbler_0075_162428.jpg test completed - 2\n", + "Masking Canada_Warbler_0013_162375.jpg test completed - 3\n", + "Masking Canada_Warbler_0027_162223.jpg test completed - 4\n", + "Masking Canada_Warbler_0113_162403.jpg test completed - 5\n", + "Masking Canada_Warbler_0032_162229.jpg test completed - 6\n", + "Masking Canada_Warbler_0049_162379.jpg test completed - 7\n", + "Masking Canada_Warbler_0008_162416.jpg test completed - 8\n", + "Masking Canada_Warbler_0104_162345.jpg test completed - 9\n", + "Masking Canada_Warbler_0115_162309.jpg test completed - 10\n", + "Masking Canada_Warbler_0041_162418.jpg test completed - 11\n", + "Masking Canada_Warbler_0039_162330.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/163.Cape_May_Warbler\n", + "Train indexes: [ 3 15 45 5 18 53 34 13 58 39 4 6 31 17 40 37 33 57 41 56 27 29 28 23\n", + " 9 1 2 11 55 46 22 0 51 21 35 19]\n", + "Validation indexes: [48 52 24 7 42 59 44 8 32 38 10 50]\n", + "Test indexes: [47 49 30 54 43 12 25 20 16 36 14 26]\n", + "Masking Cape_May_Warbler_0005_163197.jpg train completed - 1\n", + "Masking Cape_May_Warbler_0032_162659.jpg train completed - 2\n", + "Masking Cape_May_Warbler_0100_162923.jpg train completed - 3\n", + "Masking Cape_May_Warbler_0008_163062.jpg train completed - 4\n", + "Masking Cape_May_Warbler_0040_163036.jpg train completed - 5\n", + "Masking Cape_May_Warbler_0115_163121.jpg train completed - 6\n", + "Masking 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train completed - 21\n", + "Masking Cape_May_Warbler_0062_162955.jpg train completed - 22\n", + "Masking Cape_May_Warbler_0061_163061.jpg train completed - 23\n", + "Masking Cape_May_Warbler_0049_162909.jpg train completed - 24\n", + "Masking Cape_May_Warbler_0020_162629.jpg train completed - 25\n", + "Masking Cape_May_Warbler_0003_162920.jpg train completed - 26\n", + "Masking Cape_May_Warbler_0004_162928.jpg train completed - 27\n", + "Masking Cape_May_Warbler_0023_163133.jpg train completed - 28\n", + "Masking Cape_May_Warbler_0119_162976.jpg train completed - 29\n", + "Masking Cape_May_Warbler_0101_163169.jpg train completed - 30\n", + "Masking Cape_May_Warbler_0048_162915.jpg train completed - 31\n", + "Masking Cape_May_Warbler_0001_139008.jpg train completed - 32\n", + "Masking Cape_May_Warbler_0111_162959.jpg train completed - 33\n", + "Masking Cape_May_Warbler_0046_163167.jpg train completed - 34\n", + "Masking Cape_May_Warbler_0076_163075.jpg train completed - 35\n", + "Masking Cape_May_Warbler_0043_163172.jpg train completed - 36\n", + "Masking Cape_May_Warbler_0104_162958.jpg valid completed - 1\n", + "Masking Cape_May_Warbler_0113_163130.jpg valid completed - 2\n", + "Masking Cape_May_Warbler_0053_162950.jpg valid completed - 3\n", + "Masking Cape_May_Warbler_0013_163052.jpg valid completed - 4\n", + "Masking Cape_May_Warbler_0093_163089.jpg valid completed - 5\n", + "Masking Cape_May_Warbler_0129_163157.jpg valid completed - 6\n", + "Masking Cape_May_Warbler_0096_163093.jpg valid completed - 7\n", + "Masking Cape_May_Warbler_0015_163159.jpg valid completed - 8\n", + "Masking Cape_May_Warbler_0068_163147.jpg valid completed - 9\n", + "Masking Cape_May_Warbler_0084_163132.jpg valid completed - 10\n", + "Masking Cape_May_Warbler_0022_162912.jpg valid completed - 11\n", + "Masking Cape_May_Warbler_0108_163108.jpg valid completed - 12\n", + "Masking Cape_May_Warbler_0103_162972.jpg test completed - 1\n", + "Masking Cape_May_Warbler_0107_162941.jpg test completed - 2\n", + "Masking Cape_May_Warbler_0066_163005.jpg test completed - 3\n", + "Masking Cape_May_Warbler_0117_163079.jpg test completed - 4\n", + "Masking Cape_May_Warbler_0095_162965.jpg test completed - 5\n", + "Masking Cape_May_Warbler_0026_162913.jpg test completed - 6\n", + "Masking Cape_May_Warbler_0055_163171.jpg test completed - 7\n", + "Masking Cape_May_Warbler_0044_163055.jpg test completed - 8\n", + "Masking Cape_May_Warbler_0033_162657.jpg test completed - 9\n", + "Masking Cape_May_Warbler_0077_162979.jpg test completed - 10\n", + "Masking Cape_May_Warbler_0031_163012.jpg test completed - 11\n", + "Masking Cape_May_Warbler_0058_162948.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/164.Cerulean_Warbler\n", + "Train indexes: [13 49 15 28 50 6 10 42 23 35 20 41 9 11 5 58 24 36 22 37 8 27 0 55\n", + " 21 1 29 59 31 33 32 40 38 12 17 14]\n", + "Validation indexes: [45 53 46 48 2 26 4 25 39 57 18 34]\n", + "Test indexes: [16 44 56 19 52 54 43 7 30 3 51 47]\n", + "Masking Cerulean_Warbler_0020_163353.jpg train completed - 1\n", + "Masking Cerulean_Warbler_0083_163380.jpg train completed - 2\n", + "Masking Cerulean_Warbler_0023_797191.jpg train completed - 3\n", + "Masking Cerulean_Warbler_0045_797217.jpg train completed - 4\n", + "Masking Cerulean_Warbler_0084_797177.jpg train completed - 5\n", + "Masking Cerulean_Warbler_0010_797210.jpg train completed - 6\n", + "Masking Cerulean_Warbler_0017_797198.jpg train completed - 7\n", + "Masking Cerulean_Warbler_0075_797233.jpg train completed - 8\n", + "Masking Cerulean_Warbler_0039_163420.jpg train completed - 9\n", + "Masking Cerulean_Warbler_0056_163339.jpg train completed - 10\n", + "Masking Cerulean_Warbler_0035_163269.jpg train completed - 11\n", + "Masking Cerulean_Warbler_0072_163200.jpg train completed - 12\n", + "Masking Cerulean_Warbler_0016_163265.jpg train completed - 13\n", + "Masking Cerulean_Warbler_0018_797183.jpg train completed - 14\n", + "Masking 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train completed - 29\n", + "Masking Cerulean_Warbler_0054_797235.jpg train completed - 30\n", + "Masking Cerulean_Warbler_0053_797197.jpg train completed - 31\n", + "Masking Cerulean_Warbler_0071_163201.jpg train completed - 32\n", + "Masking Cerulean_Warbler_0063_797204.jpg train completed - 33\n", + "Masking Cerulean_Warbler_0019_797186.jpg train completed - 34\n", + "Masking Cerulean_Warbler_0025_797232.jpg train completed - 35\n", + "Masking Cerulean_Warbler_0022_797189.jpg train completed - 36\n", + "Masking Cerulean_Warbler_0078_163271.jpg valid completed - 1\n", + "Masking Cerulean_Warbler_0088_797194.jpg valid completed - 2\n", + "Masking Cerulean_Warbler_0080_163399.jpg valid completed - 3\n", + "Masking Cerulean_Warbler_0082_797180.jpg valid completed - 4\n", + "Masking Cerulean_Warbler_0004_797199.jpg valid completed - 5\n", + "Masking Cerulean_Warbler_0042_797223.jpg valid completed - 6\n", + "Masking Cerulean_Warbler_0007_797215.jpg valid completed - 7\n", + "Masking Cerulean_Warbler_0041_163535.jpg valid completed - 8\n", + "Masking Cerulean_Warbler_0067_797220.jpg valid completed - 9\n", + "Masking Cerulean_Warbler_0092_163455.jpg valid completed - 10\n", + "Masking Cerulean_Warbler_0028_163302.jpg valid completed - 11\n", + "Masking Cerulean_Warbler_0055_163524.jpg valid completed - 12\n", + "Masking Cerulean_Warbler_0024_163406.jpg test completed - 1\n", + "Masking Cerulean_Warbler_0077_797202.jpg test completed - 2\n", + "Masking Cerulean_Warbler_0091_163331.jpg test completed - 3\n", + "Masking Cerulean_Warbler_0033_797176.jpg test completed - 4\n", + "Masking Cerulean_Warbler_0087_163451.jpg test completed - 5\n", + "Masking Cerulean_Warbler_0089_163412.jpg test completed - 6\n", + "Masking Cerulean_Warbler_0076_163294.jpg test completed - 7\n", + "Masking Cerulean_Warbler_0012_163417.jpg test completed - 8\n", + "Masking Cerulean_Warbler_0047_797211.jpg test completed - 9\n", + "Masking Cerulean_Warbler_0005_797206.jpg test completed - 10\n", + "Masking Cerulean_Warbler_0086_797214.jpg test completed - 11\n", + "Masking Cerulean_Warbler_0081_797179.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/165.Chestnut_sided_Warbler\n", + "Train indexes: [29 28 2 23 39 25 14 41 8 18 48 13 36 9 45 1 37 33 20 4 58 26 52 22\n", + " 17 24 0 6 32 57 5 56 59 30 31 12]\n", + "Validation indexes: [40 10 44 19 51 55 38 7 3 49 54 42]\n", + "Test indexes: [11 15 34 35 16 27 43 50 47 53 21 46]\n", + "Masking Chestnut_Sided_Warbler_0066_164078.jpg train completed - 1\n", + "Masking Chestnut_Sided_Warbler_0063_163927.jpg train completed - 2\n", + "Masking Chestnut_Sided_Warbler_0005_164061.jpg train completed - 3\n", + "Masking Chestnut_Sided_Warbler_0053_163615.jpg train completed - 4\n", + "Masking Chestnut_Sided_Warbler_0087_164221.jpg train completed - 5\n", + "Masking Chestnut_Sided_Warbler_0058_163990.jpg train completed - 6\n", + "Masking Chestnut_Sided_Warbler_0036_163622.jpg train completed - 7\n", + "Masking 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Chestnut_Sided_Warbler_0127_163860.jpg train completed - 21\n", + "Masking Chestnut_Sided_Warbler_0061_163978.jpg train completed - 22\n", + "Masking Chestnut_Sided_Warbler_0110_164023.jpg train completed - 23\n", + "Masking Chestnut_Sided_Warbler_0052_163728.jpg train completed - 24\n", + "Masking Chestnut_Sided_Warbler_0041_163944.jpg train completed - 25\n", + "Masking Chestnut_Sided_Warbler_0057_163957.jpg train completed - 26\n", + "Masking Chestnut_Sided_Warbler_0001_163813.jpg train completed - 27\n", + "Masking Chestnut_Sided_Warbler_0014_163801.jpg train completed - 28\n", + "Masking Chestnut_Sided_Warbler_0071_163784.jpg train completed - 29\n", + "Masking Chestnut_Sided_Warbler_0126_164090.jpg train completed - 30\n", + "Masking Chestnut_Sided_Warbler_0013_163749.jpg train completed - 31\n", + "Masking Chestnut_Sided_Warbler_0125_164247.jpg train completed - 32\n", + "Masking Chestnut_Sided_Warbler_0128_163696.jpg train completed - 33\n", + "Masking Chestnut_Sided_Warbler_0068_164184.jpg train completed - 34\n", + "Masking Chestnut_Sided_Warbler_0069_163921.jpg train completed - 35\n", + "Masking Chestnut_Sided_Warbler_0034_163911.jpg train completed - 36\n", + "Masking Chestnut_Sided_Warbler_0089_163776.jpg valid completed - 1\n", + "Masking Chestnut_Sided_Warbler_0030_163847.jpg valid completed - 2\n", + "Masking Chestnut_Sided_Warbler_0097_163750.jpg valid completed - 3\n", + "Masking Chestnut_Sided_Warbler_0044_163975.jpg valid completed - 4\n", + "Masking Chestnut_Sided_Warbler_0108_164356.jpg valid completed - 5\n", + "Masking Chestnut_Sided_Warbler_0124_164109.jpg valid completed - 6\n", + "Masking Chestnut_Sided_Warbler_0086_164024.jpg valid completed - 7\n", + "Masking Chestnut_Sided_Warbler_0016_164060.jpg valid completed - 8\n", + "Masking Chestnut_Sided_Warbler_0006_163858.jpg valid completed - 9\n", + "Masking Chestnut_Sided_Warbler_0105_163996.jpg valid completed - 10\n", + "Masking Chestnut_Sided_Warbler_0121_164125.jpg valid completed - 11\n", + "Masking Chestnut_Sided_Warbler_0094_164152.jpg valid completed - 12\n", + "Masking Chestnut_Sided_Warbler_0033_163607.jpg test completed - 1\n", + "Masking Chestnut_Sided_Warbler_0037_164233.jpg test completed - 2\n", + "Masking Chestnut_Sided_Warbler_0075_164231.jpg test completed - 3\n", + "Masking Chestnut_Sided_Warbler_0081_163854.jpg test completed - 4\n", + "Masking Chestnut_Sided_Warbler_0038_164354.jpg test completed - 5\n", + "Masking Chestnut_Sided_Warbler_0062_163859.jpg test completed - 6\n", + "Masking Chestnut_Sided_Warbler_0096_163672.jpg test completed - 7\n", + "Masking Chestnut_Sided_Warbler_0107_164040.jpg test completed - 8\n", + "Masking Chestnut_Sided_Warbler_0103_163669.jpg test completed - 9\n", + "Masking Chestnut_Sided_Warbler_0117_164066.jpg test completed - 10\n", + "Masking Chestnut_Sided_Warbler_0049_163735.jpg test completed - 11\n", + "Masking Chestnut_Sided_Warbler_0101_164324.