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+# Bone Fracture Detection: Computer Vision Project
+
+## Overview: 
+A comprehensive dataset of X-ray images was created for bone fracture detection, specifically designed for computer vision projects. The primary goal of this dataset is to aid in developing and evaluating algorithms for automated bone fracture detection.
+
+The dataset contains images categorized into different classes, each representing a specific type of bone fracture. These classes include Elbow Positive, Fingers Positive, Forearm Fracture, Humerus Fracture, Shoulder Fracture, and Wrist Positive.
+
+Each image in the dataset is annotated with either bounding boxes or pixel-level segmentation masks to indicate the location and extent of the detected fracture. This facilitates the training and evaluation of bone fracture detection algorithms.
+
+The bone fracture detection dataset is a useful resource for researchers and developers who want to train machine learning models, specifically focusing on object detection algorithms, to automatically detect and classify bone fractures in X-ray images. The dataset's diversity of fracture classes enables the development of robust models capable of accurately identifying fractures in different regions of the upper extremities.
+
+The aim of creating this dataset is to accelerate the development of computer vision solutions for automated fracture detection, supporting advancements in medical diagnostics and improving patient care.
+
+## Access:
+The dataset is available for download from Kaggle at the following URL:
+https://www.kaggle.com/datasets/pkdarabi/bone-fracture-detection-computer-vision-project
+
+Their COCO annotations (for Faster RCNN model):
+https://www.kaggle.com/datasets/banddaniel/bone-fracture-detection-detection-coco-annots
+
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diff --git a/Bone Fracture Detection/Models/Bone_Fracture_RCNN.ipynb b/Bone Fracture Detection/Models/Bone_Fracture_RCNN.ipynb
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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "collapsed_sections": [
+        "PI2EidjWHleF"
+      ]
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.colab import drive\n",
+        "\n",
+        "drive.mount('/content/drive' , force_remount = True)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "D7PNajxLhVIo",
+        "outputId": "3acb3588-d3f5-4533-e63c-29b5cb1ab19d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mounted at /content/drive\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Faster RCNN by FaceBook Detectron\n"
+      ],
+      "metadata": {
+        "id": "NN8YZsiJgglf"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import sys, os, distutils.core\n",
+        "from IPython.display import clear_output\n",
+        "\n",
+        "# Cloning Detectron Model from Github\n",
+        "!python -m pip install pyyaml==5.1\n",
+        "!git clone 'https://github.com/facebookresearch/detectron2'\n",
+        "dist = distutils.core.run_setup(\"./detectron2/setup.py\")\n",
+        "!python -m pip install {' '.join([f\"'{x}'\" for x in dist.install_requires])}\n",
+        "sys.path.insert(0, os.path.abspath('./detectron2'))\n",
+        "clear_output()"
+      ],
+      "metadata": {
+        "id": "bhO0vDub_o5s"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import torch, detectron2\n",
+        "from detectron2.utils.logger import setup_logger\n",
+        "setup_logger()\n",
+        "print(\"detectron2 version:\", detectron2.__version__)\n",
+        "\n",
+        "import numpy as np\n",
+        "import os, json, cv2, random , pickle\n",
+        "from IPython import display\n",
+        "import PIL\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# importing detectron2 utilities\n",
+        "from detectron2 import model_zoo\n",
+        "from detectron2.engine import DefaultPredictor\n",
+        "from detectron2.config import get_cfg\n",
+        "from detectron2.utils.visualizer import Visualizer\n",
+        "from detectron2.data import MetadataCatalog, DatasetCatalog\n",
+        "\n",
+        "\n",
+        "SEED = 9\n",
+        "THRESHOLD = 0.3"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "5TO66S06gjCT",
+        "outputId": "9d36403d-edba-4460-c82e-75216a8ba90c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "detectron2 version: 0.6\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from detectron2.data.datasets import register_coco_instances\n",
+        "from detectron2.structures import BoxMode\n",
+        "\n",
+        "base_path = '/content/drive/MyDrive/BoneFractureDataset'\n",
+        "\n",
+        "for d in [\"train\", \"valid\", \"test\"]:\n",
+        "    register_coco_instances(f\"bone_fractures_{d}\", {},\n",
+        "                            f\"{base_path}/{d}_annotations.coco.json\",\n",
+        "                            f\"{base_path}/{d}/images\")"
+      ],
+      "metadata": {
+        "id": "UkX4dvGLhe79"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Base Model : ResnetXt"
+      ],
+      "metadata": {
+        "id": "7HYCLgnLvQUE"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 1.1 Training the Model"
+      ],
+      "metadata": {
+        "id": "PI2EidjWHleF"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from detectron2.engine import DefaultTrainer\n",
+        "\n",
+        "EPOCHS = 1000\n",
+        "NUM_CLASSES = 8 #7 for the one they have provided and 1 for when there is no fracture\n",
+        "BASE_LR = 0.001\n",
+        "\n",
+        "cfg = get_cfg()\n",
+        "cfg.merge_from_file(model_zoo.get_config_file(\"COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml\"))\n",
+        "cfg.DATASETS.TRAIN = (\"bone_fractures_train\")\n",
+        "cfg.DATASETS.TEST = ()\n",
+        "cfg.DATALOADER.NUM_WORKERS = 2\n",
+        "cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(\"COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml\")\n",
+        "cfg.SOLVER.IMS_PER_BATCH = 2\n",
+        "cfg.SOLVER.BASE_LR = BASE_LR\n",
+        "cfg.SOLVER.MAX_ITER = EPOCHS\n",
+        "cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 512\n",
+        "cfg.MODEL.ROI_HEADS.NUM_CLASSES = NUM_CLASSES\n",
+        "\n",
+        "os.makedirs(cfg.OUTPUT_DIR, exist_ok=True)\n",
+        "\n",
+        "# Uncomment the following to train\n",
+        "'''trainer = DefaultTrainer(cfg)\n",
+        "trainer.resume_or_load(resume=False)\n",
+        "trainer.train()'''"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 35
+        },
+        "id": "1A40Iegvn0zn",
+        "outputId": "469f646a-1756-4a75-a902-fe9d2335c28e"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'trainer = DefaultTrainer(cfg)\\ntrainer.resume_or_load(resume=False)\\ntrainer.train()'"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cfg.MODEL.WEIGHTS = os.path.join('/content/drive/MyDrive/BoneFractureDataset/output', \"model_final.pth\")\n",
+        "cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = THRESHOLD"
+      ],
+      "metadata": {
+        "id": "lz4LQQ8l0AKc"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "with open(\"/content/drive/MyDrive/BoneFractureDataset/cfg.pkl\", \"wb\") as f:\n",
+        "    pickle.dump(cfg , f)"
+      ],
+      "metadata": {
+        "id": "HBvdjPyX2d0W"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 1.2 Training Results"
+      ],
+      "metadata": {
+        "id": "Rp7J6sn4FDpE"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def reading_metrics_from_json(metrics_path):\n",
+        "    metrics = []\n",
+        "\n",
+        "    with open(metrics_path, 'r') as f:\n",
+        "        for line in f:\n",
+        "            metrics.append(json.loads(line))\n",
+        "    return metrics\n",
+        "\n",
+        "\n",
+        "def metric(metrics, specific_metric):\n",
+        "    metric = []\n",
+        "    for i in range(len(metrics)):\n",
+        "        try:\n",
+        "            metric.append(metrics[i][specific_metric])\n",
+        "        except KeyError:\n",
+        "            pass\n",
+        "\n",
+        "    return metric\n",
+        "\n",
+        "train_metrics = reading_metrics_from_json('/content/drive/MyDrive/BoneFractureDataset/output/metrics.json')\n",
+        "\n",
+        "total_loss = metric(train_metrics, 'total_loss')\n",
+        "cls_acc = metric(train_metrics, 'fast_rcnn/cls_accuracy')\n",
+        "false_neg = metric(train_metrics, 'fast_rcnn/false_negative')\n",
+        "loss_box_reg = metric(train_metrics, 'loss_box_reg')\n",
+        "iters = np.arange(1,EPOCHS,EPOCHS/len(total_loss))\n",
+        "\n",
+        "fig, axs = plt.subplots(1,4, figsize = (17, 4), dpi = 120)\n",
+        "fig.suptitle('Faster R-CNN w/ResNeXt 101 FPN', color = 'blue', fontsize=15,  style = \"italic\")\n",
+        "\n",
+        "axs[0].grid(linestyle = 'dashdot')\n",
+        "axs[0].plot(iters, loss_box_reg)\n",
+        "axs[0].set_xlabel('epochs', fontsize = 10)\n",
+        "axs[0].set_title('Loss Box Regression', fontsize = 10)\n",
+        "tit0 = ' (the last value {0:.4f})'.format(loss_box_reg[-1])\n",
+        "axs[0].set_title('Loss Box Regression ' + tit0, fontsize = 10, color = 'red')\n",
+        "\n",
+        "axs[1].grid(linestyle = 'dashdot')\n",
+        "axs[1].plot(iters, cls_acc)\n",
+        "axs[1].set_xlabel('epochs', fontsize = 10)\n",
+        "tit1 = ' (the last value {0:.3f})'.format(cls_acc[-1])\n",
+        "axs[1].set_title('Class Accuracy ' + tit1, fontsize = 10, color = 'red')\n",
+        "\n",
+        "axs[2].grid(linestyle = 'dashdot')\n",
+        "axs[2].plot(iters, total_loss)\n",
+        "axs[2].set_xlabel('epochs', fontsize = 10)\n",
+        "tit2 = ' (the last value {0:.4f})'.format(total_loss[-1])\n",
+        "axs[2].set_title('Total Loss ' + tit2, fontsize = 10, color = 'red')\n",
+        "\n",
+        "axs[3].grid(linestyle = 'dashdot')\n",
+        "axs[3].plot(iters, false_neg)\n",
+        "axs[3].set_xlabel('epochs', fontsize = 10)\n",
+        "axs[3].set_title('False Negative', fontsize = 10, color = 'red')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 480
+        },
+        "id": "yydPY__aFB-A",
+        "outputId": "fd4d488b-791c-4554-e22a-56e283dfad06"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0.5, 1.0, 'False Negative')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 7
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 2040x480 with 4 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "with open(\"/content/drive/MyDrive/BoneFractureDataset/cfg.pkl\", \"rb\") as f:\n",
+        "    cfg = pickle.load(f)\n",
+        "\n",
+        "cfg.MODEL.DEVICE = \"cpu\"\n",
+        "cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.1\n",
+        "cfg.OUTPUT_DIR = '/content/drive/MyDrive/BoneFractureDataset/output/'\n",
+        "predictor = DefaultPredictor(cfg)"
+      ],
+      "metadata": {
+        "id": "VnCTliRW6Lns",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "8b0a5c25-f318-4445-e5c1-6701c4d827eb"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[05/27 15:45:06 d2.checkpoint.detection_checkpoint]: [DetectionCheckpointer] Loading from /content/drive/MyDrive/BoneFractureDataset/output/model_final.pth ...\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import cv2\n",
+        "from detectron2.utils.visualizer import Visualizer\n",
+        "\n",
+        "# Some Random Image from the dataset\n",
+        "img_path = '/content/drive/MyDrive/BoneFractureDataset/train/images/fracture-of-the-humeral-capitellum-milch-type-1-1-1-_jpg.rf.f40a3ca2a57d511a40839bd1ca615d54.jpg'\n",
+        "img = cv2.imread(img_path)\n",
+        "img = cv2.resize(img , (256 , 256))\n",
+        "img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)\n",
+        "output = predictor(img)\n",
+        "\n",
+        "instances = output[\"instances\"]\n",
+        "boxes = instances.pred_boxes if instances.has(\"pred_boxes\") else None\n",
+        "class_id = instances.pred_classes if instances.has(\"pred_classes\") else None\n",
+        "\n",
+        "metadata = MetadataCatalog.get(cfg.DATASETS.TRAIN[0])\n",
+        "\n",
+        "v = Visualizer(img, metadata, scale=1.2)\n",
+        "v = v.draw_instance_predictions(instances.to(\"cpu\"))\n",
+        "result_img = v.get_image()\n",
+        "\n",
+        "# Display the image with bounding boxes\n",
+        "plt.figure(figsize=(10, 10))\n",
+        "plt.imshow(result_img)\n",
+        "plt.axis(\"off\")\n",
+        "plt.show()\n",
+        "\n",
+        "if boxes is not None:\n",
+        "    boxes = boxes.tensor.cpu().numpy()\n",
+        "if class_id is not None:\n",
+        "    class_names = ['elbow positive', 'fingers positive', 'forearm fracture', 'humerus fracture', 'humerus', 'shoulder fracture', 'wrist positive' , 'No Fracture']\n",
+        "    class_id = class_id.cpu().numpy()\n",
+        "    classes = class_names[int(class_id[0])]\n",
+        "\n",
+        "print('\\n'+ '-' * 40)\n",
+        "print(f\"Bounding Boxes : {boxes}\")\n",
+        "print(f\"Type of fracture : {classes}\")\n",
+        "print( '-' * 40)"
+      ],
+      "metadata": {
+        "id": "OWysh9r34bwt",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 948
+        },
+        "outputId": "618fe7ad-4512-4613-cc7b-bcd1c05dea42"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/torch/functional.py:512: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3587.)\n",
+            "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x1000 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "----------------------------------------\n",
+            "Bounding Boxes : [[158.39722 106.50416 199.72261 152.4228 ]]\n",
+            "Type of fracture : humerus fracture\n",
+            "----------------------------------------\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "fEL14ypfrhXg"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Bone Fracture Detection/Models/Bone_Fracture_YOLO.ipynb b/Bone Fracture Detection/Models/Bone_Fracture_YOLO.ipynb
new file mode 100644
index 000000000..c7fe613f9
--- /dev/null
+++ b/Bone Fracture Detection/Models/Bone_Fracture_YOLO.ipynb	
@@ -0,0 +1,2337 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "collapsed_sections": [
+        "1JQx_OMIDK2Q",
+        "FFVX3ILi7tll",
+        "mAcQJZ26GEcu",
+        "ET405KdVTYhu"
+      ]
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Using YOLO-V8 Model\n",
+        "---"
+      ],
+      "metadata": {
+        "id": "1JQx_OMIDK2Q"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "yOgyJVR769--",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "38117aaf-8dfe-43b6-f710-05494cbd5845"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting ultralytics\n",
+            "  Downloading ultralytics-8.2.18-py3-none-any.whl (757 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m757.2/757.2 kB\u001b[0m \u001b[31m11.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: matplotlib>=3.3.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (3.7.1)\n",
+            "Requirement already satisfied: opencv-python>=4.6.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (4.8.0.76)\n",
+            "Requirement already satisfied: pillow>=7.1.2 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (9.4.0)\n",
+            "Requirement already satisfied: pyyaml>=5.3.1 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (6.0.1)\n",
+            "Requirement already satisfied: requests>=2.23.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (2.31.0)\n",
+            "Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (1.11.4)\n",
+            "Requirement already satisfied: torch>=1.8.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (2.2.1+cu121)\n",
+            "Requirement already satisfied: torchvision>=0.9.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (0.17.1+cu121)\n",
+            "Requirement already satisfied: tqdm>=4.64.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (4.66.4)\n",
+            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from ultralytics) (5.9.5)\n",
+            "Requirement already satisfied: py-cpuinfo in /usr/local/lib/python3.10/dist-packages (from ultralytics) (9.0.0)\n",
+            "Collecting thop>=0.1.1 (from ultralytics)\n",
+            "  Downloading thop-0.1.1.post2209072238-py3-none-any.whl (15 kB)\n",
+            "Requirement already satisfied: pandas>=1.1.4 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (2.0.3)\n",
+            "Requirement already satisfied: seaborn>=0.11.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (0.13.1)\n",
+            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (1.2.1)\n",
+            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (0.12.1)\n",
+            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (4.51.0)\n",
+            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (1.4.5)\n",
+            "Requirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (1.25.2)\n",
+            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (24.0)\n",
+            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (3.1.2)\n",
+            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (2.8.2)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.1.4->ultralytics) (2023.4)\n",
+            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.1.4->ultralytics) (2024.1)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->ultralytics) (3.3.2)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->ultralytics) (3.7)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->ultralytics) (2.0.7)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->ultralytics) (2024.2.2)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (3.14.0)\n",
+            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (4.11.0)\n",
+            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (1.12)\n",
+            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (3.3)\n",
+            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (3.1.4)\n",
+            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (2023.6.0)\n",
+            "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch>=1.8.0->ultralytics)\n",
+            "  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
+            "Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch>=1.8.0->ultralytics)\n",
+            "  Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n",
+            "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch>=1.8.0->ultralytics)\n",
+            "  Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
+            "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch>=1.8.0->ultralytics)\n",
+            "  Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
+            "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch>=1.8.0->ultralytics)\n",
+            "  Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
+            "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch>=1.8.0->ultralytics)\n",
+            "  Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",
+            "Collecting nvidia-curand-cu12==10.3.2.106 (from torch>=1.8.0->ultralytics)\n",
+            "  Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n",
+            "Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch>=1.8.0->ultralytics)\n",
+            "  Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
+            "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch>=1.8.0->ultralytics)\n",
+            "  Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
+            "Collecting nvidia-nccl-cu12==2.19.3 (from torch>=1.8.0->ultralytics)\n",
+            "  Using cached nvidia_nccl_cu12-2.19.3-py3-none-manylinux1_x86_64.whl (166.0 MB)\n",
+            "Collecting nvidia-nvtx-cu12==12.1.105 (from torch>=1.8.0->ultralytics)\n",
+            "  Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",
+            "Requirement already satisfied: triton==2.2.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (2.2.0)\n",
+            "Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.8.0->ultralytics)\n",
+            "  Using cached nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (21.1 MB)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib>=3.3.0->ultralytics) (1.16.0)\n",
+            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.8.0->ultralytics) (2.1.5)\n",
+            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.8.0->ultralytics) (1.3.0)\n",
+            "Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, thop, ultralytics\n",
+            "Successfully installed nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.127 nvidia-nvtx-cu12-12.1.105 thop-0.1.1.post2209072238 ultralytics-8.2.18\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install ultralytics"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!nvidia-smi"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "HXMN1g7j4hto",
+        "outputId": "f7889ac8-befa-4905-cb3b-4ab5a945e100"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Tue May 21 11:22:11 2024       \n",
+            "+---------------------------------------------------------------------------------------+\n",
+            "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
+            "|-----------------------------------------+----------------------+----------------------+\n",
+            "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
+            "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
+            "|                                         |                      |               MIG M. |\n",
+            "|=========================================+======================+======================|\n",
+            "|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\n",
+            "| N/A   47C    P8              12W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
+            "|                                         |                      |                  N/A |\n",
+            "+-----------------------------------------+----------------------+----------------------+\n",
+            "                                                                                         \n",
+            "+---------------------------------------------------------------------------------------+\n",
+            "| Processes:                                                                            |\n",
+            "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
+            "|        ID   ID                                                             Usage      |\n",
+            "|=======================================================================================|\n",
+            "|  No running processes found                                                           |\n",
+            "+---------------------------------------------------------------------------------------+\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Modules to be imported\n",
+        "\n",
+        "---\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "FFVX3ILi7tll"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import os\n",
+        "import zipfile\n",
+        "import cv2\n",
+        "import matplotlib.pyplot as plt\n",
+        "from ultralytics import YOLO"
+      ],
+      "metadata": {
+        "id": "9pvadZR17J0M"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Importing Dataset\n",
+        "---"
+      ],
+      "metadata": {
+        "id": "mAcQJZ26GEcu"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "0u9tKRTTGeEi",
+        "outputId": "ef684701-0cab-48ba-ce9e-6d0c2e6dd9ec"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mounted at /content/drive\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# To Download the dataset and extract all the files\n",
+        "!pip install -q kaggle\n",
+        "\n",
+        "!mkdir -p ~/.kaggle\n",
+        "!cp kaggle.json ~/.kaggle/\n",
+        "!chmod 600 ~/.kaggle/kaggle.json\n",
+        "\n",
+        "!kaggle datasets download -d pkdarabi/bone-fracture-detection-computer-vision-project -p /content/drive/MyDrive/BoneFractureDataset"
+      ],
+      "metadata": {
+        "id": "Emsbr3m-GCjp"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "dataset_path = \"/content/drive/MyDrive/BoneFractureDataset\"\n",
+        "with zipfile.ZipFile(os.path.join(dataset_path , 'bone-fracture-detection-computer-vision-project.zip') , 'r') as zip_file:\n",
+        "  zip_file.extractall(dataset_path)"
+      ],
+      "metadata": {
+        "id": "8k_Lwva5Crm6"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Training YOLO Model\n",
+        "---"
+      ],
+      "metadata": {
+        "id": "ET405KdVTYhu"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_model = YOLO('yolov8m.pt')\n",
+        "train_model.train(data = '/content/drive/MyDrive/BoneFractureDataset/data.yaml' , epochs = 50 , imgsz = 256 , device = 'cuda')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "iIIwgUGG2Wam",
+        "outputId": "a2699363-927c-47e7-fd4d-ed512c3c965a"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Ultralytics YOLOv8.2.18 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
+            "\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=detect, mode=train, model=yolov8n.pt, data=/content/drive/MyDrive/BoneFractureDataset/data.yaml, epochs=50, time=None, patience=100, batch=16, imgsz=256, save=True, save_period=-1, cache=False, device=cuda, workers=8, project=None, name=train3, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train3\n",
+            "Overriding model.yaml nc=80 with nc=7\n",
+            "\n",
+            "                   from  n    params  module                                       arguments                     \n",
+            "  0                  -1  1       464  ultralytics.nn.modules.conv.Conv             [3, 16, 3, 2]                 \n",
+            "  1                  -1  1      4672  ultralytics.nn.modules.conv.Conv             [16, 32, 3, 2]                \n",
+            "  2                  -1  1      7360  ultralytics.nn.modules.block.C2f             [32, 32, 1, True]             \n",
+            "  3                  -1  1     18560  ultralytics.nn.modules.conv.Conv             [32, 64, 3, 2]                \n",
+            "  4                  -1  2     49664  ultralytics.nn.modules.block.C2f             [64, 64, 2, True]             \n",
+            "  5                  -1  1     73984  ultralytics.nn.modules.conv.Conv             [64, 128, 3, 2]               \n",
+            "  6                  -1  2    197632  ultralytics.nn.modules.block.C2f             [128, 128, 2, True]           \n",
+            "  7                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              \n",
+            "  8                  -1  1    460288  ultralytics.nn.modules.block.C2f             [256, 256, 1, True]           \n",
+            "  9                  -1  1    164608  ultralytics.nn.modules.block.SPPF            [256, 256, 5]                 \n",
+            " 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
+            " 11             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
+            " 12                  -1  1    148224  ultralytics.nn.modules.block.C2f             [384, 128, 1]                 \n",
+            " 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
+            " 14             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
+            " 15                  -1  1     37248  ultralytics.nn.modules.block.C2f             [192, 64, 1]                  \n",
+            " 16                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]                \n",
+            " 17            [-1, 12]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
+            " 18                  -1  1    123648  ultralytics.nn.modules.block.C2f             [192, 128, 1]                 \n",
+            " 19                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              \n",
+            " 20             [-1, 9]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
+            " 21                  -1  1    493056  ultralytics.nn.modules.block.C2f             [384, 256, 1]                 \n",
+            " 22        [15, 18, 21]  1    752677  ultralytics.nn.modules.head.Detect           [7, [64, 128, 256]]           \n",
+            "Model summary: 225 layers, 3012213 parameters, 3012197 gradients, 8.2 GFLOPs\n",
+            "\n",
+            "Transferred 319/355 items from pretrained weights\n",
+            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/detect/train3', view at http://localhost:6006/\n",
+            "Freezing layer 'model.22.dfl.conv.weight'\n",
+            "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks with YOLOv8n...\n",
+            "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/drive/MyDrive/BoneFractureDataset/train/labels.cache... 3631 images, 1827 backgrounds, 0 corrupt: 100%|██████████| 3631/3631 [00:00<?, ?it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
+            "  self.pid = os.fork()\n",
+            "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/drive/MyDrive/BoneFractureDataset/valid/labels.cache... 348 images, 175 backgrounds, 0 corrupt: 100%|██████████| 348/348 [00:00<?, ?it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Plotting labels to runs/detect/train3/labels.jpg... \n",
+            "\u001b[34m\u001b[1moptimizer:\u001b[0m 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... \n",