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/166.Golden_winged_Warbler\n", + "Train indexes: [36 25 58 18 30 2 32 12 15 19 21 1 11 39 27 49 45 52 57 51 48 31 33 10\n", + " 37 38 55 53 13 54 41 9 22 44 7]\n", + "Validation indexes: [42 40 43 47 34 26 14 50 29 16 24 6]\n", + "Test indexes: [ 4 5 17 3 0 46 8 23 20 28 35 56]\n", + "Masking Golden_Winged_Warbler_0062_794850.jpg train completed - 1\n", + "Masking Golden_Winged_Warbler_0044_794836.jpg train completed - 2\n", + "Masking Golden_Winged_Warbler_0095_794809.jpg train completed - 3\n", + "Masking Golden_Winged_Warbler_0036_794817.jpg train completed - 4\n", + "Masking Golden_Winged_Warbler_0052_164529.jpg train completed - 5\n", + "Masking Golden_Winged_Warbler_0009_794813.jpg train completed - 6\n", + "Masking Golden_Winged_Warbler_0056_794845.jpg train completed - 7\n", + "Masking Golden_Winged_Warbler_0024_794807.jpg train completed - 8\n", + "Masking Golden_Winged_Warbler_0032_794849.jpg train completed - 9\n", + "Masking Golden_Winged_Warbler_0037_794839.jpg train completed - 10\n", + "Masking Golden_Winged_Warbler_0040_164605.jpg train completed - 11\n", + "Masking Golden_Winged_Warbler_0004_164470.jpg train completed - 12\n", + "Masking Golden_Winged_Warbler_0022_794833.jpg train completed - 13\n", + "Masking Golden_Winged_Warbler_0067_794846.jpg train completed - 14\n", + "Masking Golden_Winged_Warbler_0049_164509.jpg train completed - 15\n", + "Masking Golden_Winged_Warbler_0081_164487.jpg train completed - 16\n", + "Masking Golden_Winged_Warbler_0076_164591.jpg train completed - 17\n", + "Masking Golden_Winged_Warbler_0084_164541.jpg train completed - 18\n", + "Masking Golden_Winged_Warbler_0092_164465.jpg train completed - 19\n", + "Masking Golden_Winged_Warbler_0083_794801.jpg train completed - 20\n", + "Masking Golden_Winged_Warbler_0079_794820.jpg train completed - 21\n", + "Masking Golden_Winged_Warbler_0054_794847.jpg train completed - 22\n", + "Masking Golden_Winged_Warbler_0059_794855.jpg train completed - 23\n", + "Masking Golden_Winged_Warbler_0021_794821.jpg train completed - 24\n", + "Masking Golden_Winged_Warbler_0065_794842.jpg train completed - 25\n", + "Masking Golden_Winged_Warbler_0066_794803.jpg train completed - 26\n", + "Masking Golden_Winged_Warbler_0090_794830.jpg train completed - 27\n", + "Masking Golden_Winged_Warbler_0086_794844.jpg train completed - 28\n", + "Masking Golden_Winged_Warbler_0030_164462.jpg train completed - 29\n", + "Masking Golden_Winged_Warbler_0087_794810.jpg train completed - 30\n", + "Masking Golden_Winged_Warbler_0069_164456.jpg train completed - 31\n", + "Masking Golden_Winged_Warbler_0018_164558.jpg train completed - 32\n", + "Masking Golden_Winged_Warbler_0041_164379.jpg train completed - 33\n", + "Masking Golden_Winged_Warbler_0075_794848.jpg train completed - 34\n", + "Masking Golden_Winged_Warbler_0015_794819.jpg train completed - 35\n", + "Masking Golden_Winged_Warbler_0070_164460.jpg valid completed - 1\n", + "Masking Golden_Winged_Warbler_0068_794825.jpg valid completed - 2\n", + "Masking Golden_Winged_Warbler_0071_164370.jpg valid completed - 3\n", + "Masking Golden_Winged_Warbler_0078_794827.jpg valid completed - 4\n", + "Masking Golden_Winged_Warbler_0060_164368.jpg valid completed - 5\n", + "Masking Golden_Winged_Warbler_0046_794828.jpg valid completed - 6\n", + "Masking Golden_Winged_Warbler_0031_794840.jpg valid completed - 7\n", + "Masking Golden_Winged_Warbler_0082_164544.jpg valid completed - 8\n", + "Masking Golden_Winged_Warbler_0051_794805.jpg valid completed - 9\n", + "Masking Golden_Winged_Warbler_0034_794800.jpg valid completed - 10\n", + "Masking Golden_Winged_Warbler_0043_164476.jpg valid completed - 11\n", + "Masking Golden_Winged_Warbler_0014_164464.jpg valid completed - 12\n", + "Masking Golden_Winged_Warbler_0012_164496.jpg test completed - 1\n", + "Masking Golden_Winged_Warbler_0013_794837.jpg test completed - 2\n", + "Masking 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6\n", + "Masking Kentucky_Warbler_0010_795912.jpg test completed - 7\n", + "Masking Kentucky_Warbler_0078_795889.jpg test completed - 8\n", + "Masking Kentucky_Warbler_0051_795884.jpg test completed - 9\n", + "Masking Kentucky_Warbler_0065_795899.jpg test completed - 10\n", + "Masking Kentucky_Warbler_0063_795904.jpg test completed - 11\n", + "Masking Kentucky_Warbler_0022_795882.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/169.Magnolia_Warbler\n", + "Train indexes: [48 24 33 47 27 20 16 28 7 31 4 18 3 17 55 15 38 42 29 34 5 2 8 32\n", + " 43 53 19 13 51 52 6 23 21 41 39]\n", + "Validation indexes: [46 0 9 44 14 37 54 58 30 56 26 49]\n", + "Test indexes: [35 11 36 12 25 22 1 40 50 10 57 45]\n", + "Masking Magnolia_Warbler_0113_166401.jpg train completed - 1\n", + "Masking Magnolia_Warbler_0051_165646.jpg train completed - 2\n", + "Masking Magnolia_Warbler_0072_165534.jpg train completed - 3\n", + "Masking Magnolia_Warbler_0112_166406.jpg train completed - 4\n", + 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train completed - 33\n", + "Masking Magnolia_Warbler_0102_165884.jpg train completed - 34\n", + "Masking Magnolia_Warbler_0095_166098.jpg train completed - 35\n", + "Masking Magnolia_Warbler_0111_165478.jpg valid completed - 1\n", + "Masking Magnolia_Warbler_0001_166266.jpg valid completed - 2\n", + "Masking Magnolia_Warbler_0026_165498.jpg valid completed - 3\n", + "Masking Magnolia_Warbler_0105_165661.jpg valid completed - 4\n", + "Masking Magnolia_Warbler_0039_165532.jpg valid completed - 5\n", + "Masking Magnolia_Warbler_0090_166087.jpg valid completed - 6\n", + "Masking Magnolia_Warbler_0134_165801.jpg valid completed - 7\n", + "Masking Magnolia_Warbler_0140_165543.jpg valid completed - 8\n", + "Masking Magnolia_Warbler_0064_165471.jpg valid completed - 9\n", + "Masking Magnolia_Warbler_0137_165507.jpg valid completed - 10\n", + "Masking Magnolia_Warbler_0053_165682.jpg valid completed - 11\n", + "Masking Magnolia_Warbler_0114_165467.jpg valid completed - 12\n", + "Masking Magnolia_Warbler_0077_165674.jpg test completed - 1\n", + "Masking Magnolia_Warbler_0030_165782.jpg test completed - 2\n", + "Masking Magnolia_Warbler_0079_165783.jpg test completed - 3\n", + "Masking Magnolia_Warbler_0032_165960.jpg test completed - 4\n", + "Masking Magnolia_Warbler_0052_165474.jpg test completed - 5\n", + "Masking Magnolia_Warbler_0047_165900.jpg test completed - 6\n", + "Masking Magnolia_Warbler_0004_165535.jpg test completed - 7\n", + "Masking Magnolia_Warbler_0097_165455.jpg test completed - 8\n", + "Masking Magnolia_Warbler_0120_165462.jpg test completed - 9\n", + "Masking Magnolia_Warbler_0029_165567.jpg test completed - 10\n", + "Masking Magnolia_Warbler_0139_166081.jpg test completed - 11\n", + "Masking Magnolia_Warbler_0106_165689.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/170.Mourning_Warbler\n", + "Train indexes: [39 24 57 33 51 21 5 1 34 10 44 0 7 30 50 36 17 41 35 8 56 40 45 6\n", + " 52 32 31 2 14 18 26 58 54 43 19 4]\n", + "Validation indexes: [37 28 16 38 48 47 55 12 42 46 15 53]\n", + "Test indexes: [23 3 22 49 25 20 11 59 9 29 27 13]\n", + "Masking Mourning_Warbler_0055_166436.jpg train completed - 1\n", + "Masking Mourning_Warbler_0037_795381.jpg train completed - 2\n", + "Masking Mourning_Warbler_0081_166434.jpg train completed - 3\n", + "Masking Mourning_Warbler_0049_166469.jpg train completed - 4\n", + "Masking Mourning_Warbler_0072_795379.jpg train completed - 5\n", + "Masking Mourning_Warbler_0034_795384.jpg train completed - 6\n", + "Masking Mourning_Warbler_0009_166429.jpg train completed - 7\n", + "Masking Mourning_Warbler_0005_795372.jpg train completed - 8\n", + "Masking Mourning_Warbler_0050_795343.jpg train completed - 9\n", + "Masking Mourning_Warbler_0018_795386.jpg train completed - 10\n", + "Masking Mourning_Warbler_0061_166598.jpg train completed - 11\n", + "Masking Mourning_Warbler_0002_166520.jpg train completed - 12\n", + "Masking Mourning_Warbler_0012_166515.jpg train completed - 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completed - 12\n", + "/kaggle/working/CUB_200_2011/images/174.Palm_Warbler\n", + "Train indexes: [15 33 5 27 18 32 7 34 37 1 44 14 24 16 36 45 41 43 54 57 12 19 2 35\n", + " 22 3 55 50 21 52 4 9 46 56 11 38]\n", + "Validation indexes: [23 53 49 20 28 25 8 13 40 59 39 0]\n", + "Test indexes: [51 29 10 17 26 58 47 42 6 48 31 30]\n", + "Masking Palm_Warbler_0039_170894.jpg train completed - 1\n", + "Masking Palm_Warbler_0077_169042.jpg train completed - 2\n", + "Masking Palm_Warbler_0006_169429.jpg train completed - 3\n", + "Masking Palm_Warbler_0061_169954.jpg train completed - 4\n", + "Masking Palm_Warbler_0046_169837.jpg train completed - 5\n", + "Masking Palm_Warbler_0075_168751.jpg train completed - 6\n", + "Masking Palm_Warbler_0010_169452.jpg train completed - 7\n", + "Masking Palm_Warbler_0081_169256.jpg train completed - 8\n", + "Masking Palm_Warbler_0086_169676.jpg train completed - 9\n", + "Masking Palm_Warbler_0002_169527.jpg train completed - 10\n", + "Masking 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valid completed - 5\n", + "Masking Palm_Warbler_0056_168740.jpg valid completed - 6\n", + "Masking Palm_Warbler_0012_170857.jpg valid completed - 7\n", + "Masking Palm_Warbler_0028_169670.jpg valid completed - 8\n", + "Masking Palm_Warbler_0093_170499.jpg valid completed - 9\n", + "Masking Palm_Warbler_0136_170276.jpg valid completed - 10\n", + "Masking Palm_Warbler_0092_170604.jpg valid completed - 11\n", + "Masking Palm_Warbler_0001_170297.jpg valid completed - 12\n", + "Masking Palm_Warbler_0112_169595.jpg test completed - 1\n", + "Masking Palm_Warbler_0067_169318.jpg test completed - 2\n", + "Masking Palm_Warbler_0015_169626.jpg test completed - 3\n", + "Masking Palm_Warbler_0044_169319.jpg test completed - 4\n", + "Masking Palm_Warbler_0060_168686.jpg test completed - 5\n", + "Masking Palm_Warbler_0134_168943.jpg test completed - 6\n", + "Masking Palm_Warbler_0107_170620.jpg test completed - 7\n", + "Masking Palm_Warbler_0096_170867.jpg test completed - 8\n", + "Masking Palm_Warbler_0007_169473.jpg test completed - 9\n", + "Masking Palm_Warbler_0108_169426.jpg test completed - 10\n", + "Masking Palm_Warbler_0073_169781.jpg test completed - 11\n", + "Masking Palm_Warbler_0068_170243.