+            "\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.000909, momentum=0.9) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias(decay=0.0)\n",
+            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mmodel graph visualization added ✅\n",
+            "Image sizes 256 train, 256 val\n",
+            "Using 2 dataloader workers\n",
+            "Logging results to \u001b[1mruns/detect/train3\u001b[0m\n",
+            "Starting training for 50 epochs...\n",
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "       1/50     0.755G      3.402      5.153      1.987         20        256: 100%|██████████| 227/227 [02:06<00:00,  1.79it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:07<00:00,  1.41it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204      0.175     0.0152    0.00834    0.00286\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "       2/50     0.774G      2.991      4.095      1.698         13        256: 100%|██████████| 227/227 [00:56<00:00,  3.99it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  2.99it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204      0.548     0.0428     0.0226    0.00643\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "       3/50     0.765G      2.909      3.652      1.708          8        256: 100%|██████████| 227/227 [00:57<00:00,  3.98it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.54it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204      0.403      0.111      0.057     0.0179\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "       4/50     0.765G      2.857      3.557      1.641         21        256: 100%|██████████| 227/227 [00:56<00:00,  3.98it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.42it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204      0.187     0.0865     0.0296     0.0112\n",
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "       5/50     0.765G      2.771      3.283       1.61         24        256: 100%|██████████| 227/227 [00:56<00:00,  4.01it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.14it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204      0.113       0.12     0.0725     0.0207\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "       6/50     0.763G      2.728      3.139      1.573         13        256: 100%|██████████| 227/227 [00:56<00:00,  4.03it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.13it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204       0.17      0.153      0.106     0.0365\n",
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "       7/50     0.763G      2.674      2.998       1.56         23        256: 100%|██████████| 227/227 [00:57<00:00,  3.97it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.15it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204      0.302      0.165      0.116     0.0369\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "       8/50     0.763G      2.671       2.99      1.553         12        256: 100%|██████████| 227/227 [00:55<00:00,  4.11it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.99it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204      0.155      0.153      0.105     0.0382\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "       9/50     0.763G      2.618      2.869      1.536         20        256: 100%|██████████| 227/227 [00:57<00:00,  3.98it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.26it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204       0.18       0.15      0.124     0.0423\n",
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "      10/50     0.763G      2.565       2.77      1.504         19        256: 100%|██████████| 227/227 [00:57<00:00,  3.97it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.08it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204      0.185      0.135      0.125     0.0469\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "      11/50     0.763G       2.53      2.737      1.481          7        256: 100%|██████████| 227/227 [00:56<00:00,  4.04it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.14it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204      0.238      0.166      0.161     0.0599\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "      12/50     0.768G      2.503      2.668       1.47         13        256: 100%|██████████| 227/227 [00:56<00:00,  3.98it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.27it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204       0.26      0.217      0.153     0.0585\n",
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "      13/50     0.763G      2.473      2.537      1.469         11        256: 100%|██████████| 227/227 [00:56<00:00,  4.05it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.30it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204      0.176      0.231      0.165     0.0489\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "      14/50     0.763G       2.42      2.492      1.439         16        256: 100%|██████████| 227/227 [00:55<00:00,  4.09it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.11it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204      0.259      0.257      0.198     0.0655\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "      15/50     0.763G      2.381       2.51      1.437         13        256: 100%|██████████| 227/227 [00:54<00:00,  4.14it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:04<00:00,  2.41it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204      0.247      0.188      0.175     0.0637\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "      16/50     0.763G      2.368      2.434      1.429         15        256: 100%|██████████| 227/227 [00:54<00:00,  4.18it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:04<00:00,  2.41it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204       0.45      0.158      0.171     0.0598\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "      17/50     0.763G      2.385      2.373      1.421         17        256: 100%|██████████| 227/227 [00:55<00:00,  4.08it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  2.76it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204      0.277      0.271      0.214     0.0697\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "      18/50     0.763G      2.331      2.352       1.41         10        256: 100%|██████████| 227/227 [00:55<00:00,  4.07it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:04<00:00,  2.61it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204      0.292      0.168      0.179     0.0651\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "      19/50     0.765G      2.264      2.278      1.387         17        256: 100%|██████████| 227/227 [00:54<00:00,  4.17it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:04<00:00,  2.44it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204      0.323      0.203      0.193     0.0628\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "      20/50     0.763G      2.326      2.282      1.393         21        256: 100%|██████████| 227/227 [00:53<00:00,  4.21it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:05<00:00,  2.14it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204      0.239      0.256      0.193     0.0707\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "      21/50     0.763G       2.29      2.198      1.377         13        256: 100%|██████████| 227/227 [00:54<00:00,  4.16it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:04<00:00,  2.32it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204      0.293      0.281      0.216     0.0742\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "      22/50     0.763G      2.211      2.144      1.356         18        256: 100%|██████████| 227/227 [00:54<00:00,  4.20it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:04<00:00,  2.41it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204       0.28      0.292      0.211     0.0706\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "      23/50     0.763G      2.218      2.189      1.342         13        256: 100%|██████████| 227/227 [00:54<00:00,  4.20it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:05<00:00,  2.08it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204      0.301      0.277      0.245     0.0847\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "      24/50     0.765G      2.205      2.119      1.363         19        256: 100%|██████████| 227/227 [00:54<00:00,  4.18it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:04<00:00,  2.26it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204      0.292      0.279      0.209     0.0714\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "      25/50     0.763G      2.193      2.077      1.344         14        256: 100%|██████████| 227/227 [00:56<00:00,  4.00it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:04<00:00,  2.71it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204      0.384      0.262      0.234     0.0836\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "      26/50     0.763G      2.132      2.044      1.324         10        256: 100%|██████████| 227/227 [00:56<00:00,  4.03it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.45it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204      0.379      0.204      0.229     0.0798\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "      27/50     0.763G      2.125      1.969      1.322         17        256: 100%|██████████| 227/227 [00:56<00:00,  3.99it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.55it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204       0.26      0.308       0.24       0.08\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "      28/50     0.763G      2.144      1.959      1.322         11        256: 100%|██████████| 227/227 [00:56<00:00,  4.04it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.05it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204      0.322      0.287      0.233     0.0762\n",
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "      29/50     0.763G      2.121      1.925      1.303         11        256: 100%|██████████| 227/227 [00:57<00:00,  3.98it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.08it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204      0.342      0.217      0.212     0.0656\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "      30/50     0.763G      2.107      1.896      1.308          8        256: 100%|██████████| 227/227 [00:57<00:00,  3.92it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.11it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204      0.266      0.273      0.215     0.0775\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "      31/50     0.763G      2.082      1.877      1.302         12        256: 100%|██████████| 227/227 [00:57<00:00,  3.98it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.37it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204      0.299      0.212      0.195     0.0648\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "      32/50     0.763G      2.066      1.827      1.289         14        256: 100%|██████████| 227/227 [00:57<00:00,  3.96it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.39it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204      0.311      0.253      0.237     0.0762\n",
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "      33/50     0.763G       2.04      1.824       1.28         16        256: 100%|██████████| 227/227 [00:57<00:00,  3.98it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.17it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204      0.278       0.28      0.248     0.0854\n",
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "      34/50     0.763G      2.013      1.737      1.271         17        256: 100%|██████████| 227/227 [00:57<00:00,  3.97it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.20it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204      0.334      0.255      0.238     0.0811\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "      35/50     0.763G       1.99      1.736      1.265         18        256: 100%|██████████| 227/227 [00:56<00:00,  3.99it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.29it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204      0.273      0.262      0.214     0.0701\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "      36/50     0.763G      1.976      1.719      1.245         13        256: 100%|██████████| 227/227 [00:57<00:00,  3.92it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.34it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204      0.284      0.253      0.211     0.0723\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "      37/50     0.763G      1.957      1.683      1.245         15        256: 100%|██████████| 227/227 [00:55<00:00,  4.06it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.15it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204      0.302       0.26      0.239     0.0774\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "      38/50     0.763G       1.93      1.644      1.238         15        256: 100%|██████████| 227/227 [00:56<00:00,  4.00it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  2.98it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204      0.307      0.244      0.221     0.0715\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "      39/50     0.763G      1.928      1.644      1.226         17        256: 100%|██████████| 227/227 [00:54<00:00,  4.13it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:04<00:00,  2.50it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204      0.284      0.267      0.217     0.0754\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "      40/50     0.765G      1.914      1.608      1.216         22        256: 100%|██████████| 227/227 [00:54<00:00,  4.15it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:04<00:00,  2.49it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204      0.292      0.242      0.221     0.0748\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Closing dataloader mosaic\n",
+            "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