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/175.Pine_Warbler\n", + "Train indexes: [ 2 44 12 58 40 21 33 57 48 4 59 37 0 5 6 10 34 39 32 1 22 45 36 38\n", + " 28 43 52 35 11 25 19 42 41 13 18 15]\n", + "Validation indexes: [56 17 16 14 51 20 31 24 54 3 7 9]\n", + "Test indexes: [27 8 46 30 47 50 53 55 23 26 49 29]\n", + "Masking Pine_Warbler_0004_171215.jpg train completed - 1\n", + "Masking Pine_Warbler_0105_170983.jpg train completed - 2\n", + "Masking Pine_Warbler_0025_171110.jpg train completed - 3\n", + "Masking Pine_Warbler_0136_171382.jpg train completed - 4\n", + "Masking Pine_Warbler_0101_171501.jpg train completed - 5\n", + "Masking Pine_Warbler_0046_171452.jpg train completed - 6\n", + "Masking Pine_Warbler_0083_171117.jpg train completed - 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Pine_Warbler_0038_171386.jpg valid completed - 2\n", + "Masking Pine_Warbler_0037_171649.jpg valid completed - 3\n", + "Masking Pine_Warbler_0033_171657.jpg valid completed - 4\n", + "Masking Pine_Warbler_0120_172340.jpg valid completed - 5\n", + "Masking Pine_Warbler_0045_171150.jpg valid completed - 6\n", + "Masking Pine_Warbler_0078_171374.jpg valid completed - 7\n", + "Masking Pine_Warbler_0054_171287.jpg valid completed - 8\n", + "Masking Pine_Warbler_0127_171742.jpg valid completed - 9\n", + "Masking Pine_Warbler_0005_171548.jpg valid completed - 10\n", + "Masking Pine_Warbler_0010_171239.jpg valid completed - 11\n", + "Masking Pine_Warbler_0020_171989.jpg valid completed - 12\n", + "Masking Pine_Warbler_0065_172119.jpg test completed - 1\n", + "Masking Pine_Warbler_0017_171678.jpg test completed - 2\n", + "Masking Pine_Warbler_0108_170597.jpg test completed - 3\n", + "Masking Pine_Warbler_0076_172104.jpg test completed - 4\n", + "Masking Pine_Warbler_0111_171040.jpg test completed - 5\n", + "Masking Pine_Warbler_0119_171551.jpg test completed - 6\n", + "Masking Pine_Warbler_0126_171282.jpg test completed - 7\n", + "Masking Pine_Warbler_0129_171035.jpg test completed - 8\n", + "Masking Pine_Warbler_0052_171380.jpg test completed - 9\n", + "Masking Pine_Warbler_0060_171635.jpg test completed - 10\n", + "Masking Pine_Warbler_0118_171152.jpg test completed - 11\n", + "Masking Pine_Warbler_0075_171933.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/176.Prairie_Warbler\n", + "Train indexes: [ 8 13 53 6 12 39 50 31 3 56 28 51 11 35 48 26 23 59 42 46 49 37 45 15\n", + " 17 33 54 29 9 43 47 30 0 2 36 57]\n", + "Validation indexes: [40 41 19 16 4 10 20 27 7 32 1 22]\n", + "Test indexes: [52 55 44 25 5 21 24 34 38 18 58 14]\n", + "Masking Prairie_Warbler_0025_165306.jpg train completed - 1\n", + "Masking Prairie_Warbler_0034_172549.jpg train completed - 2\n", + "Masking Prairie_Warbler_0130_172609.jpg train completed - 3\n", + "Masking 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18\n", + "Masking Prairie_Warbler_0108_172559.jpg train completed - 19\n", + "Masking Prairie_Warbler_0115_172689.jpg train completed - 20\n", + "Masking Prairie_Warbler_0123_172603.jpg train completed - 21\n", + "Masking Prairie_Warbler_0096_172577.jpg train completed - 22\n", + "Masking Prairie_Warbler_0113_172544.jpg train completed - 23\n", + "Masking Prairie_Warbler_0040_173056.jpg train completed - 24\n", + "Masking Prairie_Warbler_0048_173095.jpg train completed - 25\n", + "Masking Prairie_Warbler_0082_172783.jpg train completed - 26\n", + "Masking Prairie_Warbler_0132_172705.jpg train completed - 27\n", + "Masking Prairie_Warbler_0075_172709.jpg train completed - 28\n", + "Masking Prairie_Warbler_0027_172465.jpg train completed - 29\n", + "Masking Prairie_Warbler_0109_172909.jpg train completed - 30\n", + "Masking Prairie_Warbler_0118_173261.jpg train completed - 31\n", + "Masking Prairie_Warbler_0078_172729.jpg train completed - 32\n", + "Masking Prairie_Warbler_0002_172622.jpg train completed - 33\n", + "Masking Prairie_Warbler_0009_173022.jpg train completed - 34\n", + "Masking Prairie_Warbler_0094_91950.jpg train completed - 35\n", + "Masking Prairie_Warbler_0136_172768.jpg train completed - 36\n", + "Masking Prairie_Warbler_0106_173071.jpg valid completed - 1\n", + "Masking Prairie_Warbler_0107_173080.jpg valid completed - 2\n", + "Masking Prairie_Warbler_0051_172585.jpg valid completed - 3\n", + "Masking Prairie_Warbler_0047_173210.jpg valid completed - 4\n", + "Masking Prairie_Warbler_0011_172744.jpg valid completed - 5\n", + "Masking Prairie_Warbler_0028_173123.jpg valid completed - 6\n", + "Masking Prairie_Warbler_0052_173400.jpg valid completed - 7\n", + "Masking Prairie_Warbler_0071_173140.jpg valid completed - 8\n", + "Masking Prairie_Warbler_0021_172902.jpg valid completed - 9\n", + "Masking Prairie_Warbler_0080_172724.jpg valid completed - 10\n", + "Masking Prairie_Warbler_0007_173081.jpg valid completed - 11\n", + "Masking Prairie_Warbler_0054_172602.jpg valid completed - 12\n", + "Masking Prairie_Warbler_0127_172913.jpg test completed - 1\n", + "Masking Prairie_Warbler_0133_173279.jpg test completed - 2\n", + "Masking Prairie_Warbler_0112_173383.jpg test completed - 3\n", + "Masking Prairie_Warbler_0063_172682.jpg test completed - 4\n", + "Masking Prairie_Warbler_0014_172542.jpg test completed - 5\n", + "Masking Prairie_Warbler_0053_173290.jpg test completed - 6\n", + "Masking Prairie_Warbler_0062_172755.jpg test completed - 7\n", + "Masking Prairie_Warbler_0086_172534.jpg test completed - 8\n", + "Masking Prairie_Warbler_0104_172615.jpg test completed - 9\n", + "Masking Prairie_Warbler_0050_173281.jpg test completed - 10\n", + "Masking Prairie_Warbler_0137_172610.jpg test completed - 11\n", + "Masking Prairie_Warbler_0037_172550.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/177.Prothonotary_Warbler\n", + "Train indexes: [40 1 11 9 4 53 16 58 59 10 45 41 31 37 18 42 25 38 33 12 54 19 21 50\n", + " 24 13 49 0 36 8 20 56 6 43 44 51]\n", + "Validation indexes: [27 46 15 39 5 3 22 7 32 30 52 29]\n", + "Test indexes: [28 23 35 34 17 57 14 55 47 48 2 26]\n", + "Masking Prothonotary_Warbler_0089_174636.jpg train completed - 1\n", + "Masking Prothonotary_Warbler_0008_173425.jpg train completed - 2\n", + "Masking Prothonotary_Warbler_0038_173867.jpg train completed - 3\n", + "Masking Prothonotary_Warbler_0033_174123.jpg train completed - 4\n", + "Masking Prothonotary_Warbler_0020_174122.jpg train completed - 5\n", + "Masking Prothonotary_Warbler_0110_173857.jpg train completed - 6\n", + "Masking Prothonotary_Warbler_0049_174213.jpg train completed - 7\n", + "Masking Prothonotary_Warbler_0124_173686.jpg train completed - 8\n", + "Masking Prothonotary_Warbler_0127_174149.jpg train completed - 9\n", + "Masking Prothonotary_Warbler_0037_173418.jpg train completed - 10\n", + "Masking Prothonotary_Warbler_0098_173913.jpg train completed - 11\n", + "Masking Prothonotary_Warbler_0090_174183.jpg train completed - 12\n", + "Masking Prothonotary_Warbler_0074_173443.jpg train completed - 13\n", + "Masking Prothonotary_Warbler_0084_173939.jpg train completed - 14\n", + "Masking Prothonotary_Warbler_0055_173419.jpg train completed - 15\n", + "Masking Prothonotary_Warbler_0094_173607.jpg train completed - 16\n", + "Masking Prothonotary_Warbler_0065_173422.jpg train completed - 17\n", + "Masking Prothonotary_Warbler_0085_173829.jpg train completed - 18\n", + "Masking Prothonotary_Warbler_0079_173899.jpg train completed - 19\n", + "Masking Prothonotary_Warbler_0040_173447.jpg train completed - 20\n", + "Masking Prothonotary_Warbler_0112_174594.jpg train completed - 21\n", + "Masking Prothonotary_Warbler_0057_173865.jpg train completed - 22\n", + "Masking Prothonotary_Warbler_0059_173434.jpg train completed - 23\n", + "Masking Prothonotary_Warbler_0107_173921.jpg train completed - 24\n", + "Masking Prothonotary_Warbler_0064_174106.jpg train completed - 25\n", + "Masking Prothonotary_Warbler_0045_173536.jpg train completed - 26\n", + "Masking Prothonotary_Warbler_0106_174221.jpg train completed - 27\n", + "Masking Prothonotary_Warbler_0004_173475.jpg train completed - 28\n", + "Masking Prothonotary_Warbler_0083_173929.jpg train completed - 29\n", + "Masking Prothonotary_Warbler_0031_174578.jpg train completed - 30\n", + "Masking Prothonotary_Warbler_0058_173440.jpg train completed - 31\n", + "Masking Prothonotary_Warbler_0117_174622.jpg train completed - 32\n", + "Masking Prothonotary_Warbler_0022_174138.jpg train completed - 33\n", + "Masking Prothonotary_Warbler_0096_174372.jpg train completed - 34\n", + "Masking Prothonotary_Warbler_0097_174554.jpg train completed - 35\n", + "Masking Prothonotary_Warbler_0108_174125.jpg train completed - 36\n", + "Masking Prothonotary_Warbler_0069_174210.jpg valid completed - 1\n", + "Masking Prothonotary_Warbler_0100_174539.jpg valid completed - 2\n", + "Masking Prothonotary_Warbler_0047_174340.jpg valid completed - 3\n", + "Masking Prothonotary_Warbler_0088_173606.jpg valid completed - 4\n", + "Masking Prothonotary_Warbler_0021_173816.jpg valid completed - 5\n", + "Masking Prothonotary_Warbler_0019_173838.jpg valid completed - 6\n", + "Masking Prothonotary_Warbler_0062_174412.jpg valid completed - 7\n", + "Masking Prothonotary_Warbler_0023_173788.jpg valid completed - 8\n", + "Masking Prothonotary_Warbler_0076_174118.jpg valid completed - 9\n", + "Masking Prothonotary_Warbler_0073_174607.jpg valid completed - 10\n", + "Masking Prothonotary_Warbler_0109_174121.jpg valid completed - 11\n", + "Masking Prothonotary_Warbler_0071_173690.jpg valid completed - 12\n", + "Masking Prothonotary_Warbler_0070_174650.jpg test completed - 1\n", + "Masking Prothonotary_Warbler_0063_173830.jpg test completed - 2\n", + "Masking Prothonotary_Warbler_0082_173970.jpg test completed - 3\n", + "Masking Prothonotary_Warbler_0081_173510.jpg test completed - 4\n", + "Masking Prothonotary_Warbler_0054_174556.jpg test completed - 5\n", + "Masking Prothonotary_Warbler_0121_174643.jpg test completed - 6\n", + "Masking Prothonotary_Warbler_0046_174104.jpg test completed - 7\n", + "Masking Prothonotary_Warbler_0116_173444.jpg test completed - 8\n", + "Masking Prothonotary_Warbler_0102_174595.jpg test completed - 9\n", + "Masking Prothonotary_Warbler_0105_174097.jpg test completed - 10\n", + "Masking Prothonotary_Warbler_0018_174196.jpg test completed - 11\n", + "Masking Prothonotary_Warbler_0066_174318.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/178.Swainson_Warbler\n", + "Train indexes: [33 26 5 6 55 17 50 49 25 20 47 15 7 40 24 3 11 29 51 28 23 14 54 10\n", + " 53 34 2 4 48 8 35 0 1]\n", + "Validation indexes: [19 45 52 36 31 18 43 32 16 12 44]\n", + "Test indexes: [21 41 38 37 22 9 39 13 30 42 27 46]\n", + "Masking Swainson_Warbler_0034_794890.jpg train completed - 1\n", + "Masking Swainson_Warbler_0027_794877.jpg train completed - 2\n", + "Masking Swainson_Warbler_0006_794857.jpg train completed - 3\n", + "Masking Swainson_Warbler_0007_174745.jpg train completed - 4\n", + "Masking Swainson_Warbler_0056_794871.jpg train completed - 5\n", + "Masking Swainson_Warbler_0018_174715.jpg train completed - 6\n", + "Masking Swainson_Warbler_0051_794900.jpg train completed - 7\n", + "Masking Swainson_Warbler_0050_794861.jpg train completed - 8\n", + "Masking Swainson_Warbler_0026_794884.jpg train completed - 9\n", + "Masking Swainson_Warbler_0021_794898.jpg train completed - 10\n", + "Masking Swainson_Warbler_0048_794887.jpg train completed - 11\n", + "Masking Swainson_Warbler_0016_174705.jpg train completed - 12\n", + "Masking Swainson_Warbler_0008_794886.jpg train completed - 13\n", + "Masking Swainson_Warbler_0041_794893.jpg train completed - 14\n", + "Masking Swainson_Warbler_0025_794881.jpg train completed - 15\n", + "Masking Swainson_Warbler_0004_794874.jpg train completed - 16\n", + "Masking Swainson_Warbler_0012_174739.jpg train completed - 17\n", + "Masking Swainson_Warbler_0030_794864.jpg train completed - 18\n", + "Masking Swainson_Warbler_0052_794875.jpg train completed - 19\n", + "Masking Swainson_Warbler_0029_794883.jpg train completed - 20\n", + "Masking Swainson_Warbler_0024_794885.jpg train completed - 21\n", + "Masking Swainson_Warbler_0015_794891.jpg train completed - 22\n", + "Masking Swainson_Warbler_0055_794899.jpg train completed - 23\n", + "Masking Swainson_Warbler_0011_174680.jpg train completed - 24\n", + "Masking Swainson_Warbler_0054_174689.jpg train completed - 25\n", + "Masking Swainson_Warbler_0035_174741.jpg train completed - 26\n", + "Masking Swainson_Warbler_0003_794866.jpg train completed - 27\n", + "Masking Swainson_Warbler_0005_794888.jpg train completed - 28\n", + "Masking Swainson_Warbler_0049_794880.jpg train completed - 29\n", + "Masking Swainson_Warbler_0009_174746.jpg train completed - 30\n", + "Masking Swainson_Warbler_0036_174699.jpg train completed - 31\n", + "Masking Swainson_Warbler_0001_794869.jpg train completed - 32\n", + "Masking Swainson_Warbler_0002_174683.jpg train completed - 33\n", + "Masking Swainson_Warbler_0020_794863.jpg valid completed - 1\n", + "Masking Swainson_Warbler_0046_794872.jpg valid completed - 2\n", + "Masking Swainson_Warbler_0053_174678.jpg valid completed - 3\n", + "Masking Swainson_Warbler_0037_174691.jpg valid completed - 4\n", + "Masking Swainson_Warbler_0032_174728.jpg valid completed - 5\n", + "Masking Swainson_Warbler_0019_174692.jpg valid completed - 6\n", + "Masking Swainson_Warbler_0044_794894.jpg valid completed - 7\n", + "Masking Swainson_Warbler_0033_794856.jpg valid completed - 8\n", + "Masking Swainson_Warbler_0017_174685.jpg valid completed - 9\n", + "Masking Swainson_Warbler_0013_794892.jpg valid completed - 10\n", + "Masking Swainson_Warbler_0045_794876.jpg valid completed - 11\n", + "Masking Swainson_Warbler_0022_794868.jpg test completed - 1\n", + "Masking Swainson_Warbler_0042_794895.jpg test completed - 2\n", + "Masking Swainson_Warbler_0039_794859.jpg test completed - 3\n", + "Masking Swainson_Warbler_0038_794882.jpg test completed - 4\n", + "Masking Swainson_Warbler_0023_174717.jpg test completed - 5\n", + "Masking Swainson_Warbler_0010_174724.jpg test completed - 6\n", + "Masking Swainson_Warbler_0040_794860.jpg test completed - 7\n", + "Masking Swainson_Warbler_0014_794858.jpg test completed - 8\n", + "Masking Swainson_Warbler_0031_174696.jpg test completed - 9\n", + "Masking Swainson_Warbler_0043_794865.jpg test completed - 10\n", + "Masking Swainson_Warbler_0028_794896.jpg test completed - 11\n", + "Masking Swainson_Warbler_0047_794870.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/179.Tennessee_Warbler\n", + "Train indexes: [41 20 31 53 34 13 9 14 49 46 42 54 23 33 7 30 24 52 56 3 28 29 15 22\n", + " 6 17 38 1 26 58 36 25 2 51 27]\n", + "Validation indexes: [ 0 35 10 18 39 8 37 4 47 57 45 50]\n", + "Test indexes: [19 48 5 43 40 21 12 11 16 44 32 55]\n", + "Masking Tennessee_Warbler_0070_174962.jpg train completed - 1\n", + "Masking Tennessee_Warbler_0038_174760.jpg train completed - 2\n", + "Masking Tennessee_Warbler_0050_174851.jpg train completed - 3\n", + "Masking Tennessee_Warbler_0091_175050.jpg train completed - 4\n", + "Masking Tennessee_Warbler_0054_174914.jpg train completed - 5\n", + "Masking Tennessee_Warbler_0025_174973.jpg train completed - 6\n", + "Masking Tennessee_Warbler_0021_174761.jpg train completed - 7\n", + "Masking Tennessee_Warbler_0026_175118.jpg train completed - 8\n", + "Masking Tennessee_Warbler_0085_156530.jpg train completed - 9\n", + "Masking Tennessee_Warbler_0080_167714.jpg train completed - 10\n", + "Masking Tennessee_Warbler_0071_175084.jpg train completed - 11\n", + "Masking Tennessee_Warbler_0092_174810.jpg train completed - 12\n", + "Masking Tennessee_Warbler_0041_174900.jpg train completed - 13\n", + "Masking Tennessee_Warbler_0052_175089.jpg train completed - 14\n", + "Masking Tennessee_Warbler_0015_174847.jpg train completed - 15\n", + "Masking Tennessee_Warbler_0049_175006.jpg train completed - 16\n", + "Masking Tennessee_Warbler_0042_175092.jpg train completed - 17\n", + "Masking Tennessee_Warbler_0089_174764.jpg train completed - 18\n", + "Masking Tennessee_Warbler_0096_174993.jpg train completed - 19\n", + "Masking Tennessee_Warbler_0007_174796.jpg train completed - 20\n", + "Masking Tennessee_Warbler_0046_174798.jpg train completed - 21\n", + "Masking Tennessee_Warbler_0048_175079.jpg train completed - 22\n", + "Masking Tennessee_Warbler_0027_174805.jpg train completed - 23\n", + "Masking Tennessee_Warbler_0040_174933.jpg train completed - 24\n", + "Masking Tennessee_Warbler_0014_175126.jpg train completed - 25\n", + "Masking Tennessee_Warbler_0031_174802.jpg train completed - 26\n", + "Masking Tennessee_Warbler_0063_168561.jpg train completed - 27\n", + "Masking Tennessee_Warbler_0003_175154.jpg train completed - 28\n", + "Masking Tennessee_Warbler_0044_174824.jpg train completed - 29\n", + "Masking Tennessee_Warbler_0100_175168.jpg train completed - 30\n", + "Masking Tennessee_Warbler_0061_174775.jpg train completed - 31\n", + "Masking Tennessee_Warbler_0043_175003.jpg train completed - 32\n", + "Masking Tennessee_Warbler_0004_174997.jpg train completed - 33\n", + "Masking Tennessee_Warbler_0088_175163.jpg train completed - 34\n", + "Masking Tennessee_Warbler_0045_174913.jpg train completed - 35\n", + "Masking Tennessee_Warbler_0002_174884.jpg valid completed - 1\n", + "Masking Tennessee_Warbler_0060_174840.jpg valid completed - 2\n", + "Masking Tennessee_Warbler_0022_174799.jpg valid completed - 3\n", + "Masking Tennessee_Warbler_0033_174772.jpg valid completed - 4\n", + "Masking Tennessee_Warbler_0065_174757.jpg valid completed - 5\n", + "Masking Tennessee_Warbler_0019_174786.jpg valid completed - 6\n", + "Masking Tennessee_Warbler_0062_174949.jpg valid completed - 7\n", + "Masking Tennessee_Warbler_0009_174937.jpg valid completed - 8\n", + "Masking Tennessee_Warbler_0081_174771.jpg valid completed - 9\n", + "Masking Tennessee_Warbler_0098_174800.jpg valid completed - 10\n", + "Masking Tennessee_Warbler_0076_174807.jpg valid completed - 11\n", + "Masking Tennessee_Warbler_0087_174968.jpg valid completed - 12\n", + "Masking Tennessee_Warbler_0034_174854.jpg test completed - 1\n", + "Masking Tennessee_Warbler_0084_174891.jpg test completed - 2\n", + "Masking Tennessee_Warbler_0013_163552.jpg test completed - 3\n", + "Masking Tennessee_Warbler_0074_175058.jpg test completed - 4\n", + "Masking Tennessee_Warbler_0067_174999.jpg test completed - 5\n", + "Masking Tennessee_Warbler_0039_174883.jpg test completed - 6\n", + "Masking Tennessee_Warbler_0024_174980.jpg test completed - 7\n", + "Masking Tennessee_Warbler_0023_174977.jpg test completed - 8\n", + "Masking Tennessee_Warbler_0030_172338.jpg test completed - 9\n", + "Masking Tennessee_Warbler_0075_174763.jpg test completed - 10\n", + "Masking Tennessee_Warbler_0051_175015.jpg test completed - 11\n", + "Masking Tennessee_Warbler_0095_174903.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/180.Wilson_Warbler\n", + "Train indexes: [ 7 38 52 13 36 57 28 55 20 50 59 44 43 22 17 10 5 15 19 35 16 51 14 49\n", + " 32 39 2 48 47 45 33 6 3 42 23 58]\n", + "Validation indexes: [11 56 26 25 9 24 12 4 30 18 21 40]\n", + "Test indexes: [37 0 29 34 54 31 27 8 46 1 53 41]\n", + "Masking Wilson_Warbler_0020_175505.jpg train completed - 1\n", + "Masking Wilson_Warbler_0083_175253.jpg train completed - 2\n", + "Masking Wilson_Warbler_0126_175368.jpg train completed - 3\n", + "Masking Wilson_Warbler_0034_175443.jpg train completed - 4\n", + "Masking Wilson_Warbler_0076_175780.jpg train completed - 5\n", + "Masking Wilson_Warbler_0133_175626.jpg train completed - 6\n", + "Masking Wilson_Warbler_0066_117820.jpg train completed - 7\n", + "Masking Wilson_Warbler_0131_175268.jpg train completed - 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5\n", + "Masking Wilson_Warbler_0070_175169.jpg test completed - 6\n", + "Masking Wilson_Warbler_0065_175924.jpg test completed - 7\n", + "Masking Wilson_Warbler_0024_175278.jpg test completed - 8\n", + "Masking Wilson_Warbler_0108_175179.jpg test completed - 9\n", + "Masking Wilson_Warbler_0007_175618.jpg test completed - 10\n", + "Masking Wilson_Warbler_0127_175637.jpg test completed - 11\n", + "Masking Wilson_Warbler_0095_175595.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/181.Worm_eating_Warbler\n", + "Train indexes: [51 4 39 10 14 35 33 57 8 53 43 0 38 32 46 31 24 16 34 11 20 49 50 26\n", + " 55 48 3 25 42 6 56 19 15 13 30]\n", + "Validation indexes: [ 7 29 1 36 54 21 58 47 9 18 41 5]\n", + "Test indexes: [22 17 40 45 23 52 27 2 12 37 28 44]\n", + "Masking Worm_Eating_Warbler_0087_175978.jpg train completed - 1\n", + "Masking Worm_Eating_Warbler_0011_795566.jpg train completed - 2\n", + "Masking Worm_Eating_Warbler_0067_795520.jpg train completed - 3\n", + 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31\n", + "Masking Worm_Eating_Warbler_0035_795575.jpg train completed - 32\n", + "Masking Worm_Eating_Warbler_0026_176096.jpg train completed - 33\n", + "Masking Worm_Eating_Warbler_0022_176111.jpg train completed - 34\n", + "Masking Worm_Eating_Warbler_0055_795555.jpg train completed - 35\n", + "Masking Worm_Eating_Warbler_0014_176042.jpg valid completed - 1\n", + "Masking Worm_Eating_Warbler_0054_176130.jpg valid completed - 2\n", + "Masking Worm_Eating_Warbler_0004_795550.jpg valid completed - 3\n", + "Masking Worm_Eating_Warbler_0063_795553.jpg valid completed - 4\n", + "Masking Worm_Eating_Warbler_0094_795522.jpg valid completed - 5\n", + "Masking Worm_Eating_Warbler_0039_176120.jpg valid completed - 6\n", + "Masking Worm_Eating_Warbler_0102_176069.jpg valid completed - 7\n", + "Masking Worm_Eating_Warbler_0081_175971.jpg valid completed - 8\n", + "Masking Worm_Eating_Warbler_0016_795528.jpg valid completed - 9\n", + "Masking Worm_Eating_Warbler_0034_795560.jpg valid completed - 10\n", + "Masking Worm_Eating_Warbler_0072_795559.jpg valid completed - 11\n", + "Masking Worm_Eating_Warbler_0012_795515.jpg valid completed - 12\n", + "Masking Worm_Eating_Warbler_0042_795571.jpg test completed - 1\n", + "Masking Worm_Eating_Warbler_0031_176075.jpg test completed - 2\n", + "Masking Worm_Eating_Warbler_0069_176055.jpg test completed - 3\n", + "Masking Worm_Eating_Warbler_0078_795532.jpg test completed - 4\n", + "Masking Worm_Eating_Warbler_0045_795518.jpg test completed - 5\n", + "Masking Worm_Eating_Warbler_0090_795523.jpg test completed - 6\n", + "Masking Worm_Eating_Warbler_0051_176068.jpg test completed - 7\n", + "Masking Worm_Eating_Warbler_0005_175977.jpg test completed - 8\n", + "Masking Worm_Eating_Warbler_0021_795545.jpg test completed - 9\n", + "Masking Worm_Eating_Warbler_0065_795551.jpg test completed - 10\n", + "Masking Worm_Eating_Warbler_0053_176079.jpg test completed - 11\n", + "Masking Worm_Eating_Warbler_0076_176086.