+            "  self.pid = os.fork()\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "      41/50     0.763G      1.865      1.518      1.267          9        256: 100%|██████████| 227/227 [00:51<00:00,  4.37it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:04<00:00,  2.49it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204      0.254      0.265      0.213     0.0751\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "      42/50     0.763G      1.792      1.431      1.231          9        256: 100%|██████████| 227/227 [00:51<00:00,  4.38it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.43it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204       0.26      0.251       0.21     0.0701\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "      43/50     0.763G      1.791      1.394       1.22          8        256: 100%|██████████| 227/227 [00:54<00:00,  4.19it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.58it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204      0.263      0.283      0.214     0.0725\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "      44/50     0.763G      1.761      1.382      1.208          6        256: 100%|██████████| 227/227 [00:56<00:00,  4.05it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.52it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204      0.306      0.226      0.213      0.074\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "      45/50     0.763G      1.743      1.331        1.2          8        256: 100%|██████████| 227/227 [00:55<00:00,  4.11it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  4.65it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204      0.286      0.256      0.222      0.074\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "      46/50     0.763G      1.716      1.325      1.197          7        256: 100%|██████████| 227/227 [00:54<00:00,  4.18it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.26it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204      0.304      0.254      0.224     0.0716\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "      47/50     0.763G      1.698      1.282      1.181          8        256: 100%|██████████| 227/227 [00:53<00:00,  4.24it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:04<00:00,  2.24it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204      0.265      0.258       0.21     0.0711\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "      48/50     0.763G      1.682      1.272      1.175         14        256: 100%|██████████| 227/227 [01:01<00:00,  3.67it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:02<00:00,  3.93it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204      0.326      0.265      0.225     0.0736\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "      49/50     0.763G      1.657      1.248      1.163         11        256: 100%|██████████| 227/227 [00:52<00:00,  4.33it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:04<00:00,  2.28it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204      0.308      0.257      0.224     0.0747\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "      50/50     0.763G      1.636      1.242      1.154          7        256: 100%|██████████| 227/227 [00:52<00:00,  4.33it/s]\n",
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:04<00:00,  2.50it/s]"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204      0.336      0.239      0.217     0.0736\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "50 epochs completed in 0.862 hours.\n",
+            "Optimizer stripped from runs/detect/train3/weights/last.pt, 6.2MB\n",
+            "Optimizer stripped from runs/detect/train3/weights/best.pt, 6.2MB\n",
+            "\n",
+            "Validating runs/detect/train3/weights/best.pt...\n",
+            "Ultralytics YOLOv8.2.18 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
+            "Model summary (fused): 168 layers, 3007013 parameters, 0 gradients, 8.1 GFLOPs\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 11/11 [00:03<00:00,  3.34it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                   all        348        204      0.278       0.28      0.248     0.0847\n",
+            "        elbow positive        348         29     0.0614     0.0345     0.0294    0.00742\n",
+            "      fingers positive        348         48       0.39      0.229      0.245     0.0645\n",
+            "      forearm fracture        348         43      0.446      0.488      0.485       0.17\n",
+            "               humerus        348         36      0.528      0.556      0.556      0.214\n",
+            "     shoulder fracture        348         20      0.164        0.3       0.15      0.046\n",
+            "        wrist positive        348         28     0.0756     0.0714     0.0246    0.00657\n",
+            "Speed: 0.1ms preprocess, 1.3ms inference, 0.0ms loss, 2.3ms postprocess per image\n",
+            "Results saved to \u001b[1mruns/detect/train3\u001b[0m\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "ultralytics.utils.metrics.DetMetrics object with attributes:\n",
+              "\n",
+              "ap_class_index: array([0, 1, 2, 4, 5, 6])\n",
+              "box: ultralytics.utils.metrics.Metric object\n",
+              "confusion_matrix: <ultralytics.utils.metrics.ConfusionMatrix object at 0x7a4e67c83250>\n",
+              "curves: ['Precision-Recall(B)', 'F1-Confidence(B)', 'Precision-Confidence(B)', 'Recall-Confidence(B)']\n",
+              "curves_results: [[array([          0,    0.001001,    0.002002,    0.003003,    0.004004,    0.005005,    0.006006,    0.007007,    0.008008,    0.009009,     0.01001,    0.011011,    0.012012,    0.013013,    0.014014,    0.015015,    0.016016,    0.017017,    0.018018,    0.019019,     0.02002,    0.021021,    0.022022,    0.023023,\n",
+              "          0.024024,    0.025025,    0.026026,    0.027027,    0.028028,    0.029029,     0.03003,    0.031031,    0.032032,    0.033033,    0.034034,    0.035035,    0.036036,    0.037037,    0.038038,    0.039039,     0.04004,    0.041041,    0.042042,    0.043043,    0.044044,    0.045045,    0.046046,    0.047047,\n",
+              "          0.048048,    0.049049,     0.05005,    0.051051,    0.052052,    0.053053,    0.054054,    0.055055,    0.056056,    0.057057,    0.058058,    0.059059,     0.06006,    0.061061,    0.062062,    0.063063,    0.064064,    0.065065,    0.066066,    0.067067,    0.068068,    0.069069,     0.07007,    0.071071,\n",
+              "          0.072072,    0.073073,    0.074074,    0.075075,    0.076076,    0.077077,    0.078078,    0.079079,     0.08008,    0.081081,    0.082082,    0.083083,    0.084084,    0.085085,    0.086086,    0.087087,    0.088088,    0.089089,     0.09009,    0.091091,    0.092092,    0.093093,    0.094094,    0.095095,\n",
+              "          0.096096,    0.097097,    0.098098,    0.099099,      0.1001,      0.1011,      0.1021,      0.1031,      0.1041,     0.10511,     0.10611,     0.10711,     0.10811,     0.10911,     0.11011,     0.11111,     0.11211,     0.11311,     0.11411,     0.11512,     0.11612,     0.11712,     0.11812,     0.11912,\n",
+              "           0.12012,     0.12112,     0.12212,     0.12312,     0.12412,     0.12513,     0.12613,     0.12713,     0.12813,     0.12913,     0.13013,     0.13113,     0.13213,     0.13313,     0.13413,     0.13514,     0.13614,     0.13714,     0.13814,     0.13914,     0.14014,     0.14114,     0.14214,     0.14314,\n",
+              "           0.14414,     0.14515,     0.14615,     0.14715,     0.14815,     0.14915,     0.15015,     0.15115,     0.15215,     0.15315,     0.15415,     0.15516,     0.15616,     0.15716,     0.15816,     0.15916,     0.16016,     0.16116,     0.16216,     0.16316,     0.16416,     0.16517,     0.16617,     0.16717,\n",
+              "           0.16817,     0.16917,     0.17017,     0.17117,     0.17217,     0.17317,     0.17417,     0.17518,     0.17618,     0.17718,     0.17818,     0.17918,     0.18018,     0.18118,     0.18218,     0.18318,     0.18418,     0.18519,     0.18619,     0.18719,     0.18819,     0.18919,     0.19019,     0.19119,\n",
+              "           0.19219,     0.19319,     0.19419,      0.1952,      0.1962,      0.1972,      0.1982,      0.1992,      0.2002,      0.2012,      0.2022,      0.2032,      0.2042,     0.20521,     0.20621,     0.20721,     0.20821,     0.20921,     0.21021,     0.21121,     0.21221,     0.21321,     0.21421,     0.21522,\n",
+              "           0.21622,     0.21722,     0.21822,     0.21922,     0.22022,     0.22122,     0.22222,     0.22322,     0.22422,     0.22523,     0.22623,     0.22723,     0.22823,     0.22923,     0.23023,     0.23123,     0.23223,     0.23323,     0.23423,     0.23524,     0.23624,     0.23724,     0.23824,     0.23924,\n",
+              "           0.24024,     0.24124,     0.24224,     0.24324,     0.24424,     0.24525,     0.24625,     0.24725,     0.24825,     0.24925,     0.25025,     0.25125,     0.25225,     0.25325,     0.25425,     0.25526,     0.25626,     0.25726,     0.25826,     0.25926,     0.26026,     0.26126,     0.26226,     0.26326,\n",
+              "           0.26426,     0.26527,     0.26627,     0.26727,     0.26827,     0.26927,     0.27027,     0.27127,     0.27227,     0.27327,     0.27427,     0.27528,     0.27628,     0.27728,     0.27828,     0.27928,     0.28028,     0.28128,     0.28228,     0.28328,     0.28428,     0.28529,     0.28629,     0.28729,\n",
+              "           0.28829,     0.28929,     0.29029,     0.29129,     0.29229,     0.29329,     0.29429,      0.2953,      0.2963,      0.2973,      0.2983,      0.2993,      0.3003,      0.3013,      0.3023,      0.3033,      0.3043,     0.30531,     0.30631,     0.30731,     0.30831,     0.30931,     0.31031,     0.31131,\n",
+              "           0.31231,     0.31331,     0.31431,     0.31532,     0.31632,     0.31732,     0.31832,     0.31932,     0.32032,     0.32132,     0.32232,     0.32332,     0.32432,     0.32533,     0.32633,     0.32733,     0.32833,     0.32933,     0.33033,     0.33133,     0.33233,     0.33333,     0.33433,     0.33534,\n",
+              "           0.33634,     0.33734,     0.33834,     0.33934,     0.34034,     0.34134,     0.34234,     0.34334,     0.34434,     0.34535,     0.34635,     0.34735,     0.34835,     0.34935,     0.35035,     0.35135,     0.35235,     0.35335,     0.35435,     0.35536,     0.35636,     0.35736,     0.35836,     0.35936,\n",
+              "           0.36036,     0.36136,     0.36236,     0.36336,     0.36436,     0.36537,     0.36637,     0.36737,     0.36837,     0.36937,     0.37037,     0.37137,     0.37237,     0.37337,     0.37437,     0.37538,     0.37638,     0.37738,     0.37838,     0.37938,     0.38038,     0.38138,     0.38238,     0.38338,\n",
+              "           0.38438,     0.38539,     0.38639,     0.38739,     0.38839,     0.38939,     0.39039,     0.39139,     0.39239,     0.39339,     0.39439,      0.3954,      0.3964,      0.3974,      0.3984,      0.3994,      0.4004,      0.4014,      0.4024,      0.4034,      0.4044,     0.40541,     0.40641,     0.40741,\n",
+              "           0.40841,     0.40941,     0.41041,     0.41141,     0.41241,     0.41341,     0.41441,     0.41542,     0.41642,     0.41742,     0.41842,     0.41942,     0.42042,     0.42142,     0.42242,     0.42342,     0.42442,     0.42543,     0.42643,     0.42743,     0.42843,     0.42943,     0.43043,     0.43143,\n",
+              "           0.43243,     0.43343,     0.43443,     0.43544,     0.43644,     0.43744,     0.43844,     0.43944,     0.44044,     0.44144,     0.44244,     0.44344,     0.44444,     0.44545,     0.44645,     0.44745,     0.44845,     0.44945,     0.45045,     0.45145,     0.45245,     0.45345,     0.45445,     0.45546,\n",
+              "           0.45646,     0.45746,     0.45846,     0.45946,     0.46046,     0.46146,     0.46246,     0.46346,     0.46446,     0.46547,     0.46647,     0.46747,     0.46847,     0.46947,     0.47047,     0.47147,     0.47247,     0.47347,     0.47447,     0.47548,     0.47648,     0.47748,     0.47848,     0.47948,\n",
+              "           0.48048,     0.48148,     0.48248,     0.48348,     0.48448,     0.48549,     0.48649,     0.48749,     0.48849,     0.48949,     0.49049,     0.49149,     0.49249,     0.49349,     0.49449,      0.4955,      0.4965,      0.4975,      0.4985,      0.4995,      0.5005,      0.5015,      0.5025,      0.5035,\n",
+              "            0.5045,     0.50551,     0.50651,     0.50751,     0.50851,     0.50951,     0.51051,     0.51151,     0.51251,     0.51351,     0.51451,     0.51552,     0.51652,     0.51752,     0.51852,     0.51952,     0.52052,     0.52152,     0.52252,     0.52352,     0.52452,     0.52553,     0.52653,     0.52753,\n",
+              "           0.52853,     0.52953,     0.53053,     0.53153,     0.53253,     0.53353,     0.53453,     0.53554,     0.53654,     0.53754,     0.53854,     0.53954,     0.54054,     0.54154,     0.54254,     0.54354,     0.54454,     0.54555,     0.54655,     0.54755,     0.54855,     0.54955,     0.55055,     0.55155,\n",
+              "           0.55255,     0.55355,     0.55455,     0.55556,     0.55656,     0.55756,     0.55856,     0.55956,     0.56056,     0.56156,     0.56256,     0.56356,     0.56456,     0.56557,     0.56657,     0.56757,     0.56857,     0.56957,     0.57057,     0.57157,     0.57257,     0.57357,     0.57457,     0.57558,\n",
+              "           0.57658,     0.57758,     0.57858,     0.57958,     0.58058,     0.58158,     0.58258,     0.58358,     0.58458,     0.58559,     0.58659,     0.58759,     0.58859,     0.58959,     0.59059,     0.59159,     0.59259,     0.59359,     0.59459,      0.5956,      0.5966,      0.5976,      0.5986,      0.5996,\n",
+              "            0.6006,      0.6016,      0.6026,      0.6036,      0.6046,     0.60561,     0.60661,     0.60761,     0.60861,     0.60961,     0.61061,     0.61161,     0.61261,     0.61361,     0.61461,     0.61562,     0.61662,     0.61762,     0.61862,     0.61962,     0.62062,     0.62162,     0.62262,     0.62362,\n",
+              "           0.62462,     0.62563,     0.62663,     0.62763,     0.62863,     0.62963,     0.63063,     0.63163,     0.63263,     0.63363,     0.63463,     0.63564,     0.63664,     0.63764,     0.63864,     0.63964,     0.64064,     0.64164,     0.64264,     0.64364,     0.64464,     0.64565,     0.64665,     0.64765,\n",
+              "           0.64865,     0.64965,     0.65065,     0.65165,     0.65265,     0.65365,     0.65465,     0.65566,     0.65666,     0.65766,     0.65866,     0.65966,     0.66066,     0.66166,     0.66266,     0.66366,     0.66466,     0.66567,     0.66667,     0.66767,     0.66867,     0.66967,     0.67067,     0.67167,\n",
+              "           0.67267,     0.67367,     0.67467,     0.67568,     0.67668,     0.67768,     0.67868,     0.67968,     0.68068,     0.68168,     0.68268,     0.68368,     0.68468,     0.68569,     0.68669,     0.68769,     0.68869,     0.68969,     0.69069,     0.69169,     0.69269,     0.69369,     0.69469,      0.6957,\n",
+              "            0.6967,      0.6977,      0.6987,      0.6997,      0.7007,      0.7017,      0.7027,      0.7037,      0.7047,     0.70571,     0.70671,     0.70771,     0.70871,     0.70971,     0.71071,     0.71171,     0.71271,     0.71371,     0.71471,     0.71572,     0.71672,     0.71772,     0.71872,     0.71972,\n",
+              "           0.72072,     0.72172,     0.72272,     0.72372,     0.72472,     0.72573,     0.72673,     0.72773,     0.72873,     0.72973,     0.73073,     0.73173,     0.73273,     0.73373,     0.73473,     0.73574,     0.73674,     0.73774,     0.73874,     0.73974,     0.74074,     0.74174,     0.74274,     0.74374,\n",
+              "           0.74474,     0.74575,     0.74675,     0.74775,     0.74875,     0.74975,     0.75075,     0.75175,     0.75275,     0.75375,     0.75475,     0.75576,     0.75676,     0.75776,     0.75876,     0.75976,     0.76076,     0.76176,     0.76276,     0.76376,     0.76476,     0.76577,     0.76677,     0.76777,\n",
+              "           0.76877,     0.76977,     0.77077,     0.77177,     0.77277,     0.77377,     0.77477,     0.77578,     0.77678,     0.77778,     0.77878,     0.77978,     0.78078,     0.78178,     0.78278,     0.78378,     0.78478,     0.78579,     0.78679,     0.78779,     0.78879,     0.78979,     0.79079,     0.79179,\n",
+              "           0.79279,     0.79379,     0.79479,      0.7958,      0.7968,      0.7978,      0.7988,      0.7998,      0.8008,      0.8018,      0.8028,      0.8038,      0.8048,     0.80581,     0.80681,     0.80781,     0.80881,     0.80981,     0.81081,     0.81181,     0.81281,     0.81381,     0.81481,     0.81582,\n",
+              "           0.81682,     0.81782,     0.81882,     0.81982,     0.82082,     0.82182,     0.82282,     0.82382,     0.82482,     0.82583,     0.82683,     0.82783,     0.82883,     0.82983,     0.83083,     0.83183,     0.83283,     0.83383,     0.83483,     0.83584,     0.83684,     0.83784,     0.83884,     0.83984,\n",
+              "           0.84084,     0.84184,     0.84284,     0.84384,     0.84484,     0.84585,     0.84685,     0.84785,     0.84885,     0.84985,     0.85085,     0.85185,     0.85285,     0.85385,     0.85485,     0.85586,     0.85686,     0.85786,     0.85886,     0.85986,     0.86086,     0.86186,     0.86286,     0.86386,\n",
+              "           0.86486,     0.86587,     0.86687,     0.86787,     0.86887,     0.86987,     0.87087,     0.87187,     0.87287,     0.87387,     0.87487,     0.87588,     0.87688,     0.87788,     0.87888,     0.87988,     0.88088,     0.88188,     0.88288,     0.88388,     0.88488,     0.88589,     0.88689,     0.88789,\n",
+              "           0.88889,     0.88989,     0.89089,     0.89189,     0.89289,     0.89389,     0.89489,      0.8959,      0.8969,      0.8979,      0.8989,      0.8999,      0.9009,      0.9019,      0.9029,      0.9039,      0.9049,     0.90591,     0.90691,     0.90791,     0.90891,     0.90991,     0.91091,     0.91191,\n",
+              "           0.91291,     0.91391,     0.91491,     0.91592,     0.91692,     0.91792,     0.91892,     0.91992,     0.92092,     0.92192,     0.92292,     0.92392,     0.92492,     0.92593,     0.92693,     0.92793,     0.92893,     0.92993,     0.93093,     0.93193,     0.93293,     0.93393,     0.93493,     0.93594,\n",
+              "           0.93694,     0.93794,     0.93894,     0.93994,     0.94094,     0.94194,     0.94294,     0.94394,     0.94494,     0.94595,     0.94695,     0.94795,     0.94895,     0.94995,     0.95095,     0.95195,     0.95295,     0.95395,     0.95495,     0.95596,     0.95696,     0.95796,     0.95896,     0.95996,\n",
+              "           0.96096,     0.96196,     0.96296,     0.96396,     0.96496,     0.96597,     0.96697,     0.96797,     0.96897,     0.96997,     0.97097,     0.97197,     0.97297,     0.97397,     0.97497,     0.97598,     0.97698,     0.97798,     0.97898,     0.97998,     0.98098,     0.98198,     0.98298,     0.98398,\n",
+              "           0.98498,     0.98599,     0.98699,     0.98799,     0.98899,     0.98999,     0.99099,     0.99199,     0.99299,     0.99399,     0.99499,       0.996,       0.997,       0.998,       0.999,           1]), array([[        0.1,         0.1,         0.1, ...,  7.5596e-05,  3.7798e-05,           0],\n",
+              "       [          1,           1,           1, ...,  9.5217e-05,  4.7608e-05,           0],\n",
+              "       [          1,           1,           1, ...,  0.00012362,  6.1809e-05,           0],\n",
+              "       [          1,           1,           1, ...,   0.0003249,  0.00016245,           0],\n",
+              "       [    0.42857,     0.42857,     0.42857, ...,  0.00018122,  9.0612e-05,           0],\n",
+              "       [    0.16667,     0.16667,     0.16667, ...,  2.4863e-05,  1.2431e-05,           0]]), 'Recall', 'Precision'], [array([          0,    0.001001,    0.002002,    0.003003,    0.004004,    0.005005,    0.006006,    0.007007,    0.008008,    0.009009,     0.01001,    0.011011,    0.012012,    0.013013,    0.014014,    0.015015,    0.016016,    0.017017,    0.018018,    0.019019,     0.02002,    0.021021,    0.022022,    0.023023,\n",
+              "          0.024024,    0.025025,    0.026026,    0.027027,    0.028028,    0.029029,     0.03003,    0.031031,    0.032032,    0.033033,    0.034034,    0.035035,    0.036036,    0.037037,    0.038038,    0.039039,     0.04004,    0.041041,    0.042042,    0.043043,    0.044044,    0.045045,    0.046046,    0.047047,\n",
+              "          0.048048,    0.049049,     0.05005,    0.051051,    0.052052,    0.053053,    0.054054,    0.055055,    0.056056,    0.057057,    0.058058,    0.059059,     0.06006,    0.061061,    0.062062,    0.063063,    0.064064,    0.065065,    0.066066,    0.067067,    0.068068,    0.069069,     0.07007,    0.071071,\n",
+              "          0.072072,    0.073073,    0.074074,    0.075075,    0.076076,    0.077077,    0.078078,    0.079079,     0.08008,    0.081081,    0.082082,    0.083083,    0.084084,    0.085085,    0.086086,    0.087087,    0.088088,    0.089089,     0.09009,    0.091091,    0.092092,    0.093093,    0.094094,    0.095095,\n",
+              "          0.096096,    0.097097,    0.098098,    0.099099,      0.1001,      0.1011,      0.1021,      0.1031,      0.1041,     0.10511,     0.10611,     0.10711,     0.10811,     0.10911,     0.11011,     0.11111,     0.11211,     0.11311,     0.11411,     0.11512,     0.11612,     0.11712,     0.11812,     0.11912,\n",
+              "           0.12012,     0.12112,     0.12212,     0.12312,     0.12412,     0.12513,     0.12613,     0.12713,     0.12813,     0.12913,     0.13013,     0.13113,     0.13213,     0.13313,     0.13413,     0.13514,     0.13614,     0.13714,     0.13814,     0.13914,     0.14014,     0.14114,     0.14214,     0.14314,\n",
+              "           0.14414,     0.14515,     0.14615,     0.14715,     0.14815,     0.14915,     0.15015,     0.15115,     0.15215,     0.15315,     0.15415,     0.15516,     0.15616,     0.15716,     0.15816,     0.15916,     0.16016,     0.16116,     0.16216,     0.16316,     0.16416,     0.16517,     0.16617,     0.16717,\n",
+              "           0.16817,     0.16917,     0.17017,     0.17117,     0.17217,     0.17317,     0.17417,     0.17518,     0.17618,     0.17718,     0.17818,     0.17918,     0.18018,     0.18118,     0.18218,     0.18318,     0.18418,     0.18519,     0.18619,     0.18719,     0.18819,     0.18919,     0.19019,     0.19119,\n",
+              "           0.19219,     0.19319,     0.19419,      0.1952,      0.1962,      0.1972,      0.1982,      0.1992,      0.2002,      0.2012,      0.2022,      0.2032,      0.2042,     0.20521,     0.20621,     0.20721,     0.20821,     0.20921,     0.21021,     0.21121,     0.21221,     0.21321,     0.21421,     0.21522,\n",
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+              "           0.24024,     0.24124,     0.24224,     0.24324,     0.24424,     0.24525,     0.24625,     0.24725,     0.24825,     0.24925,     0.25025,     0.25125,     0.25225,     0.25325,     0.25425,     0.25526,     0.25626,     0.25726,     0.25826,     0.25926,     0.26026,     0.26126,     0.26226,     0.26326,\n",
+              "           0.26426,     0.26527,     0.26627,     0.26727,     0.26827,     0.26927,     0.27027,     0.27127,     0.27227,     0.27327,     0.27427,     0.27528,     0.27628,     0.27728,     0.27828,     0.27928,     0.28028,     0.28128,     0.28228,     0.28328,     0.28428,     0.28529,     0.28629,     0.28729,\n",
+              "           0.28829,     0.28929,     0.29029,     0.29129,     0.29229,     0.29329,     0.29429,      0.2953,      0.2963,      0.2973,      0.2983,      0.2993,      0.3003,      0.3013,      0.3023,      0.3033,      0.3043,     0.30531,     0.30631,     0.30731,     0.30831,     0.30931,     0.31031,     0.31131,\n",
+              "           0.31231,     0.31331,     0.31431,     0.31532,     0.31632,     0.31732,     0.31832,     0.31932,     0.32032,     0.32132,     0.32232,     0.32332,     0.32432,     0.32533,     0.32633,     0.32733,     0.32833,     0.32933,     0.33033,     0.33133,     0.33233,     0.33333,     0.33433,     0.33534,\n",
+              "           0.33634,     0.33734,     0.33834,     0.33934,     0.34034,     0.34134,     0.34234,     0.34334,     0.34434,     0.34535,     0.34635,     0.34735,     0.34835,     0.34935,     0.35035,     0.35135,     0.35235,     0.35335,     0.35435,     0.35536,     0.35636,     0.35736,     0.35836,     0.35936,\n",
+              "           0.36036,     0.36136,     0.36236,     0.36336,     0.36436,     0.36537,     0.36637,     0.36737,     0.36837,     0.36937,     0.37037,     0.37137,     0.37237,     0.37337,     0.37437,     0.37538,     0.37638,     0.37738,     0.37838,     0.37938,     0.38038,     0.38138,     0.38238,     0.38338,\n",
+              "           0.38438,     0.38539,     0.38639,     0.38739,     0.38839,     0.38939,     0.39039,     0.39139,     0.39239,     0.39339,     0.39439,      0.3954,      0.3964,      0.3974,      0.3984,      0.3994,      0.4004,      0.4014,      0.4024,      0.4034,      0.4044,     0.40541,     0.40641,     0.40741,\n",
+              "           0.40841,     0.40941,     0.41041,     0.41141,     0.41241,     0.41341,     0.41441,     0.41542,     0.41642,     0.41742,     0.41842,     0.41942,     0.42042,     0.42142,     0.42242,     0.42342,     0.42442,     0.42543,     0.42643,     0.42743,     0.42843,     0.42943,     0.43043,     0.43143,\n",
+              "           0.43243,     0.43343,     0.43443,     0.43544,     0.43644,     0.43744,     0.43844,     0.43944,     0.44044,     0.44144,     0.44244,     0.44344,     0.44444,     0.44545,     0.44645,     0.44745,     0.44845,     0.44945,     0.45045,     0.45145,     0.45245,     0.45345,     0.45445,     0.45546,\n",
+              "           0.45646,     0.45746,     0.45846,     0.45946,     0.46046,     0.46146,     0.46246,     0.46346,     0.46446,     0.46547,     0.46647,     0.46747,     0.46847,     0.46947,     0.47047,     0.47147,     0.47247,     0.47347,     0.47447,     0.47548,     0.47648,     0.47748,     0.47848,     0.47948,\n",
+              "           0.48048,     0.48148,     0.48248,     0.48348,     0.48448,     0.48549,     0.48649,     0.48749,     0.48849,     0.48949,     0.49049,     0.49149,     0.49249,     0.49349,     0.49449,      0.4955,      0.4965,      0.4975,      0.4985,      0.4995,      0.5005,      0.5015,      0.5025,      0.5035,\n",
+              "            0.5045,     0.50551,     0.50651,     0.50751,     0.50851,     0.50951,     0.51051,     0.51151,     0.51251,     0.51351,     0.51451,     0.51552,     0.51652,     0.51752,     0.51852,     0.51952,     0.52052,     0.52152,     0.52252,     0.52352,     0.52452,     0.52553,     0.52653,     0.52753,\n",
+              "           0.52853,     0.52953,     0.53053,     0.53153,     0.53253,     0.53353,     0.53453,     0.53554,     0.53654,     0.53754,     0.53854,     0.53954,     0.54054,     0.54154,     0.54254,     0.54354,     0.54454,     0.54555,     0.54655,     0.54755,     0.54855,     0.54955,     0.55055,     0.55155,\n",
+              "           0.55255,     0.55355,     0.55455,     0.55556,     0.55656,     0.55756,     0.55856,     0.55956,     0.56056,     0.56156,     0.56256,     0.56356,     0.56456,     0.56557,     0.56657,     0.56757,     0.56857,     0.56957,     0.57057,     0.57157,     0.57257,     0.57357,     0.57457,     0.57558,\n",
+              "           0.57658,     0.57758,     0.57858,     0.57958,     0.58058,     0.58158,     0.58258,     0.58358,     0.58458,     0.58559,     0.58659,     0.58759,     0.58859,     0.58959,     0.59059,     0.59159,     0.59259,     0.59359,     0.59459,      0.5956,      0.5966,      0.5976,      0.5986,      0.5996,\n",
+              "            0.6006,      0.6016,      0.6026,      0.6036,      0.6046,     0.60561,     0.60661,     0.60761,     0.60861,     0.60961,     0.61061,     0.61161,     0.61261,     0.61361,     0.61461,     0.61562,     0.61662,     0.61762,     0.61862,     0.61962,     0.62062,     0.62162,     0.62262,     0.62362,\n",
+              "           0.62462,     0.62563,     0.62663,     0.62763,     0.62863,     0.62963,     0.63063,     0.63163,     0.63263,     0.63363,     0.63463,     0.63564,     0.63664,     0.63764,     0.63864,     0.63964,     0.64064,     0.64164,     0.64264,     0.64364,     0.64464,     0.64565,     0.64665,     0.64765,\n",
+              "           0.64865,     0.64965,     0.65065,     0.65165,     0.65265,     0.65365,     0.65465,     0.65566,     0.65666,     0.65766,     0.65866,     0.65966,     0.66066,     0.66166,     0.66266,     0.66366,     0.66466,     0.66567,     0.66667,     0.66767,     0.66867,     0.66967,     0.67067,     0.67167,\n",
+              "           0.67267,     0.67367,     0.67467,     0.67568,     0.67668,     0.67768,     0.67868,     0.67968,     0.68068,     0.68168,     0.68268,     0.68368,     0.68468,     0.68569,     0.68669,     0.68769,     0.68869,     0.68969,     0.69069,     0.69169,     0.69269,     0.69369,     0.69469,      0.6957,\n",
+              "            0.6967,      0.6977,      0.6987,      0.6997,      0.7007,      0.7017,      0.7027,      0.7037,      0.7047,     0.70571,     0.70671,     0.70771,     0.70871,     0.70971,     0.71071,     0.71171,     0.71271,     0.71371,     0.71471,     0.71572,     0.71672,     0.71772,     0.71872,     0.71972,\n",
+              "           0.72072,     0.72172,     0.72272,     0.72372,     0.72472,     0.72573,     0.72673,     0.72773,     0.72873,     0.72973,     0.73073,     0.73173,     0.73273,     0.73373,     0.73473,     0.73574,     0.73674,     0.73774,     0.73874,     0.73974,     0.74074,     0.74174,     0.74274,     0.74374,\n",