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/182.Yellow_Warbler\n", + "Train indexes: [53 35 59 5 6 29 17 47 18 24 58 28 32 10 30 45 40 56 9 49 46 20 0 13\n", + " 23 55 57 51 7 22 8 21 14 39 15 36]\n", + "Validation indexes: [ 4 25 34 16 27 33 2 52 54 50 41 11]\n", + "Test indexes: [48 42 26 3 43 19 1 44 12 31 38 37]\n", + "Masking Yellow_Warbler_0112_176301.jpg train completed - 1\n", + "Masking Yellow_Warbler_0071_176655.jpg train completed - 2\n", + "Masking Yellow_Warbler_0123_176471.jpg train completed - 3\n", + "Masking Yellow_Warbler_0014_176414.jpg train completed - 4\n", + "Masking Yellow_Warbler_0016_176452.jpg train completed - 5\n", + "Masking Yellow_Warbler_0057_176675.jpg train completed - 6\n", + "Masking Yellow_Warbler_0034_176204.jpg train completed - 7\n", + "Masking Yellow_Warbler_0100_176733.jpg train completed - 8\n", + "Masking Yellow_Warbler_0035_176360.jpg train completed - 9\n", + "Masking Yellow_Warbler_0044_176367.jpg train completed - 10\n", + "Masking 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"Masking Yellow_Warbler_0118_176409.jpg train completed - 26\n", + "Masking Yellow_Warbler_0121_176402.jpg train completed - 27\n", + "Masking Yellow_Warbler_0105_176849.jpg train completed - 28\n", + "Masking Yellow_Warbler_0017_176888.jpg train completed - 29\n", + "Masking Yellow_Warbler_0042_176460.jpg train completed - 30\n", + "Masking Yellow_Warbler_0018_176674.jpg train completed - 31\n", + "Masking Yellow_Warbler_0040_176954.jpg train completed - 32\n", + "Masking Yellow_Warbler_0029_176855.jpg train completed - 33\n", + "Masking Yellow_Warbler_0087_176591.jpg train completed - 34\n", + "Masking Yellow_Warbler_0030_176236.jpg train completed - 35\n", + "Masking Yellow_Warbler_0073_176218.jpg train completed - 36\n", + "Masking Yellow_Warbler_0013_176437.jpg valid completed - 1\n", + "Masking Yellow_Warbler_0046_176212.jpg valid completed - 2\n", + "Masking Yellow_Warbler_0069_176346.jpg valid completed - 3\n", + "Masking Yellow_Warbler_0031_176282.jpg valid completed - 4\n", + "Masking Yellow_Warbler_0049_176526.jpg valid completed - 5\n", + "Masking Yellow_Warbler_0067_176856.jpg valid completed - 6\n", + "Masking Yellow_Warbler_0007_176616.jpg valid completed - 7\n", + "Masking Yellow_Warbler_0106_176383.jpg valid completed - 8\n", + "Masking Yellow_Warbler_0114_176201.jpg valid completed - 9\n", + "Masking Yellow_Warbler_0104_176541.jpg valid completed - 10\n", + "Masking Yellow_Warbler_0090_176366.jpg valid completed - 11\n", + "Masking Yellow_Warbler_0025_176189.jpg valid completed - 12\n", + "Masking Yellow_Warbler_0101_176864.jpg test completed - 1\n", + "Masking Yellow_Warbler_0093_176490.jpg test completed - 2\n", + "Masking Yellow_Warbler_0047_176456.jpg test completed - 3\n", + "Masking Yellow_Warbler_0011_176676.jpg test completed - 4\n", + "Masking Yellow_Warbler_0094_176365.jpg test completed - 5\n", + "Masking Yellow_Warbler_0038_176388.jpg test completed - 6\n", + "Masking Yellow_Warbler_0004_176947.jpg test completed - 7\n", + "Masking 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10\n", + "Masking Northern_Waterthrush_0033_177214.jpg valid completed - 11\n", + "Masking Northern_Waterthrush_0098_177116.jpg valid completed - 12\n", + "Masking Northern_Waterthrush_0097_177045.jpg test completed - 1\n", + "Masking Northern_Waterthrush_0103_177162.jpg test completed - 2\n", + "Masking Northern_Waterthrush_0005_177023.jpg test completed - 3\n", + "Masking Northern_Waterthrush_0011_177311.jpg test completed - 4\n", + "Masking Northern_Waterthrush_0044_176961.jpg test completed - 5\n", + "Masking Northern_Waterthrush_0009_177078.jpg test completed - 6\n", + "Masking Northern_Waterthrush_0084_177239.jpg test completed - 7\n", + "Masking Northern_Waterthrush_0021_177187.jpg test completed - 8\n", + "Masking Northern_Waterthrush_0063_177346.jpg test completed - 9\n", + "Masking Northern_Waterthrush_0089_177167.jpg test completed - 10\n", + "Masking Northern_Waterthrush_0058_177143.jpg test completed - 11\n", + "Masking Northern_Waterthrush_0015_177075.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/184.Louisiana_Waterthrush\n", + "Train indexes: [37 29 6 20 58 35 45 27 57 43 11 17 36 0 14 1 24 47 28 39 22 8 44 16\n", + " 15 52 49 53 32 41 26 25 56 38 50 12]\n", + "Validation indexes: [10 30 4 7 3 5 40 18 55 19 34 51]\n", + "Test indexes: [ 2 13 42 23 9 48 46 31 54 21 33 59]\n", + "Masking Louisiana_Waterthrush_0055_795280.jpg train completed - 1\n", + "Masking Louisiana_Waterthrush_0045_795274.jpg train completed - 2\n", + "Masking Louisiana_Waterthrush_0014_795238.jpg train completed - 3\n", + "Masking Louisiana_Waterthrush_0031_177509.jpg train completed - 4\n", + "Masking Louisiana_Waterthrush_0091_795246.jpg train completed - 5\n", + "Masking Louisiana_Waterthrush_0053_795241.jpg train completed - 6\n", + "Masking Louisiana_Waterthrush_0066_795259.jpg train completed - 7\n", + "Masking Louisiana_Waterthrush_0042_177551.jpg train completed - 8\n", + "Masking Louisiana_Waterthrush_0088_795284.jpg train completed - 9\n", + "Masking 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train completed - 23\n", + "Masking Louisiana_Waterthrush_0026_177503.jpg train completed - 24\n", + "Masking Louisiana_Waterthrush_0025_177403.jpg train completed - 25\n", + "Masking Louisiana_Waterthrush_0080_177379.jpg train completed - 26\n", + "Masking Louisiana_Waterthrush_0077_795247.jpg train completed - 27\n", + "Masking Louisiana_Waterthrush_0081_795283.jpg train completed - 28\n", + "Masking Louisiana_Waterthrush_0048_795277.jpg train completed - 29\n", + "Masking Louisiana_Waterthrush_0059_177449.jpg train completed - 30\n", + "Masking Louisiana_Waterthrush_0041_795279.jpg train completed - 31\n", + "Masking Louisiana_Waterthrush_0040_795251.jpg train completed - 32\n", + "Masking Louisiana_Waterthrush_0087_795261.jpg train completed - 33\n", + "Masking Louisiana_Waterthrush_0056_795245.jpg train completed - 34\n", + "Masking Louisiana_Waterthrush_0078_177444.jpg train completed - 35\n", + "Masking Louisiana_Waterthrush_0021_795250.jpg train completed - 36\n", + "Masking 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1\n", + "Masking Bewick_Wren_0032_184870.jpg test completed - 2\n", + "Masking Bewick_Wren_0083_185190.jpg test completed - 3\n", + "Masking Bewick_Wren_0092_184901.jpg test completed - 4\n", + "Masking Bewick_Wren_0104_185256.jpg test completed - 5\n", + "Masking Bewick_Wren_0037_185332.jpg test completed - 6\n", + "Masking Bewick_Wren_0135_185251.jpg test completed - 7\n", + "Masking Bewick_Wren_0124_184771.jpg test completed - 8\n", + "Masking Bewick_Wren_0062_185063.jpg test completed - 9\n", + "Masking Bewick_Wren_0010_185142.jpg test completed - 10\n", + "Masking Bewick_Wren_0106_185013.jpg test completed - 11\n", + "Masking Bewick_Wren_0081_185080.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/194.Cactus_Wren\n", + "Train indexes: [52 10 24 35 59 36 42 55 6 46 20 21 38 27 28 5 40 22 33 18 51 9 15 56\n", + " 45 17 31 29 47 48 1 58 49 32 39 8]\n", + "Validation indexes: [44 23 0 16 14 34 3 12 41 13 2 37]\n", + "Test indexes: [57 54 53 7 4 43 26 19 11 25 30 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11\n", + "Masking Cactus_Wren_0080_185901.jpg valid completed - 12\n", + "Masking Cactus_Wren_0123_186068.jpg test completed - 1\n", + "Masking Cactus_Wren_0117_185591.jpg test completed - 2\n", + "Masking Cactus_Wren_0116_185927.jpg test completed - 3\n", + "Masking Cactus_Wren_0016_185582.jpg test completed - 4\n", + "Masking Cactus_Wren_0008_186083.jpg test completed - 5\n", + "Masking Cactus_Wren_0095_185633.jpg test completed - 6\n", + "Masking Cactus_Wren_0063_185589.jpg test completed - 7\n", + "Masking Cactus_Wren_0042_185514.jpg test completed - 8\n", + "Masking Cactus_Wren_0027_185893.jpg test completed - 9\n", + "Masking Cactus_Wren_0060_185950.jpg test completed - 10\n", + "Masking Cactus_Wren_0069_186033.jpg test completed - 11\n", + "Masking Cactus_Wren_0108_185733.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/195.Carolina_Wren\n", + "Train indexes: [44 39 19 48 54 16 56 2 13 42 45 50 27 40 14 52 1 32 31 47 15 59 22 6\n", + " 26 57 7 38 34 0 3 43 37 25 21 20]\n", + "Validation indexes: [33 5 28 17 36 46 49 8 11 41 9 18]\n", + "Test indexes: [30 55 24 12 23 53 10 4 29 51 35 58]\n", + "Masking Carolina_Wren_0095_186561.jpg train completed - 1\n", + "Masking Carolina_Wren_0082_186421.jpg train completed - 2\n", + "Masking Carolina_Wren_0043_186286.jpg train completed - 3\n", + "Masking Carolina_Wren_0108_186089.jpg train completed - 4\n", + "Masking Carolina_Wren_0125_187009.jpg train completed - 5\n", + "Masking Carolina_Wren_0035_186356.jpg train completed - 6\n", + "Masking Carolina_Wren_0128_186581.jpg train completed - 7\n", + "Masking Carolina_Wren_0005_186109.jpg train completed - 8\n", + "Masking Carolina_Wren_0031_186713.jpg train completed - 9\n", + "Masking Carolina_Wren_0090_186942.jpg train completed - 10\n", + "Masking Carolina_Wren_0099_186237.jpg train completed - 11\n", + "Masking Carolina_Wren_0113_186675.jpg train completed - 12\n", + "Masking Carolina_Wren_0058_186409.jpg train completed - 13\n", + "Masking 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test completed - 11\n", + "Masking Carolina_Wren_0141_186347.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/196.House_Wren\n", + "Train indexes: [17 43 36 56 53 13 15 30 29 1 31 3 9 39 19 40 24 33 0 12 5 47 58 7\n", + " 16 14 55 51 26 11 42 48 4 8 37]\n", + "Validation indexes: [28 34 38 46 44 22 45 50 6 32 18 2]\n", + "Test indexes: [35 27 10 21 20 41 52 54 49 57 25 23]\n", + "Masking House_Wren_0055_187397.jpg train completed - 1\n", + "Masking House_Wren_0114_188016.jpg train completed - 2\n", + "Masking House_Wren_0099_187240.jpg train completed - 3\n", + "Masking House_Wren_0134_188077.jpg train completed - 4\n", + "Masking House_Wren_0129_187376.jpg train completed - 5\n", + "Masking House_Wren_0043_187224.jpg train completed - 6\n", + "Masking House_Wren_0046_187477.jpg train completed - 7\n", + "Masking House_Wren_0090_187762.jpg train completed - 8\n", + "Masking House_Wren_0088_187243.jpg train completed - 9\n", + "Masking House_Wren_0004_187448.jpg train 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"Masking House_Wren_0117_187492.jpg valid completed - 7\n", + "Masking House_Wren_0125_187251.jpg valid completed - 8\n", + "Masking House_Wren_0017_187330.jpg valid completed - 9\n", + "Masking House_Wren_0092_187100.jpg valid completed - 10\n", + "Masking House_Wren_0057_187157.jpg valid completed - 11\n", + "Masking House_Wren_0005_187493.jpg valid completed - 12\n", + "Masking House_Wren_0098_187368.jpg test completed - 1\n", + "Masking House_Wren_0086_187815.jpg test completed - 2\n", + "Masking House_Wren_0034_187120.jpg test completed - 3\n", + "Masking House_Wren_0063_188076.jpg test completed - 4\n", + "Masking House_Wren_0062_187194.jpg test completed - 5\n", + "Masking House_Wren_0110_187111.jpg test completed - 6\n", + "Masking House_Wren_0127_187832.jpg test completed - 7\n", + "Masking House_Wren_0130_187109.jpg test completed - 8\n", + "Masking House_Wren_0123_187095.jpg test completed - 9\n", + "Masking House_Wren_0137_187273.jpg test completed - 10\n", + "Masking House_Wren_0083_187406.jpg test completed - 11\n", + "Masking House_Wren_0071_187399.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/197.Marsh_Wren\n", + "Train indexes: [28 1 59 47 17 45 4 52 29 14 43 10 46 9 21 38 57 22 2 0 49 51 27 50\n", + " 8 37 58 39 55 31 30 53 3 6 42 13]\n", + "Validation indexes: [24 16 41 12 33 18 23 19 26 5 48 40]\n", + "Test indexes: [35 25 15 7 11 56 32 54 20 36 34 44]\n", + "Masking Marsh_Wren_0062_188158.jpg train completed - 1\n", + "Masking Marsh_Wren_0005_188235.jpg train completed - 2\n", + "Masking Marsh_Wren_0147_188367.jpg train completed - 3\n", + "Masking Marsh_Wren_0108_188788.jpg train completed - 4\n", + "Masking Marsh_Wren_0042_188195.jpg train completed - 5\n", + "Masking Marsh_Wren_0106_188100.jpg train completed - 6\n", + "Masking Marsh_Wren_0012_188275.jpg train completed - 7\n", + "Masking Marsh_Wren_0118_188512.jpg train completed - 8\n", + "Masking Marsh_Wren_0068_188446.jpg train completed - 9\n", + "Masking 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"Masking Marsh_Wren_0082_188699.jpg train completed - 26\n", + "Masking Marsh_Wren_0145_188193.jpg train completed - 27\n", + "Masking Marsh_Wren_0094_188710.jpg train completed - 28\n", + "Masking Marsh_Wren_0133_188565.jpg train completed - 29\n", + "Masking Marsh_Wren_0070_188205.jpg train completed - 30\n", + "Masking Marsh_Wren_0069_188776.jpg train completed - 31\n", + "Masking Marsh_Wren_0119_188404.jpg train completed - 32\n", + "Masking Marsh_Wren_0008_188533.jpg train completed - 33\n", + "Masking Marsh_Wren_0015_188852.jpg train completed - 34\n", + "Masking Marsh_Wren_0099_188579.jpg train completed - 35\n", + "Masking Marsh_Wren_0036_188374.jpg train completed - 36\n", + "Masking Marsh_Wren_0053_188276.jpg valid completed - 1\n", + "Masking Marsh_Wren_0040_188252.jpg valid completed - 2\n", + "Masking Marsh_Wren_0097_188214.jpg valid completed - 3\n", + "Masking Marsh_Wren_0033_188778.jpg valid completed - 4\n", + "Masking Marsh_Wren_0072_188764.jpg valid completed - 5\n", + "Masking Marsh_Wren_0043_188426.jpg valid completed - 6\n", + "Masking Marsh_Wren_0051_188260.jpg valid completed - 7\n", + "Masking Marsh_Wren_0044_188270.jpg valid completed - 8\n", + "Masking Marsh_Wren_0056_188241.jpg valid completed - 9\n", + "Masking Marsh_Wren_0014_188802.jpg valid completed - 10\n", + "Masking Marsh_Wren_0109_188329.jpg valid completed - 11\n", + "Masking Marsh_Wren_0095_188371.jpg valid completed - 12\n", + "Masking Marsh_Wren_0076_188108.jpg test completed - 1\n", + "Masking Marsh_Wren_0055_188123.jpg test completed - 2\n", + "Masking Marsh_Wren_0039_188201.jpg test completed - 3\n", + "Masking Marsh_Wren_0018_188363.jpg test completed - 4\n", + "Masking Marsh_Wren_0028_188797.jpg test completed - 5\n", + "Masking Marsh_Wren_0134_188213.jpg test completed - 6\n", + "Masking Marsh_Wren_0071_188111.jpg test completed - 7\n", + "Masking Marsh_Wren_0122_188323.jpg test completed - 8\n", + "Masking Marsh_Wren_0048_188370.jpg test completed - 9\n", + "Masking Marsh_Wren_0080_188812.jpg test completed - 10\n", + "Masking Marsh_Wren_0075_188549.jpg test completed - 11\n", + "Masking Marsh_Wren_0103_188483.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/198.Rock_Wren\n", + "Train indexes: [38 6 8 7 27 21 33 41 15 0 25 1 10 59 58 57 42 11 39 24 50 44 32 54\n", + " 52 55 34 29 51 37 16 3 20 9 53 31]\n", + "Validation indexes: [ 2 30 18 4 12 40 19 14 13 23 17 22]\n", + "Test indexes: [26 48 28 46 47 43 45 36 5 56 35 49]\n", + "Masking Rock_Wren_0073_188952.jpg train completed - 1\n", + "Masking Rock_Wren_0020_188895.jpg train completed - 2\n", + "Masking Rock_Wren_0022_188958.jpg train completed - 3\n", + "Masking Rock_Wren_0021_189175.jpg train completed - 4\n", + "Masking Rock_Wren_0050_189207.jpg train completed - 5\n", + "Masking Rock_Wren_0040_189159.jpg train completed - 6\n", + "Masking Rock_Wren_0065_188995.jpg train completed - 7\n", + "Masking Rock_Wren_0080_189340.jpg train completed - 8\n", + "Masking 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Rock_Wren_0026_189181.jpg valid completed - 5\n", + "Masking Rock_Wren_0078_188960.jpg valid completed - 6\n", + "Masking Rock_Wren_0037_189256.jpg valid completed - 7\n", + "Masking Rock_Wren_0029_189145.jpg valid completed - 8\n", + "Masking Rock_Wren_0027_189331.jpg valid completed - 9\n", + "Masking Rock_Wren_0042_189006.jpg valid completed - 10\n", + "Masking Rock_Wren_0035_188998.jpg valid completed - 11\n", + "Masking Rock_Wren_0041_189227.jpg valid completed - 12\n", + "Masking Rock_Wren_0045_189153.jpg test completed - 1\n", + "Masking Rock_Wren_0104_189161.jpg test completed - 2\n", + "Masking Rock_Wren_0053_189112.jpg test completed - 3\n", + "Masking Rock_Wren_0098_188947.jpg test completed - 4\n", + "Masking Rock_Wren_0100_189426.jpg test completed - 5\n", + "Masking Rock_Wren_0087_188988.jpg test completed - 6\n", + "Masking Rock_Wren_0096_188966.jpg test completed - 7\n", + "Masking Rock_Wren_0069_188969.jpg test completed - 8\n", + "Masking Rock_Wren_0019_188968.jpg test completed - 9\n", + "Masking Rock_Wren_0122_189042.jpg test completed - 10\n", + "Masking Rock_Wren_0067_189152.jpg test completed - 11\n", + "Masking Rock_Wren_0109_189094.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/199.Winter_Wren\n", + "Train indexes: [54 24 45 47 41 10 31 42 39 43 20 55 33 27 0 1 48 57 40 28 11 18 30 50\n", + " 13 34 46 8 15 38 36 59 22 4 3 32]\n", + "Validation indexes: [ 6 49 9 12 37 17 2 21 7 25 14 16]\n", + "Test indexes: [53 5 51 35 29 52 56 58 23 44 26 19]\n", + "Masking Winter_Wren_0129_189843.jpg train completed - 1\n", + "Masking Winter_Wren_0066_189637.jpg train completed - 2\n", + "Masking Winter_Wren_0113_189558.jpg train completed - 3\n", + "Masking Winter_Wren_0117_189999.jpg train completed - 4\n", + "Masking Winter_Wren_0109_189850.jpg train completed - 5\n", + "Masking Winter_Wren_0030_190311.jpg train completed - 6\n", + "Masking Winter_Wren_0082_189549.jpg train completed - 7\n", + "Masking Winter_Wren_0110_189569.jpg 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completed - 3\n", + "Masking Winter_Wren_0037_190123.jpg valid completed - 4\n", + "Masking Winter_Wren_0095_189985.jpg valid completed - 5\n", + "Masking Winter_Wren_0049_189504.jpg valid completed - 6\n", + "Masking Winter_Wren_0005_189750.jpg valid completed - 7\n", + "Masking Winter_Wren_0062_189501.jpg valid completed - 8\n", + "Masking Winter_Wren_0021_189597.jpg valid completed - 9\n", + "Masking Winter_Wren_0071_189689.jpg valid completed - 10\n", + "Masking Winter_Wren_0044_190068.jpg valid completed - 11\n", + "Masking Winter_Wren_0048_189683.jpg valid completed - 12\n", + "Masking Winter_Wren_0128_190093.jpg test completed - 1\n", + "Masking Winter_Wren_0015_190099.jpg test completed - 2\n", + "Masking Winter_Wren_0126_190342.jpg test completed - 3\n", + "Masking Winter_Wren_0090_190104.jpg test completed - 4\n", + "Masking Winter_Wren_0077_190366.jpg test completed - 5\n", + "Masking Winter_Wren_0127_190091.jpg test completed - 6\n", + "Masking Winter_Wren_0131_190061.jpg test completed - 7\n", + "Masking Winter_Wren_0141_190152.jpg test completed - 8\n", + "Masking Winter_Wren_0065_189675.jpg test completed - 9\n", + "Masking Winter_Wren_0112_189507.jpg test completed - 10\n", + "Masking Winter_Wren_0072_189521.jpg test completed - 11\n", + "Masking Winter_Wren_0051_189990.jpg test completed - 12\n", + "/kaggle/working/CUB_200_2011/images/200.Common_Yellowthroat\n", + "Train indexes: [ 5 2 41 8 36 51 15 44 32 30 34 40 58 42 1 4 35 16 37 49 33 39 14 12\n", + " 10 50 28 25 53 17 26 0 3 48 56 47]\n", + "Validation indexes: [19 45 54 46 55 38 20 18 59 22 31 13]\n", + "Test indexes: [11 43 21 7 29 57 9 6 27 23 52 24]\n", + "Masking Common_Yellowthroat_0010_190572.jpg train completed - 1\n", + "Masking Common_Yellowthroat_0006_190576.jpg train completed - 2\n", + "Masking Common_Yellowthroat_0086_190639.jpg train completed - 3\n", + "Masking Common_Yellowthroat_0016_190986.jpg train completed - 4\n", + "Masking Common_Yellowthroat_0072_190402.jpg train 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[ + "from PIL import Image\n", + "\n", + "def mask_image(directories):\n", + " os.makedirs('/kaggle/working/Masked_Images', exist_ok=True)\n", + " \n", + " for directory in directories:\n", + " img_directory = f'/kaggle/working/CUB_200_2011/images/{directory}'\n", + " print(img_directory)\n", + " img_files = sorted(os.listdir(img_directory))\n", + " jpg_files = [img for img in img_files if img.endswith('.jpg')]\n", + " seg_directory = f'/kaggle/working/segmentations/{directory}' \n", + " seg_files = sorted(os.listdir(seg_directory))\n", + " png_files = [img for img in seg_files if img.endswith('.png')]\n", + " jpg_files = sorted(jpg_files)\n", + " png_files = sorted(png_files)\n", + " indexes = np.arange(len(jpg_files))\n", + " np.random.shuffle(indexes)\n", + "\n", + " # Calculate the split point for 80:20\n", + " split_point = int(0.8 * len(jpg_files))\n", + "\n", + " # Divide the indexes into 80:20\n", + " train_indexes = indexes[:split_point]\n", + " test_indexes = indexes[split_point:]\n", + "\n", + " train_split_point = int(0.75 * len(train_indexes))\n", + " train_subset = train_indexes[:train_split_point]\n", + " validation_subset = train_indexes[train_split_point:]\n", + "\n", + " print(\"Train indexes:\", train_subset)\n", + " print(\"Validation indexes:\", validation_subset)\n", + " print(\"Test indexes:\", test_indexes)\n", + " split_indexes = [train_subset,validation_subset,test_indexes]\n", + " split_dir = ['train','valid','test']\n", + " jpg_array = np.array(jpg_files)\n", + " png_array = np.array(png_files)\n", + " for i in range(3):\n", + " masked_image_count = 0\n", + " for jpg_file,png_file in zip(jpg_array[split_indexes[i]],png_array[split_indexes[i]]):\n", + " # Load the original image and mask using Pillow\n", + " image = Image.open(f'/kaggle/working/CUB_200_2011/images/{directory}/{jpg_file}')\n", + " mask = Image.open(f'/kaggle/working/segmentations/{directory}/{png_file}').convert('L') # Convert mask to grayscale\n", + "\n", + " # Ensure the mask has the same size as the image\n", + " mask = mask.resize(image.size)\n", + "\n", + " # Convert the images to NumPy arrays\n", + " image_array = np.array(image)\n", + " mask_array = np.array(mask)\n", + "\n", + " # Normalize the mask to be in the range of [0, 1]\n", + " mask_array = mask_array / 255.0\n", + "\n", + " # Ensure the mask has the correct shape (broadcastable)\n", + " if len(image_array.shape) == 3:\n", + " mask_array = np.expand_dims(mask_array, axis=-1)\n", + "\n", + " # Apply the mask to the image\n", + " masked_image_array = image_array * mask_array\n", + "\n", + " # Convert the result back to a PIL Image\n", + " masked_image = Image.fromarray(np.uint8(masked_image_array))\n", + "\n", + " # Optionally save the result\n", + " os.makedirs(f'/kaggle/working/Masked_Images/{split_dir[i]}/{directory}', exist_ok=True)\n", + " masked_image.save(f'/kaggle/working/Masked_Images/{split_dir[i]}/{directory}/{jpg_file}')\n", + " masked_image_count += 1\n", + " print(f'Masking {jpg_file} {split_dir[i]} completed - {masked_image_count}')\n", + "\n", + "mask_image(directories)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "61b3c4af", + "metadata": { + "execution": { + "iopub.execute_input": "2024-10-03T19:47:42.125644Z", + "iopub.status.busy": "2024-10-03T19:47:42.125267Z", + "iopub.status.idle": "2024-10-03T19:47:54.165393Z", + "shell.execute_reply": "2024-10-03T19:47:54.164591Z" + }, + "papermill": { + "duration": 12.102714, + "end_time": "2024-10-03T19:47:54.167762", + "exception": false, + "start_time": "2024-10-03T19:47:42.065048", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "from tensorflow import keras\n", + "from tensorflow.keras import layers\n", + "from tensorflow.keras.preprocessing.image import ImageDataGenerator" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "13189d24", + "metadata": { + "execution": { + "iopub.execute_input": "2024-10-03T19:47:54.290347Z", + "iopub.status.busy": "2024-10-03T19:47:54.289404Z", + "iopub.status.idle": "2024-10-03T19:47:57.527513Z", + "shell.execute_reply": "2024-10-03T19:47:57.526602Z" + }, + "papermill": { + "duration": 3.302311, + "end_time": "2024-10-03T19:47:57.530280", + "exception": false, + "start_time": "2024-10-03T19:47:54.227969", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 7045 images belonging to 200 classes.\n", + "Found 7045 images belonging to 200 classes.\n", + "Found 7045 images belonging to 200 classes.\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import scipy\n", + "BS = 32\n", + "image_size = (224,224)\n", + "train_datagen = ImageDataGenerator(rescale=1./255,\n", + " rotation_range=10,\n", + " width_shift_range=0.1,\n", + " height_shift_range=0.1,\n", + " shear_range=0.1,\n", + " zoom_range=0.1,\n", + " horizontal_flip=True,\n", + " fill_mode='nearest')\n", + "\n", + "valid_datagen = ImageDataGenerator(rescale=1./255)\n", + "\n", + "train_generator = train_datagen.flow_from_directory(\n", + " './Masked_Images/train',\n", + " target_size= image_size,\n", + " batch_size=BS,\n", + " class_mode='categorical',\n", + " color_mode='rgb')\n", + "\n", + "valid_generator = valid_datagen.flow_from_directory(\n", + " './Masked_Images/train',\n", + " target_size= image_size,\n", + " batch_size=BS,\n", + " class_mode='categorical',\n", + " color_mode='rgb')\n", + "\n", + "test_generator = valid_datagen.flow_from_directory(\n", + " './Masked_Images/train',\n", + " target_size= image_size,\n", + " batch_size=BS,\n", + " class_mode='categorical',\n", + " color_mode='rgb')\n", + "\n", + "for _ in range(3):\n", + " img, label = next(train_generator)\n", + " plt.imshow(img[0])\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "75b6c2cf", + "metadata": { + "execution": { + "iopub.execute_input": "2024-10-03T19:47:57.658796Z", + "iopub.status.busy": "2024-10-03T19:47:57.658101Z", + "iopub.status.idle": "2024-10-03T21:42:05.969662Z", + "shell.execute_reply": "2024-10-03T21:42:05.968834Z" + }, + "papermill": { + "duration": 6848.378049, + "end_time": "2024-10-03T21:42:05.971703", + "exception": false, + "start_time": "2024-10-03T19:47:57.593654", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n", + "\u001b[1m58889256/58889256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n" + ] + }, + { + "data": { + "text/html": [ + "