+              "           0.74474,     0.74575,     0.74675,     0.74775,     0.74875,     0.74975,     0.75075,     0.75175,     0.75275,     0.75375,     0.75475,     0.75576,     0.75676,     0.75776,     0.75876,     0.75976,     0.76076,     0.76176,     0.76276,     0.76376,     0.76476,     0.76577,     0.76677,     0.76777,\n",
+              "           0.76877,     0.76977,     0.77077,     0.77177,     0.77277,     0.77377,     0.77477,     0.77578,     0.77678,     0.77778,     0.77878,     0.77978,     0.78078,     0.78178,     0.78278,     0.78378,     0.78478,     0.78579,     0.78679,     0.78779,     0.78879,     0.78979,     0.79079,     0.79179,\n",
+              "           0.79279,     0.79379,     0.79479,      0.7958,      0.7968,      0.7978,      0.7988,      0.7998,      0.8008,      0.8018,      0.8028,      0.8038,      0.8048,     0.80581,     0.80681,     0.80781,     0.80881,     0.80981,     0.81081,     0.81181,     0.81281,     0.81381,     0.81481,     0.81582,\n",
+              "           0.81682,     0.81782,     0.81882,     0.81982,     0.82082,     0.82182,     0.82282,     0.82382,     0.82482,     0.82583,     0.82683,     0.82783,     0.82883,     0.82983,     0.83083,     0.83183,     0.83283,     0.83383,     0.83483,     0.83584,     0.83684,     0.83784,     0.83884,     0.83984,\n",
+              "           0.84084,     0.84184,     0.84284,     0.84384,     0.84484,     0.84585,     0.84685,     0.84785,     0.84885,     0.84985,     0.85085,     0.85185,     0.85285,     0.85385,     0.85485,     0.85586,     0.85686,     0.85786,     0.85886,     0.85986,     0.86086,     0.86186,     0.86286,     0.86386,\n",
+              "           0.86486,     0.86587,     0.86687,     0.86787,     0.86887,     0.86987,     0.87087,     0.87187,     0.87287,     0.87387,     0.87487,     0.87588,     0.87688,     0.87788,     0.87888,     0.87988,     0.88088,     0.88188,     0.88288,     0.88388,     0.88488,     0.88589,     0.88689,     0.88789,\n",
+              "           0.88889,     0.88989,     0.89089,     0.89189,     0.89289,     0.89389,     0.89489,      0.8959,      0.8969,      0.8979,      0.8989,      0.8999,      0.9009,      0.9019,      0.9029,      0.9039,      0.9049,     0.90591,     0.90691,     0.90791,     0.90891,     0.90991,     0.91091,     0.91191,\n",
+              "           0.91291,     0.91391,     0.91491,     0.91592,     0.91692,     0.91792,     0.91892,     0.91992,     0.92092,     0.92192,     0.92292,     0.92392,     0.92492,     0.92593,     0.92693,     0.92793,     0.92893,     0.92993,     0.93093,     0.93193,     0.93293,     0.93393,     0.93493,     0.93594,\n",
+              "           0.93694,     0.93794,     0.93894,     0.93994,     0.94094,     0.94194,     0.94294,     0.94394,     0.94494,     0.94595,     0.94695,     0.94795,     0.94895,     0.94995,     0.95095,     0.95195,     0.95295,     0.95395,     0.95495,     0.95596,     0.95696,     0.95796,     0.95896,     0.95996,\n",
+              "           0.96096,     0.96196,     0.96296,     0.96396,     0.96496,     0.96597,     0.96697,     0.96797,     0.96897,     0.96997,     0.97097,     0.97197,     0.97297,     0.97397,     0.97497,     0.97598,     0.97698,     0.97798,     0.97898,     0.97998,     0.98098,     0.98198,     0.98298,     0.98398,\n",
+              "           0.98498,     0.98599,     0.98699,     0.98799,     0.98899,     0.98999,     0.99099,     0.99199,     0.99299,     0.99399,     0.99499,       0.996,       0.997,       0.998,       0.999,           1]), array([[   0.020538,    0.020538,    0.027576, ...,           0,           0,           0],\n",
+              "       [   0.027211,    0.027211,    0.040638, ...,           0,           0,           0],\n",
+              "       [   0.033659,    0.033659,    0.050019, ...,           0,           0,           0],\n",
+              "       [   0.060733,    0.060733,    0.089737, ...,           0,           0,           0],\n",
+              "       [    0.02673,     0.02673,    0.036197, ...,           0,           0,           0],\n",
+              "       [   0.013118,    0.013118,    0.016924, ...,           0,           0,           0]]), 'Confidence', 'F1'], [array([          0,    0.001001,    0.002002,    0.003003,    0.004004,    0.005005,    0.006006,    0.007007,    0.008008,    0.009009,     0.01001,    0.011011,    0.012012,    0.013013,    0.014014,    0.015015,    0.016016,    0.017017,    0.018018,    0.019019,     0.02002,    0.021021,    0.022022,    0.023023,\n",
+              "          0.024024,    0.025025,    0.026026,    0.027027,    0.028028,    0.029029,     0.03003,    0.031031,    0.032032,    0.033033,    0.034034,    0.035035,    0.036036,    0.037037,    0.038038,    0.039039,     0.04004,    0.041041,    0.042042,    0.043043,    0.044044,    0.045045,    0.046046,    0.047047,\n",
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+              "          0.096096,    0.097097,    0.098098,    0.099099,      0.1001,      0.1011,      0.1021,      0.1031,      0.1041,     0.10511,     0.10611,     0.10711,     0.10811,     0.10911,     0.11011,     0.11111,     0.11211,     0.11311,     0.11411,     0.11512,     0.11612,     0.11712,     0.11812,     0.11912,\n",
+              "           0.12012,     0.12112,     0.12212,     0.12312,     0.12412,     0.12513,     0.12613,     0.12713,     0.12813,     0.12913,     0.13013,     0.13113,     0.13213,     0.13313,     0.13413,     0.13514,     0.13614,     0.13714,     0.13814,     0.13914,     0.14014,     0.14114,     0.14214,     0.14314,\n",
+              "           0.14414,     0.14515,     0.14615,     0.14715,     0.14815,     0.14915,     0.15015,     0.15115,     0.15215,     0.15315,     0.15415,     0.15516,     0.15616,     0.15716,     0.15816,     0.15916,     0.16016,     0.16116,     0.16216,     0.16316,     0.16416,     0.16517,     0.16617,     0.16717,\n",
+              "           0.16817,     0.16917,     0.17017,     0.17117,     0.17217,     0.17317,     0.17417,     0.17518,     0.17618,     0.17718,     0.17818,     0.17918,     0.18018,     0.18118,     0.18218,     0.18318,     0.18418,     0.18519,     0.18619,     0.18719,     0.18819,     0.18919,     0.19019,     0.19119,\n",
+              "           0.19219,     0.19319,     0.19419,      0.1952,      0.1962,      0.1972,      0.1982,      0.1992,      0.2002,      0.2012,      0.2022,      0.2032,      0.2042,     0.20521,     0.20621,     0.20721,     0.20821,     0.20921,     0.21021,     0.21121,     0.21221,     0.21321,     0.21421,     0.21522,\n",
+              "           0.21622,     0.21722,     0.21822,     0.21922,     0.22022,     0.22122,     0.22222,     0.22322,     0.22422,     0.22523,     0.22623,     0.22723,     0.22823,     0.22923,     0.23023,     0.23123,     0.23223,     0.23323,     0.23423,     0.23524,     0.23624,     0.23724,     0.23824,     0.23924,\n",
+              "           0.24024,     0.24124,     0.24224,     0.24324,     0.24424,     0.24525,     0.24625,     0.24725,     0.24825,     0.24925,     0.25025,     0.25125,     0.25225,     0.25325,     0.25425,     0.25526,     0.25626,     0.25726,     0.25826,     0.25926,     0.26026,     0.26126,     0.26226,     0.26326,\n",
+              "           0.26426,     0.26527,     0.26627,     0.26727,     0.26827,     0.26927,     0.27027,     0.27127,     0.27227,     0.27327,     0.27427,     0.27528,     0.27628,     0.27728,     0.27828,     0.27928,     0.28028,     0.28128,     0.28228,     0.28328,     0.28428,     0.28529,     0.28629,     0.28729,\n",
+              "           0.28829,     0.28929,     0.29029,     0.29129,     0.29229,     0.29329,     0.29429,      0.2953,      0.2963,      0.2973,      0.2983,      0.2993,      0.3003,      0.3013,      0.3023,      0.3033,      0.3043,     0.30531,     0.30631,     0.30731,     0.30831,     0.30931,     0.31031,     0.31131,\n",
+              "           0.31231,     0.31331,     0.31431,     0.31532,     0.31632,     0.31732,     0.31832,     0.31932,     0.32032,     0.32132,     0.32232,     0.32332,     0.32432,     0.32533,     0.32633,     0.32733,     0.32833,     0.32933,     0.33033,     0.33133,     0.33233,     0.33333,     0.33433,     0.33534,\n",
+              "           0.33634,     0.33734,     0.33834,     0.33934,     0.34034,     0.34134,     0.34234,     0.34334,     0.34434,     0.34535,     0.34635,     0.34735,     0.34835,     0.34935,     0.35035,     0.35135,     0.35235,     0.35335,     0.35435,     0.35536,     0.35636,     0.35736,     0.35836,     0.35936,\n",
+              "           0.36036,     0.36136,     0.36236,     0.36336,     0.36436,     0.36537,     0.36637,     0.36737,     0.36837,     0.36937,     0.37037,     0.37137,     0.37237,     0.37337,     0.37437,     0.37538,     0.37638,     0.37738,     0.37838,     0.37938,     0.38038,     0.38138,     0.38238,     0.38338,\n",
+              "           0.38438,     0.38539,     0.38639,     0.38739,     0.38839,     0.38939,     0.39039,     0.39139,     0.39239,     0.39339,     0.39439,      0.3954,      0.3964,      0.3974,      0.3984,      0.3994,      0.4004,      0.4014,      0.4024,      0.4034,      0.4044,     0.40541,     0.40641,     0.40741,\n",
+              "           0.40841,     0.40941,     0.41041,     0.41141,     0.41241,     0.41341,     0.41441,     0.41542,     0.41642,     0.41742,     0.41842,     0.41942,     0.42042,     0.42142,     0.42242,     0.42342,     0.42442,     0.42543,     0.42643,     0.42743,     0.42843,     0.42943,     0.43043,     0.43143,\n",
+              "           0.43243,     0.43343,     0.43443,     0.43544,     0.43644,     0.43744,     0.43844,     0.43944,     0.44044,     0.44144,     0.44244,     0.44344,     0.44444,     0.44545,     0.44645,     0.44745,     0.44845,     0.44945,     0.45045,     0.45145,     0.45245,     0.45345,     0.45445,     0.45546,\n",
+              "           0.45646,     0.45746,     0.45846,     0.45946,     0.46046,     0.46146,     0.46246,     0.46346,     0.46446,     0.46547,     0.46647,     0.46747,     0.46847,     0.46947,     0.47047,     0.47147,     0.47247,     0.47347,     0.47447,     0.47548,     0.47648,     0.47748,     0.47848,     0.47948,\n",
+              "           0.48048,     0.48148,     0.48248,     0.48348,     0.48448,     0.48549,     0.48649,     0.48749,     0.48849,     0.48949,     0.49049,     0.49149,     0.49249,     0.49349,     0.49449,      0.4955,      0.4965,      0.4975,      0.4985,      0.4995,      0.5005,      0.5015,      0.5025,      0.5035,\n",
+              "            0.5045,     0.50551,     0.50651,     0.50751,     0.50851,     0.50951,     0.51051,     0.51151,     0.51251,     0.51351,     0.51451,     0.51552,     0.51652,     0.51752,     0.51852,     0.51952,     0.52052,     0.52152,     0.52252,     0.52352,     0.52452,     0.52553,     0.52653,     0.52753,\n",
+              "           0.52853,     0.52953,     0.53053,     0.53153,     0.53253,     0.53353,     0.53453,     0.53554,     0.53654,     0.53754,     0.53854,     0.53954,     0.54054,     0.54154,     0.54254,     0.54354,     0.54454,     0.54555,     0.54655,     0.54755,     0.54855,     0.54955,     0.55055,     0.55155,\n",
+              "           0.55255,     0.55355,     0.55455,     0.55556,     0.55656,     0.55756,     0.55856,     0.55956,     0.56056,     0.56156,     0.56256,     0.56356,     0.56456,     0.56557,     0.56657,     0.56757,     0.56857,     0.56957,     0.57057,     0.57157,     0.57257,     0.57357,     0.57457,     0.57558,\n",
+              "           0.57658,     0.57758,     0.57858,     0.57958,     0.58058,     0.58158,     0.58258,     0.58358,     0.58458,     0.58559,     0.58659,     0.58759,     0.58859,     0.58959,     0.59059,     0.59159,     0.59259,     0.59359,     0.59459,      0.5956,      0.5966,      0.5976,      0.5986,      0.5996,\n",
+              "            0.6006,      0.6016,      0.6026,      0.6036,      0.6046,     0.60561,     0.60661,     0.60761,     0.60861,     0.60961,     0.61061,     0.61161,     0.61261,     0.61361,     0.61461,     0.61562,     0.61662,     0.61762,     0.61862,     0.61962,     0.62062,     0.62162,     0.62262,     0.62362,\n",
+              "           0.62462,     0.62563,     0.62663,     0.62763,     0.62863,     0.62963,     0.63063,     0.63163,     0.63263,     0.63363,     0.63463,     0.63564,     0.63664,     0.63764,     0.63864,     0.63964,     0.64064,     0.64164,     0.64264,     0.64364,     0.64464,     0.64565,     0.64665,     0.64765,\n",
+              "           0.64865,     0.64965,     0.65065,     0.65165,     0.65265,     0.65365,     0.65465,     0.65566,     0.65666,     0.65766,     0.65866,     0.65966,     0.66066,     0.66166,     0.66266,     0.66366,     0.66466,     0.66567,     0.66667,     0.66767,     0.66867,     0.66967,     0.67067,     0.67167,\n",
+              "           0.67267,     0.67367,     0.67467,     0.67568,     0.67668,     0.67768,     0.67868,     0.67968,     0.68068,     0.68168,     0.68268,     0.68368,     0.68468,     0.68569,     0.68669,     0.68769,     0.68869,     0.68969,     0.69069,     0.69169,     0.69269,     0.69369,     0.69469,      0.6957,\n",
+              "            0.6967,      0.6977,      0.6987,      0.6997,      0.7007,      0.7017,      0.7027,      0.7037,      0.7047,     0.70571,     0.70671,     0.70771,     0.70871,     0.70971,     0.71071,     0.71171,     0.71271,     0.71371,     0.71471,     0.71572,     0.71672,     0.71772,     0.71872,     0.71972,\n",
+              "           0.72072,     0.72172,     0.72272,     0.72372,     0.72472,     0.72573,     0.72673,     0.72773,     0.72873,     0.72973,     0.73073,     0.73173,     0.73273,     0.73373,     0.73473,     0.73574,     0.73674,     0.73774,     0.73874,     0.73974,     0.74074,     0.74174,     0.74274,     0.74374,\n",
+              "           0.74474,     0.74575,     0.74675,     0.74775,     0.74875,     0.74975,     0.75075,     0.75175,     0.75275,     0.75375,     0.75475,     0.75576,     0.75676,     0.75776,     0.75876,     0.75976,     0.76076,     0.76176,     0.76276,     0.76376,     0.76476,     0.76577,     0.76677,     0.76777,\n",
+              "           0.76877,     0.76977,     0.77077,     0.77177,     0.77277,     0.77377,     0.77477,     0.77578,     0.77678,     0.77778,     0.77878,     0.77978,     0.78078,     0.78178,     0.78278,     0.78378,     0.78478,     0.78579,     0.78679,     0.78779,     0.78879,     0.78979,     0.79079,     0.79179,\n",
+              "           0.79279,     0.79379,     0.79479,      0.7958,      0.7968,      0.7978,      0.7988,      0.7998,      0.8008,      0.8018,      0.8028,      0.8038,      0.8048,     0.80581,     0.80681,     0.80781,     0.80881,     0.80981,     0.81081,     0.81181,     0.81281,     0.81381,     0.81481,     0.81582,\n",