Model: \"sequential\"\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1mModel: \"sequential\"\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ vgg16 (Functional)              │ ?                      │    14,714,688 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ flatten (Flatten)               │ ?                      │   0 (unbuilt) │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense (Dense)                   │ ?                      │   0 (unbuilt) │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization             │ ?                      │   0 (unbuilt) │\n",
+       "│ (BatchNormalization)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_1 (Dense)                 │ ?                      │   0 (unbuilt) │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "
\n" + ], + "text/plain": [ + "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", + "│ vgg16 (\u001b[38;5;33mFunctional\u001b[0m) │ ? │ \u001b[38;5;34m14,714,688\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ flatten (\u001b[38;5;33mFlatten\u001b[0m) │ ? │ \u001b[38;5;34m0\u001b[0m (unbuilt) │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense (\u001b[38;5;33mDense\u001b[0m) │ ? │ \u001b[38;5;34m0\u001b[0m (unbuilt) │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ batch_normalization │ ? │ \u001b[38;5;34m0\u001b[0m (unbuilt) │\n", + "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_1 (\u001b[38;5;33mDense\u001b[0m) │ ? │ \u001b[38;5;34m0\u001b[0m (unbuilt) │\n", + "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Total params: 14,714,688 (56.13 MB)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m14,714,688\u001b[0m (56.13 MB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Trainable params: 0 (0.00 B)\n",
+       "
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 Non-trainable params: 14,714,688 (56.13 MB)\n",
+       "
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Devices:\n", + "I0000 00:00:1727984886.582827 88 service.cc:153] StreamExecutor device (0): Tesla P100-PCIE-16GB, Compute Capability 6.0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m 2/221\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m15s\u001b[0m 73ms/step - accuracy: 0.0078 - loss: 6.5368 " + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "I0000 00:00:1727984895.950066 88 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m221/221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 401ms/step - accuracy: 0.0630 - loss: 5.3337 - val_accuracy: 0.1842 - val_loss: 4.7352\n", + "Epoch 2/100\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", + "I0000 00:00:1727984984.158573 90 asm_compiler.cc:369] ptxas warning : Registers are spilled to local memory in function 'input_reduce_fusion_6', 8 bytes spill stores, 8 bytes spill loads\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m221/221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m87s\u001b[0m 384ms/step - accuracy: 0.3080 - loss: 2.9953 - val_accuracy: 0.2835 - val_loss: 3.6954\n", + "Epoch 3/100\n", + "\u001b[1m221/221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m87s\u001b[0m 383ms/step - 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accuracy: 0.9325 - loss: 0.2394 - val_accuracy: 0.9840 - val_loss: 0.0507\n", + "Epoch 74/100\n", + "\u001b[1m221/221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m87s\u001b[0m 382ms/step - accuracy: 0.9366 - loss: 0.2148 - val_accuracy: 0.9807 - val_loss: 0.0602\n", + "Epoch 75/100\n", + "\u001b[1m221/221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m87s\u001b[0m 383ms/step - accuracy: 0.9419 - loss: 0.1949 - val_accuracy: 0.9872 - val_loss: 0.0443\n", + "Epoch 76/100\n", + "\u001b[1m221/221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m87s\u001b[0m 381ms/step - accuracy: 0.9509 - loss: 0.1635 - val_accuracy: 0.9864 - val_loss: 0.0421\n", + "Epoch 77/100\n", + "\u001b[1m221/221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m87s\u001b[0m 382ms/step - accuracy: 0.9438 - loss: 0.1784 - val_accuracy: 0.9727 - val_loss: 0.1016\n", + "Epoch 78/100\n", + "\u001b[1m221/221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m87s\u001b[0m 384ms/step - accuracy: 0.9347 - loss: 0.2190 - val_accuracy: 0.9888 - val_loss: 0.0376\n" + ] + } + ], + "source": [ + "from tensorflow.keras.applications import VGG16\n", + "\n", + "# Load VGG16 pretrained model\n", + "vgg16_model = VGG16(input_shape=(224, 224, 3), \n", + " include_top=False, \n", + " weights='imagenet')\n", + "vgg16_model.trainable = False # Freeze the base model\n", + "\n", + "# Build Sequential model\n", + "model_vgg16 = keras.Sequential([\n", + " vgg16_model,\n", + " layers.Flatten(),\n", + " layers.Dense(units=1950, activation='relu'),\n", + " layers.BatchNormalization(),\n", + " layers.Dense(units=200, activation='softmax')\n", + "])\n", + "\n", + "model_vgg16.summary()\n", + "\n", + "# Compile model\n", + "model_vgg16.compile(\n", + " optimizer='adam',\n", + " loss='categorical_crossentropy',\n", + " metrics=['accuracy']\n", + ")\n", + "\n", + "from tensorflow.keras.callbacks import EarlyStopping\n", + "\n", + "early_stop = EarlyStopping(monitor='val_loss',patience=10)\n", + "\n", + "# Fit the model\n", + "history = model_vgg16.fit(\n", + " train_generator,\n", + " validation_data=valid_generator,\n", + " epochs=100,\n", + " verbose=1,\n", + " callbacks=[early_stop]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "6f88ea9a", + "metadata": { + "execution": { + "iopub.execute_input": "2024-10-03T21:42:08.973891Z", + "iopub.status.busy": "2024-10-03T21:42:08.973478Z", + "iopub.status.idle": "2024-10-03T21:42:23.568877Z", + "shell.execute_reply": "2024-10-03T21:42:23.567946Z" + }, + "papermill": { + "duration": 16.097457, + "end_time": "2024-10-03T21:42:23.570860", + "exception": false, + "start_time": "2024-10-03T21:42:07.473403", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m221/221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 65ms/step - 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+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ inception_resnet_v2             │ ?                      │    54,336,736 │\n",
+       "│ (Functional)                    │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ flatten_1 (Flatten)             │ ?                      │   0 (unbuilt) │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_2 (Dense)                 │ ?                      │   0 (unbuilt) │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_204         │ ?                      │   0 (unbuilt) │\n",
+       "│ (BatchNormalization)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_3 (Dense)                 │ ?                      │   0 (unbuilt) │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "
\n" + ], + "text/plain": [ + "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", + "│ inception_resnet_v2 │ ? │ \u001b[38;5;34m54,336,736\u001b[0m │\n", + "│ (\u001b[38;5;33mFunctional\u001b[0m) │ │ │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ flatten_1 (\u001b[38;5;33mFlatten\u001b[0m) │ ? │ \u001b[38;5;34m0\u001b[0m (unbuilt) │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_2 (\u001b[38;5;33mDense\u001b[0m) │ ? │ \u001b[38;5;34m0\u001b[0m (unbuilt) │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ batch_normalization_204 │ ? │ \u001b[38;5;34m0\u001b[0m (unbuilt) │\n", + "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_3 (\u001b[38;5;33mDense\u001b[0m) │ ? │ \u001b[38;5;34m0\u001b[0m (unbuilt) │\n", + "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Total params: 54,336,736 (207.28 MB)\n",
+       "
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 Trainable params: 0 (0.00 B)\n",
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 Non-trainable params: 54,336,736 (207.28 MB)\n",
+       "
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val_accuracy: 0.4368 - val_loss: 2.2133\n", + "Epoch 3/100\n", + "\u001b[1m221/221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m93s\u001b[0m 411ms/step - accuracy: 0.3980 - loss: 2.4055 - val_accuracy: 0.5126 - val_loss: 1.8895\n", + "Epoch 4/100\n", + "\u001b[1m221/221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m93s\u001b[0m 410ms/step - accuracy: 0.4555 - loss: 2.1011 - val_accuracy: 0.5820 - val_loss: 1.5250\n", + "Epoch 5/100\n", + "\u001b[1m221/221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m94s\u001b[0m 414ms/step - accuracy: 0.5043 - loss: 1.8032 - val_accuracy: 0.5892 - val_loss: 1.4364\n", + "Epoch 6/100\n", + "\u001b[1m221/221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m93s\u001b[0m 412ms/step - accuracy: 0.5416 - loss: 1.6605 - val_accuracy: 0.6600 - val_loss: 1.2199\n", + "Epoch 7/100\n", + "\u001b[1m221/221\u001b[0m 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loss: 0.2345 - val_accuracy: 0.9611 - val_loss: 0.2059\n", + "Epoch 84/100\n", + "\u001b[1m221/221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m93s\u001b[0m 409ms/step - accuracy: 0.9244 - loss: 0.2326 - val_accuracy: 0.9747 - val_loss: 0.1622\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Loss')" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#model\n", + "\n", + "resnet_model=tf.keras.applications.InceptionResNetV2(input_shape=(224,224,3),\n", + " include_top=False,\n", + " weights='imagenet')\n", + "resnet_model.trainable = False\n", + "\n", + "\n", + "model_resnet = keras.Sequential([ \n", + " resnet_model,\n", + " layers.Flatten(),\n", + " layers.Dense(units=1950,activation='relu'),\n", + " layers.BatchNormalization(),\n", + " layers.Dense(units=200, activation=\"softmax\"),\n", + "])\n", + "model_resnet.summary()\n", + "\n", + "model_resnet.compile(\n", + " optimizer='adam',\n", + " loss='categorical_crossentropy',\n", + " metrics=['accuracy']\n", + ")\n", + "\n", + "#fit\n", + "\n", + "from tensorflow.keras.callbacks