+              "           0.81682,     0.81782,     0.81882,     0.81982,     0.82082,     0.82182,     0.82282,     0.82382,     0.82482,     0.82583,     0.82683,     0.82783,     0.82883,     0.82983,     0.83083,     0.83183,     0.83283,     0.83383,     0.83483,     0.83584,     0.83684,     0.83784,     0.83884,     0.83984,\n",
+              "           0.84084,     0.84184,     0.84284,     0.84384,     0.84484,     0.84585,     0.84685,     0.84785,     0.84885,     0.84985,     0.85085,     0.85185,     0.85285,     0.85385,     0.85485,     0.85586,     0.85686,     0.85786,     0.85886,     0.85986,     0.86086,     0.86186,     0.86286,     0.86386,\n",
+              "           0.86486,     0.86587,     0.86687,     0.86787,     0.86887,     0.86987,     0.87087,     0.87187,     0.87287,     0.87387,     0.87487,     0.87588,     0.87688,     0.87788,     0.87888,     0.87988,     0.88088,     0.88188,     0.88288,     0.88388,     0.88488,     0.88589,     0.88689,     0.88789,\n",
+              "           0.88889,     0.88989,     0.89089,     0.89189,     0.89289,     0.89389,     0.89489,      0.8959,      0.8969,      0.8979,      0.8989,      0.8999,      0.9009,      0.9019,      0.9029,      0.9039,      0.9049,     0.90591,     0.90691,     0.90791,     0.90891,     0.90991,     0.91091,     0.91191,\n",
+              "           0.91291,     0.91391,     0.91491,     0.91592,     0.91692,     0.91792,     0.91892,     0.91992,     0.92092,     0.92192,     0.92292,     0.92392,     0.92492,     0.92593,     0.92693,     0.92793,     0.92893,     0.92993,     0.93093,     0.93193,     0.93293,     0.93393,     0.93493,     0.93594,\n",
+              "           0.93694,     0.93794,     0.93894,     0.93994,     0.94094,     0.94194,     0.94294,     0.94394,     0.94494,     0.94595,     0.94695,     0.94795,     0.94895,     0.94995,     0.95095,     0.95195,     0.95295,     0.95395,     0.95495,     0.95596,     0.95696,     0.95796,     0.95896,     0.95996,\n",
+              "           0.96096,     0.96196,     0.96296,     0.96396,     0.96496,     0.96597,     0.96697,     0.96797,     0.96897,     0.96997,     0.97097,     0.97197,     0.97297,     0.97397,     0.97497,     0.97598,     0.97698,     0.97798,     0.97898,     0.97998,     0.98098,     0.98198,     0.98298,     0.98398,\n",
+              "           0.98498,     0.98599,     0.98699,     0.98799,     0.98899,     0.98999,     0.99099,     0.99199,     0.99299,     0.99399,     0.99499,       0.996,       0.997,       0.998,       0.999,           1]), array([[   0.010417,    0.010417,    0.014056, ...,           1,           1,           1],\n",
+              "       [   0.013872,    0.013872,    0.020938, ...,           1,           1,           1],\n",
+              "       [   0.017232,    0.017232,    0.025939, ...,           1,           1,           1],\n",
+              "       [   0.031556,    0.031556,    0.047615, ...,           1,           1,           1],\n",
+              "       [   0.013578,    0.013578,    0.018518, ...,           1,           1,           1],\n",
+              "       [   0.006653,    0.006653,   0.0086325, ...,           1,           1,           1]]), 'Confidence', 'Precision'], [array([          0,    0.001001,    0.002002,    0.003003,    0.004004,    0.005005,    0.006006,    0.007007,    0.008008,    0.009009,     0.01001,    0.011011,    0.012012,    0.013013,    0.014014,    0.015015,    0.016016,    0.017017,    0.018018,    0.019019,     0.02002,    0.021021,    0.022022,    0.023023,\n",
+              "          0.024024,    0.025025,    0.026026,    0.027027,    0.028028,    0.029029,     0.03003,    0.031031,    0.032032,    0.033033,    0.034034,    0.035035,    0.036036,    0.037037,    0.038038,    0.039039,     0.04004,    0.041041,    0.042042,    0.043043,    0.044044,    0.045045,    0.046046,    0.047047,\n",
+              "          0.048048,    0.049049,     0.05005,    0.051051,    0.052052,    0.053053,    0.054054,    0.055055,    0.056056,    0.057057,    0.058058,    0.059059,     0.06006,    0.061061,    0.062062,    0.063063,    0.064064,    0.065065,    0.066066,    0.067067,    0.068068,    0.069069,     0.07007,    0.071071,\n",
+              "          0.072072,    0.073073,    0.074074,    0.075075,    0.076076,    0.077077,    0.078078,    0.079079,     0.08008,    0.081081,    0.082082,    0.083083,    0.084084,    0.085085,    0.086086,    0.087087,    0.088088,    0.089089,     0.09009,    0.091091,    0.092092,    0.093093,    0.094094,    0.095095,\n",
+              "          0.096096,    0.097097,    0.098098,    0.099099,      0.1001,      0.1011,      0.1021,      0.1031,      0.1041,     0.10511,     0.10611,     0.10711,     0.10811,     0.10911,     0.11011,     0.11111,     0.11211,     0.11311,     0.11411,     0.11512,     0.11612,     0.11712,     0.11812,     0.11912,\n",
+              "           0.12012,     0.12112,     0.12212,     0.12312,     0.12412,     0.12513,     0.12613,     0.12713,     0.12813,     0.12913,     0.13013,     0.13113,     0.13213,     0.13313,     0.13413,     0.13514,     0.13614,     0.13714,     0.13814,     0.13914,     0.14014,     0.14114,     0.14214,     0.14314,\n",
+              "           0.14414,     0.14515,     0.14615,     0.14715,     0.14815,     0.14915,     0.15015,     0.15115,     0.15215,     0.15315,     0.15415,     0.15516,     0.15616,     0.15716,     0.15816,     0.15916,     0.16016,     0.16116,     0.16216,     0.16316,     0.16416,     0.16517,     0.16617,     0.16717,\n",
+              "           0.16817,     0.16917,     0.17017,     0.17117,     0.17217,     0.17317,     0.17417,     0.17518,     0.17618,     0.17718,     0.17818,     0.17918,     0.18018,     0.18118,     0.18218,     0.18318,     0.18418,     0.18519,     0.18619,     0.18719,     0.18819,     0.18919,     0.19019,     0.19119,\n",
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+              "           0.24024,     0.24124,     0.24224,     0.24324,     0.24424,     0.24525,     0.24625,     0.24725,     0.24825,     0.24925,     0.25025,     0.25125,     0.25225,     0.25325,     0.25425,     0.25526,     0.25626,     0.25726,     0.25826,     0.25926,     0.26026,     0.26126,     0.26226,     0.26326,\n",
+              "           0.26426,     0.26527,     0.26627,     0.26727,     0.26827,     0.26927,     0.27027,     0.27127,     0.27227,     0.27327,     0.27427,     0.27528,     0.27628,     0.27728,     0.27828,     0.27928,     0.28028,     0.28128,     0.28228,     0.28328,     0.28428,     0.28529,     0.28629,     0.28729,\n",
+              "           0.28829,     0.28929,     0.29029,     0.29129,     0.29229,     0.29329,     0.29429,      0.2953,      0.2963,      0.2973,      0.2983,      0.2993,      0.3003,      0.3013,      0.3023,      0.3033,      0.3043,     0.30531,     0.30631,     0.30731,     0.30831,     0.30931,     0.31031,     0.31131,\n",
+              "           0.31231,     0.31331,     0.31431,     0.31532,     0.31632,     0.31732,     0.31832,     0.31932,     0.32032,     0.32132,     0.32232,     0.32332,     0.32432,     0.32533,     0.32633,     0.32733,     0.32833,     0.32933,     0.33033,     0.33133,     0.33233,     0.33333,     0.33433,     0.33534,\n",
+              "           0.33634,     0.33734,     0.33834,     0.33934,     0.34034,     0.34134,     0.34234,     0.34334,     0.34434,     0.34535,     0.34635,     0.34735,     0.34835,     0.34935,     0.35035,     0.35135,     0.35235,     0.35335,     0.35435,     0.35536,     0.35636,     0.35736,     0.35836,     0.35936,\n",
+              "           0.36036,     0.36136,     0.36236,     0.36336,     0.36436,     0.36537,     0.36637,     0.36737,     0.36837,     0.36937,     0.37037,     0.37137,     0.37237,     0.37337,     0.37437,     0.37538,     0.37638,     0.37738,     0.37838,     0.37938,     0.38038,     0.38138,     0.38238,     0.38338,\n",
+              "           0.38438,     0.38539,     0.38639,     0.38739,     0.38839,     0.38939,     0.39039,     0.39139,     0.39239,     0.39339,     0.39439,      0.3954,      0.3964,      0.3974,      0.3984,      0.3994,      0.4004,      0.4014,      0.4024,      0.4034,      0.4044,     0.40541,     0.40641,     0.40741,\n",
+              "           0.40841,     0.40941,     0.41041,     0.41141,     0.41241,     0.41341,     0.41441,     0.41542,     0.41642,     0.41742,     0.41842,     0.41942,     0.42042,     0.42142,     0.42242,     0.42342,     0.42442,     0.42543,     0.42643,     0.42743,     0.42843,     0.42943,     0.43043,     0.43143,\n",
+              "           0.43243,     0.43343,     0.43443,     0.43544,     0.43644,     0.43744,     0.43844,     0.43944,     0.44044,     0.44144,     0.44244,     0.44344,     0.44444,     0.44545,     0.44645,     0.44745,     0.44845,     0.44945,     0.45045,     0.45145,     0.45245,     0.45345,     0.45445,     0.45546,\n",
+              "           0.45646,     0.45746,     0.45846,     0.45946,     0.46046,     0.46146,     0.46246,     0.46346,     0.46446,     0.46547,     0.46647,     0.46747,     0.46847,     0.46947,     0.47047,     0.47147,     0.47247,     0.47347,     0.47447,     0.47548,     0.47648,     0.47748,     0.47848,     0.47948,\n",
+              "           0.48048,     0.48148,     0.48248,     0.48348,     0.48448,     0.48549,     0.48649,     0.48749,     0.48849,     0.48949,     0.49049,     0.49149,     0.49249,     0.49349,     0.49449,      0.4955,      0.4965,      0.4975,      0.4985,      0.4995,      0.5005,      0.5015,      0.5025,      0.5035,\n",
+              "            0.5045,     0.50551,     0.50651,     0.50751,     0.50851,     0.50951,     0.51051,     0.51151,     0.51251,     0.51351,     0.51451,     0.51552,     0.51652,     0.51752,     0.51852,     0.51952,     0.52052,     0.52152,     0.52252,     0.52352,     0.52452,     0.52553,     0.52653,     0.52753,\n",
+              "           0.52853,     0.52953,     0.53053,     0.53153,     0.53253,     0.53353,     0.53453,     0.53554,     0.53654,     0.53754,     0.53854,     0.53954,     0.54054,     0.54154,     0.54254,     0.54354,     0.54454,     0.54555,     0.54655,     0.54755,     0.54855,     0.54955,     0.55055,     0.55155,\n",
+              "           0.55255,     0.55355,     0.55455,     0.55556,     0.55656,     0.55756,     0.55856,     0.55956,     0.56056,     0.56156,     0.56256,     0.56356,     0.56456,     0.56557,     0.56657,     0.56757,     0.56857,     0.56957,     0.57057,     0.57157,     0.57257,     0.57357,     0.57457,     0.57558,\n",
+              "           0.57658,     0.57758,     0.57858,     0.57958,     0.58058,     0.58158,     0.58258,     0.58358,     0.58458,     0.58559,     0.58659,     0.58759,     0.58859,     0.58959,     0.59059,     0.59159,     0.59259,     0.59359,     0.59459,      0.5956,      0.5966,      0.5976,      0.5986,      0.5996,\n",
+              "            0.6006,      0.6016,      0.6026,      0.6036,      0.6046,     0.60561,     0.60661,     0.60761,     0.60861,     0.60961,     0.61061,     0.61161,     0.61261,     0.61361,     0.61461,     0.61562,     0.61662,     0.61762,     0.61862,     0.61962,     0.62062,     0.62162,     0.62262,     0.62362,\n",
+              "           0.62462,     0.62563,     0.62663,     0.62763,     0.62863,     0.62963,     0.63063,     0.63163,     0.63263,     0.63363,     0.63463,     0.63564,     0.63664,     0.63764,     0.63864,     0.63964,     0.64064,     0.64164,     0.64264,     0.64364,     0.64464,     0.64565,     0.64665,     0.64765,\n",
+              "           0.64865,     0.64965,     0.65065,     0.65165,     0.65265,     0.65365,     0.65465,     0.65566,     0.65666,     0.65766,     0.65866,     0.65966,     0.66066,     0.66166,     0.66266,     0.66366,     0.66466,     0.66567,     0.66667,     0.66767,     0.66867,     0.66967,     0.67067,     0.67167,\n",
+              "           0.67267,     0.67367,     0.67467,     0.67568,     0.67668,     0.67768,     0.67868,     0.67968,     0.68068,     0.68168,     0.68268,     0.68368,     0.68468,     0.68569,     0.68669,     0.68769,     0.68869,     0.68969,     0.69069,     0.69169,     0.69269,     0.69369,     0.69469,      0.6957,\n",
+              "            0.6967,      0.6977,      0.6987,      0.6997,      0.7007,      0.7017,      0.7027,      0.7037,      0.7047,     0.70571,     0.70671,     0.70771,     0.70871,     0.70971,     0.71071,     0.71171,     0.71271,     0.71371,     0.71471,     0.71572,     0.71672,     0.71772,     0.71872,     0.71972,\n",
+              "           0.72072,     0.72172,     0.72272,     0.72372,     0.72472,     0.72573,     0.72673,     0.72773,     0.72873,     0.72973,     0.73073,     0.73173,     0.73273,     0.73373,     0.73473,     0.73574,     0.73674,     0.73774,     0.73874,     0.73974,     0.74074,     0.74174,     0.74274,     0.74374,\n",
+              "           0.74474,     0.74575,     0.74675,     0.74775,     0.74875,     0.74975,     0.75075,     0.75175,     0.75275,     0.75375,     0.75475,     0.75576,     0.75676,     0.75776,     0.75876,     0.75976,     0.76076,     0.76176,     0.76276,     0.76376,     0.76476,     0.76577,     0.76677,     0.76777,\n",
+              "           0.76877,     0.76977,     0.77077,     0.77177,     0.77277,     0.77377,     0.77477,     0.77578,     0.77678,     0.77778,     0.77878,     0.77978,     0.78078,     0.78178,     0.78278,     0.78378,     0.78478,     0.78579,     0.78679,     0.78779,     0.78879,     0.78979,     0.79079,     0.79179,\n",
+              "           0.79279,     0.79379,     0.79479,      0.7958,      0.7968,      0.7978,      0.7988,      0.7998,      0.8008,      0.8018,      0.8028,      0.8038,      0.8048,     0.80581,     0.80681,     0.80781,     0.80881,     0.80981,     0.81081,     0.81181,     0.81281,     0.81381,     0.81481,     0.81582,\n",
+              "           0.81682,     0.81782,     0.81882,     0.81982,     0.82082,     0.82182,     0.82282,     0.82382,     0.82482,     0.82583,     0.82683,     0.82783,     0.82883,     0.82983,     0.83083,     0.83183,     0.83283,     0.83383,     0.83483,     0.83584,     0.83684,     0.83784,     0.83884,     0.83984,\n",
+              "           0.84084,     0.84184,     0.84284,     0.84384,     0.84484,     0.84585,     0.84685,     0.84785,     0.84885,     0.84985,     0.85085,     0.85185,     0.85285,     0.85385,     0.85485,     0.85586,     0.85686,     0.85786,     0.85886,     0.85986,     0.86086,     0.86186,     0.86286,     0.86386,\n",