import EarlyStopping\n", + "\n", + "early_stop = EarlyStopping(monitor='val_loss',patience=10)\n", + "\n", + "history = model_resnet.fit(\n", + " train_generator,\n", + " validation_data=valid_generator,\n", + " epochs=100,\n", + " verbose=1,\n", + " callbacks=[early_stop]\n", + ")\n", + "\n", + "result=pd.DataFrame(history.history)\n", + "fig, ax=plt.subplots(nrows=1, ncols=2,figsize=(18,6))\n", + "ax=ax.flatten()\n", + "ax[0].plot(result[['accuracy','val_accuracy']])\n", + "ax[0].set_title(\"Accuracy\")\n", + "ax[1].plot(result[['loss','val_loss']])\n", + "ax[1].set_title(\"Loss\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "7cfcf5f8", + "metadata": { + "execution": { + "iopub.execute_input": "2024-10-03T23:56:13.732817Z", + "iopub.status.busy": "2024-10-03T23:56:13.731718Z", + "iopub.status.idle": "2024-10-03T23:56:33.577901Z", + "shell.execute_reply": "2024-10-03T23:56:33.576984Z" + }, + "papermill": { + "duration": 22.901918, + "end_time": "2024-10-03T23:56:33.579941", + "exception": false, + "start_time": "2024-10-03T23:56:10.678023", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m221/221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 89ms/step - accuracy: 0.9707 - loss: 0.1600\n" + ] + }, + { + "data": { + "text/plain": [ + "[0.16217303276062012, 0.974733829498291]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model_resnet.evaluate(test_generator)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "4f94ca60", + "metadata": { + "execution": { + "iopub.execute_input": "2024-10-03T23:56:39.778015Z", + "iopub.status.busy": "2024-10-03T23:56:39.777631Z", + "iopub.status.idle": "2024-10-03T23:56:42.686644Z", + "shell.execute_reply": "2024-10-03T23:56:42.685816Z" + }, + "papermill": { + "duration": 5.977852, + "end_time": "2024-10-03T23:56:42.688900", + "exception": false, + "start_time": "2024-10-03T23:56:36.711048", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "model_resnet.save('bird_CNN_model_resnet.h5')" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "1198ca34", + "metadata": { + "execution": { + "iopub.execute_input": "2024-10-04T00:49:11.871904Z", + "iopub.status.busy": "2024-10-04T00:49:11.870989Z", + "iopub.status.idle": "2024-10-04T02:22:52.842590Z", + "shell.execute_reply": "2024-10-04T02:22:52.841606Z" + }, + "papermill": { + "duration": 5624.834785, + "end_time": "2024-10-04T02:22:52.844758", + "exception": false, + "start_time": "2024-10-04T00:49:08.009973", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n", + "\u001b[1m87910968/87910968\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 0us/step\n" + ] + }, + { + "data": { + "text/html": [ + "
Model: \"sequential_3\"\n",
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+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ inception_v3 (Functional)       │ ?                      │    21,802,784 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ flatten_3 (Flatten)             │ ?                      │   0 (unbuilt) │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_6 (Dense)                 │ ?                      │   0 (unbuilt) │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_300         │ ?                      │   0 (unbuilt) │\n",
+       "│ (BatchNormalization)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_7 (Dense)                 │ ?                      │   0 (unbuilt) │\n",
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from tensorflow.keras.applications import InceptionV3\n", + "\n", + "# Load the InceptionV3 model\n", + "inceptionv3_model = InceptionV3(input_shape=(224, 224, 3),\n", + " include_top=False,\n", + " weights='imagenet')\n", + "\n", + "# Freeze the pre-trained model\n", + "inceptionv3_model.trainable = False\n", + "\n", + "# Create a Sequential model and add layers\n", + "model_inceptionv3 = keras.Sequential([\n", + " inceptionv3_model,\n", + " layers.Flatten(),\n", + " layers.Dense(units=1950, activation='relu'),\n", + " layers.BatchNormalization(),\n", + " layers.Dense(units=200, activation='softmax'),\n", + "])\n", + "\n", + "model_inceptionv3.summary()\n", + "\n", + "# Compile the model\n", + "model_inceptionv3.compile(\n", + " optimizer='adam',\n", + " loss='categorical_crossentropy',\n", + " metrics=['accuracy']\n", + ")\n", + "\n", + "# Fit the model\n", + "from tensorflow.keras.callbacks import EarlyStopping\n", + "\n", + "early_stop = EarlyStopping(monitor='val_loss',patience=10)\n", + "history_inceptionv3 = model_inceptionv3.fit(\n", + " train_generator,\n", + " validation_data=valid_generator,\n", + " epochs=100,\n", + " verbose=1,\n", + " callbacks=[early_stop]\n", + ")\n", + "\n", + "# Plot results\n", + "result_inceptionv3 = pd.DataFrame(history_inceptionv3.history)\n", + "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(18,6))\n", + "ax = ax.flatten()\n", + "ax[0].plot(result_inceptionv3[['accuracy', 'val_accuracy']])\n", + "ax[0].set_title(\"Accuracy - InceptionV3\")\n", + "ax[1].plot(result_inceptionv3[['loss', 'val_loss']])\n", + "ax[1].set_title(\"Loss - InceptionV3\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "4aab4620", + "metadata": { + "execution": { + "iopub.execute_input": "2024-10-04T02:23:02.656503Z", + "iopub.status.busy": "2024-10-04T02:23:02.655585Z", + "iopub.status.idle": "2024-10-04T02:23:13.228024Z", + "shell.execute_reply": "2024-10-04T02:23:13.227064Z" + }, + "papermill": { + "duration": 15.487066, + "end_time": "2024-10-04T02:23:13.230054", + "exception": false, + "start_time": "2024-10-04T02:22:57.742988", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m221/221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 47ms/step - accuracy: 0.9738 - loss: 0.1130\n" + ] + }, + { + "data": { + "text/plain": [ + "[0.15123121440410614, 0.9728885889053345]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model_inceptionv3.evaluate(test_generator)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "18a4091e", + "metadata": { + "execution": { + "iopub.execute_input": "2024-10-04T02:23:23.092861Z", + "iopub.status.busy": "2024-10-04T02:23:23.092457Z", + "iopub.status.idle": "2024-10-04T02:23:31.844782Z", + "shell.execute_reply": "2024-10-04T02:23:31.843704Z" + }, + "papermill": { + "duration": 13.673078, + "end_time": "2024-10-04T02:23:31.848899", + "exception": false, + "start_time": "2024-10-04T02:23:18.175821", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "model_inceptionv3.save('bird_CNN_model_inceptionv3.h5')" + ] + } + ], + "metadata": { + "kaggle": { + "accelerator": "gpu", + "dataSources": [ + { + "datasetId": 471346, + "sourceId": 883439, + "sourceType": "datasetVersion" + } + ], + "dockerImageVersionId": 30775, + "isGpuEnabled": true, + "isInternetEnabled": true, + "language": "python", + "sourceType": "notebook" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + }, + "papermill": { + "default_parameters": {}, + "duration": 23887.510488, + "end_time": "2024-10-04T02:23:41.272448", + "environment_variables": {}, + "exception": null, + "input_path": "__notebook__.ipynb", + "output_path": "__notebook__.ipynb", + "parameters": {}, + "start_time": "2024-10-03T19:45:33.761960", + "version": "2.6.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Bird Species Classification/Readme.md b/Bird Species Classification/Readme.md new file mode 100644 index 000000000..a81a5d42e --- /dev/null +++ b/Bird Species Classification/Readme.md @@ -0,0 +1,83 @@ +**Bird Species Classification Using Deep Learning** + +**🎯 Goal** + +The primary goal of this project is to build a robust bird species classification system using deep learning models. The system is designed to classify images of birds into one of 200 species, providing an efficient and accurate solution for wildlife monitoring and research. + +**🧵 Dataset** + +The dataset used for this project is available on Kaggle: 200 Bird Species Dataset. It contains 11,788 images, divided into 200 different bird species. + +Link: https://www.kaggle.com/datasets/veeralakrishna/200-bird-species-with-11788-images + +**🧾 Description** + +This project implements several deep learning models to classify bird species based on images. The project explores the capabilities of modern Convolutional Neural Networks (CNNs), focusing on models like InceptionResNetV2, InceptionV3, and VGG16. Each model is fine-tuned using the bird species dataset to achieve optimal performance. The results from each model are compared to determine the most accurate model for this classification task. + +**🧮 What I had done!** + + 1. Data Collection: Downloaded the bird species dataset from Kaggle. + 2. Data Preprocessing: + • Resized the images to a uniform size for consistency across models. + • Augmented the dataset with transformations (e.g., rotation, flipping) to enhance the model’s generalization. + 3. Model Selection: Selected four CNN architectures — InceptionResNetV2, InceptionV3, and VGG16. + 4. Model Training: + • Fine-tuned each model on the bird species dataset. + • Utilized ImageNet pretrained weights as a baseline, followed by further training on the dataset. + 5. Evaluation: Evaluated each model based on accuracy. + + +**🚀 Models Implemented** + + 1. InceptionResNetV2: + • Combines Inception modules and residual connections for high accuracy. + • Chosen for its ability to handle complex image classification tasks. + 2. InceptionV3: + • Selected for its efficient use of factorized convolutions, which reduce computational complexity. + 3. VGG16: + • A classical CNN architecture that is known for its deep and uniform layer structure, making it effective for image recognition. + +Each of these models was chosen based on their proven track record in image classification and their ability to handle the complexity of the bird species dataset. + +**📚 Libraries Needed** + + • TensorFlow + • NumPy + • Matplotlib + • scipy + • Pandas + +**📊 Exploratory Data Analysis Results** + +Below are some visualizations derived from the dataset (example EDA images must be included here): + +- Inception_resnet_v2 Accuracy + ![Inception_resnet_v2](./Images/inception_resnet_v2.png) +- Inception_v3 Accuracy + ![Inception_v3](./Images/InceptionV3.png) +- Masked Bird Images using Segmentation Images from dataset + - ![Masked_Bird_Image](./Images/masked_image_1.png) + - ![Masked_Bird_Image](./Images/masked_image_2.png) + - ![Masked_Bird_Image](./Images/masked_image_3.png) + +**📈 Performance of the Models based on the Accuracy Scores** + +Model | Accuracy +--- | --- +InceptionResNetV2 | 97.07% +InceptionV3 | 97.38% +VGG16 | 98.78% + +The table above shows the performance of each model based on the key metric. + +**📢 Conclusion** + +From the results, it is evident that VGG16 performs the best among all models, achieving the highest accuracy (98.78%), while InceptionResNetV2 and InceptionV3, perform similar. + +In conclusion, VGG16 is the best-fitted model for this bird species classification task, based on the achieved accuracy. + +**✒️ Your Signature** + +Vivek Prakash +GitHub: https://github.com/IkkiOcean +LinkedIn: https://www.linkedin.com/in/vivek-prakash-b46830283/ diff --git a/Bird Species Classification/requirements.txt b/Bird Species Classification/requirements.txt new file mode 100644 index 000000000..b7842d9ac --- /dev/null +++ b/Bird Species Classification/requirements.txt @@ -0,0 +1,5 @@ +matplotlib==3.5.1 +numpy==1.22.3 +Pillow==9.2.0 +tensorflow==2.9.1 +tarfile \ No newline at end of file