+              "           0.86486,     0.86587,     0.86687,     0.86787,     0.86887,     0.86987,     0.87087,     0.87187,     0.87287,     0.87387,     0.87487,     0.87588,     0.87688,     0.87788,     0.87888,     0.87988,     0.88088,     0.88188,     0.88288,     0.88388,     0.88488,     0.88589,     0.88689,     0.88789,\n",
+              "           0.88889,     0.88989,     0.89089,     0.89189,     0.89289,     0.89389,     0.89489,      0.8959,      0.8969,      0.8979,      0.8989,      0.8999,      0.9009,      0.9019,      0.9029,      0.9039,      0.9049,     0.90591,     0.90691,     0.90791,     0.90891,     0.90991,     0.91091,     0.91191,\n",
+              "           0.91291,     0.91391,     0.91491,     0.91592,     0.91692,     0.91792,     0.91892,     0.91992,     0.92092,     0.92192,     0.92292,     0.92392,     0.92492,     0.92593,     0.92693,     0.92793,     0.92893,     0.92993,     0.93093,     0.93193,     0.93293,     0.93393,     0.93493,     0.93594,\n",
+              "           0.93694,     0.93794,     0.93894,     0.93994,     0.94094,     0.94194,     0.94294,     0.94394,     0.94494,     0.94595,     0.94695,     0.94795,     0.94895,     0.94995,     0.95095,     0.95195,     0.95295,     0.95395,     0.95495,     0.95596,     0.95696,     0.95796,     0.95896,     0.95996,\n",
+              "           0.96096,     0.96196,     0.96296,     0.96396,     0.96496,     0.96597,     0.96697,     0.96797,     0.96897,     0.96997,     0.97097,     0.97197,     0.97297,     0.97397,     0.97497,     0.97598,     0.97698,     0.97798,     0.97898,     0.97998,     0.98098,     0.98198,     0.98298,     0.98398,\n",
+              "           0.98498,     0.98599,     0.98699,     0.98799,     0.98899,     0.98999,     0.99099,     0.99199,     0.99299,     0.99399,     0.99499,       0.996,       0.997,       0.998,       0.999,           1]), array([[    0.72414,     0.72414,     0.72414, ...,           0,           0,           0],\n",
+              "       [    0.70833,     0.70833,      0.6875, ...,           0,           0,           0],\n",
+              "       [    0.72093,     0.72093,     0.69767, ...,           0,           0,           0],\n",
+              "       [    0.80556,     0.80556,     0.77778, ...,           0,           0,           0],\n",
+              "       [       0.85,        0.85,         0.8, ...,           0,           0,           0],\n",
+              "       [    0.46429,     0.46429,     0.42857, ...,           0,           0,           0]]), 'Confidence', 'Recall']]\n",
+              "fitness: 0.10110146737160947\n",
+              "keys: ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)']\n",
+              "maps: array([  0.0074235,    0.064503,     0.17006,    0.084734,     0.21385,    0.046002,   0.0065694])\n",
+              "names: {0: 'elbow positive', 1: 'fingers positive', 2: 'forearm fracture', 3: 'humerus fracture', 4: 'humerus', 5: 'shoulder fracture', 6: 'wrist positive'}\n",
+              "plot: True\n",
+              "results_dict: {'metrics/precision(B)': 0.2776176602118742, 'metrics/recall(B)': 0.2798342742157898, 'metrics/mAP50(B)': 0.24840423040358597, 'metrics/mAP50-95(B)': 0.08473449370138986, 'fitness': 0.10110146737160947}\n",
+              "save_dir: PosixPath('runs/detect/train3')\n",
+              "speed: {'preprocess': 0.12799857676714316, 'inference': 1.300959751523774, 'loss': 0.0004240836220226069, 'postprocess': 2.2972459080575525}\n",
+              "task: 'detect'"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Testing the trained Model\n",
+        "---"
+      ],
+      "metadata": {
+        "id": "QgxqQPfkQ3gA"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model = YOLO('/content/drive/MyDrive/BoneFractureDataset/best_model.pt')"
+      ],
+      "metadata": {
+        "id": "Z5f-keQOQ12U"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "test = os.listdir('/content/drive/MyDrive/BoneFractureDataset/test/images')\n",
+        "test = [os.path.join('/content/drive/MyDrive/BoneFractureDataset/test/images' , path) for path in test]\n",
+        "results = model.predict(test)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "jfwGSpYsQ29p",
+        "outputId": "e1346a92-dc66-44cd-bb58-d0d04f05f8e0"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "0: 256x256 (no detections), 59.6ms\n",
+            "1: 256x256 (no detections), 59.6ms\n",
+            "2: 256x256 1 humerus, 59.6ms\n",
+            "3: 256x256 (no detections), 59.6ms\n",
+            "4: 256x256 (no detections), 59.6ms\n",
+            "5: 256x256 (no detections), 59.6ms\n",
+            "6: 256x256 2 humeruss, 59.6ms\n",
+            "7: 256x256 1 wrist positive, 59.6ms\n",
+            "8: 256x256 (no detections), 59.6ms\n",
+            "9: 256x256 (no detections), 59.6ms\n",
+            "10: 256x256 2 humeruss, 59.6ms\n",
+            "11: 256x256 (no detections), 59.6ms\n",
+            "12: 256x256 1 forearm fracture, 59.6ms\n",
+            "13: 256x256 (no detections), 59.6ms\n",
+            "14: 256x256 (no detections), 59.6ms\n",
+            "15: 256x256 1 humerus, 59.6ms\n",
+            "16: 256x256 (no detections), 59.6ms\n",
+            "17: 256x256 1 elbow positive, 59.6ms\n",
+            "18: 256x256 (no detections), 59.6ms\n",
+            "19: 256x256 (no detections), 59.6ms\n",
+            "20: 256x256 (no detections), 59.6ms\n",
+            "21: 256x256 (no detections), 59.6ms\n",
+            "22: 256x256 (no detections), 59.6ms\n",
+            "23: 256x256 (no detections), 59.6ms\n",
+            "24: 256x256 (no detections), 59.6ms\n",
+            "25: 256x256 (no detections), 59.6ms\n",
+            "26: 256x256 (no detections), 59.6ms\n",
+            "27: 256x256 1 humerus, 59.6ms\n",
+            "28: 256x256 (no detections), 59.6ms\n",
+            "29: 256x256 (no detections), 59.6ms\n",
+            "30: 256x256 (no detections), 59.6ms\n",
+            "31: 256x256 2 shoulder fractures, 59.6ms\n",
+            "32: 256x256 (no detections), 59.6ms\n",
+            "33: 256x256 (no detections), 59.6ms\n",
+            "34: 256x256 (no detections), 59.6ms\n",
+            "35: 256x256 (no detections), 59.6ms\n",
+            "36: 256x256 (no detections), 59.6ms\n",
+            "37: 256x256 (no detections), 59.6ms\n",
+            "38: 256x256 (no detections), 59.6ms\n",
+            "39: 256x256 (no detections), 59.6ms\n",
+            "40: 256x256 (no detections), 59.6ms\n",
+            "41: 256x256 (no detections), 59.6ms\n",
+            "42: 256x256 (no detections), 59.6ms\n",
+            "43: 256x256 (no detections), 59.6ms\n",
+            "44: 256x256 (no detections), 59.6ms\n",
+            "45: 256x256 (no detections), 59.6ms\n",
+            "46: 256x256 (no detections), 59.6ms\n",
+            "47: 256x256 (no detections), 59.6ms\n",
+            "48: 256x256 (no detections), 59.6ms\n",
+            "49: 256x256 (no detections), 59.6ms\n",
+            "50: 256x256 (no detections), 59.6ms\n",
+            "51: 256x256 1 wrist positive, 59.6ms\n",
+            "52: 256x256 (no detections), 59.6ms\n",
+            "53: 256x256 (no detections), 59.6ms\n",
+            "54: 256x256 1 humerus, 1 shoulder fracture, 59.6ms\n",
+            "55: 256x256 (no detections), 59.6ms\n",
+            "56: 256x256 (no detections), 59.6ms\n",
+            "57: 256x256 (no detections), 59.6ms\n",
+            "58: 256x256 (no detections), 59.6ms\n",
+            "59: 256x256 (no detections), 59.6ms\n",
+            "60: 256x256 (no detections), 59.6ms\n",
+            "61: 256x256 (no detections), 59.6ms\n",
+            "62: 256x256 (no detections), 59.6ms\n",
+            "63: 256x256 1 wrist positive, 59.6ms\n",
+            "64: 256x256 (no detections), 59.6ms\n",
+            "65: 256x256 (no detections), 59.6ms\n",
+            "66: 256x256 (no detections), 59.6ms\n",
+            "67: 256x256 (no detections), 59.6ms\n",
+            "68: 256x256 1 shoulder fracture, 59.6ms\n",
+            "69: 256x256 (no detections), 59.6ms\n",
+            "70: 256x256 (no detections), 59.6ms\n",
+            "71: 256x256 (no detections), 59.6ms\n",
+            "72: 256x256 (no detections), 59.6ms\n",
+            "73: 256x256 (no detections), 59.6ms\n",
+            "74: 256x256 (no detections), 59.6ms\n",
+            "75: 256x256 (no detections), 59.6ms\n",
+            "76: 256x256 (no detections), 59.6ms\n",
+            "77: 256x256 (no detections), 59.6ms\n",
+            "78: 256x256 (no detections), 59.6ms\n",
+            "79: 256x256 (no detections), 59.6ms\n",
+            "80: 256x256 (no detections), 59.6ms\n",
+            "81: 256x256 (no detections), 59.6ms\n",
+            "82: 256x256 1 wrist positive, 59.6ms\n",
+            "83: 256x256 (no detections), 59.6ms\n",
+            "84: 256x256 2 wrist positives, 59.6ms\n",
+            "85: 256x256 (no detections), 59.6ms\n",
+            "86: 256x256 (no detections), 59.6ms\n",
+            "87: 256x256 (no detections), 59.6ms\n",
+            "88: 256x256 (no detections), 59.6ms\n",
+            "89: 256x256 (no detections), 59.6ms\n",
+            "90: 256x256 (no detections), 59.6ms\n",
+            "91: 256x256 (no detections), 59.6ms\n",
+            "92: 256x256 (no detections), 59.6ms\n",
+            "93: 256x256 (no detections), 59.6ms\n",
+            "94: 256x256 (no detections), 59.6ms\n",
+            "95: 256x256 (no detections), 59.6ms\n",
+            "96: 256x256 (no detections), 59.6ms\n",
+            "97: 256x256 (no detections), 59.6ms\n",
+            "98: 256x256 1 wrist positive, 59.6ms\n",
+            "99: 256x256 1 elbow positive, 59.6ms\n",
+            "100: 256x256 (no detections), 59.6ms\n",
+            "101: 256x256 (no detections), 59.6ms\n",
+            "102: 256x256 (no detections), 59.6ms\n",
+            "103: 256x256 (no detections), 59.6ms\n",
+            "104: 256x256 (no detections), 59.6ms\n",
+            "105: 256x256 (no detections), 59.6ms\n",
+            "106: 256x256 1 forearm fracture, 59.6ms\n",
+            "107: 256x256 (no detections), 59.6ms\n",
+            "108: 256x256 (no detections), 59.6ms\n",
+            "109: 256x256 (no detections), 59.6ms\n",
+            "110: 256x256 (no detections), 59.6ms\n",
+            "111: 256x256 (no detections), 59.6ms\n",
+            "112: 256x256 (no detections), 59.6ms\n",
+            "113: 256x256 (no detections), 59.6ms\n",
+            "114: 256x256 (no detections), 59.6ms\n",
+            "115: 256x256 (no detections), 59.6ms\n",
+            "116: 256x256 (no detections), 59.6ms\n",
+            "117: 256x256 (no detections), 59.6ms\n",
+            "118: 256x256 3 shoulder fractures, 59.6ms\n",
+            "119: 256x256 (no detections), 59.6ms\n",
+            "120: 256x256 (no detections), 59.6ms\n",
+            "121: 256x256 1 humerus, 59.6ms\n",
+            "122: 256x256 (no detections), 59.6ms\n",
+            "123: 256x256 (no detections), 59.6ms\n",
+            "124: 256x256 (no detections), 59.6ms\n",
+            "125: 256x256 1 humerus, 59.6ms\n",
+            "126: 256x256 (no detections), 59.6ms\n",
+            "127: 256x256 (no detections), 59.6ms\n",
+            "128: 256x256 (no detections), 59.6ms\n",
+            "129: 256x256 1 shoulder fracture, 59.6ms\n",
+            "130: 256x256 (no detections), 59.6ms\n",
+            "131: 256x256 (no detections), 59.6ms\n",
+            "132: 256x256 (no detections), 59.6ms\n",
+            "133: 256x256 (no detections), 59.6ms\n",
+            "134: 256x256 (no detections), 59.6ms\n",
+            "135: 256x256 (no detections), 59.6ms\n",
+            "136: 256x256 (no detections), 59.6ms\n",
+            "137: 256x256 (no detections), 59.6ms\n",
+            "138: 256x256 (no detections), 59.6ms\n",
+            "139: 256x256 (no detections), 59.6ms\n",
+            "140: 256x256 (no detections), 59.6ms\n",
+            "141: 256x256 (no detections), 59.6ms\n",
+            "142: 256x256 (no detections), 59.6ms\n",
+            "143: 256x256 (no detections), 59.6ms\n",
+            "144: 256x256 (no detections), 59.6ms\n",
+            "145: 256x256 (no detections), 59.6ms\n",
+            "146: 256x256 (no detections), 59.6ms\n",
+            "147: 256x256 (no detections), 59.6ms\n",
+            "148: 256x256 (no detections), 59.6ms\n",
+            "149: 256x256 (no detections), 59.6ms\n",
+            "150: 256x256 1 shoulder fracture, 59.6ms\n",
+            "151: 256x256 1 forearm fracture, 59.6ms\n",
+            "152: 256x256 (no detections), 59.6ms\n",
+            "153: 256x256 (no detections), 59.6ms\n",
+            "154: 256x256 (no detections), 59.6ms\n",
+            "155: 256x256 (no detections), 59.6ms\n",
+            "156: 256x256 2 forearm fractures, 59.6ms\n",
+            "157: 256x256 1 wrist positive, 59.6ms\n",
+            "158: 256x256 1 forearm fracture, 59.6ms\n",
+            "159: 256x256 (no detections), 59.6ms\n",
+            "160: 256x256 (no detections), 59.6ms\n",
+            "161: 256x256 1 humerus, 59.6ms\n",
+            "162: 256x256 (no detections), 59.6ms\n",
+            "163: 256x256 (no detections), 59.6ms\n",
+            "164: 256x256 (no detections), 59.6ms\n",
+            "165: 256x256 1 shoulder fracture, 59.6ms\n",
+            "166: 256x256 (no detections), 59.6ms\n",
+            "167: 256x256 1 forearm fracture, 59.6ms\n",
+            "168: 256x256 1 forearm fracture, 59.6ms\n",
+            "Speed: 1.4ms preprocess, 59.6ms inference, 16.7ms postprocess per image at shape (1, 3, 256, 256)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "results[2].boxes # You can get the dimensions of the box and also the class index"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "sJgLZRNcJg_g",
+        "outputId": "8bc76a5f-097b-427e-b78a-d30eee9be8f3"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "ultralytics.engine.results.Boxes object with attributes:\n",
+              "\n",
+              "cls: tensor([4.])\n",
+              "conf: tensor([0.5513])\n",
+              "data: tensor([[334.8836, 186.0790, 447.9932, 343.2594,   0.5513,   4.0000]])\n",
+              "id: None\n",
+              "is_track: False\n",
+              "orig_shape: (420, 512)\n",
+              "shape: torch.Size([1, 6])\n",
+              "xywh: tensor([[391.4384, 264.6692, 113.1096, 157.1804]])\n",
+              "xywhn: tensor([[0.7645, 0.6302, 0.2209, 0.3742]])\n",
+              "xyxy: tensor([[334.8836, 186.0790, 447.9932, 343.2594]])\n",
+              "xyxyn: tensor([[0.6541, 0.4430, 0.8750, 0.8173]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "idx = 10\n",
+        "try:\n",
+        "  boxes = list(map(int, results[idx].boxes.xywh[0]))\n",
+        "  class_index = int(results[idx].boxes.cls[0])\n",
+        "  class_name = results[idx].names[class_index]\n",
+        "except IndexError:\n",
+        "  print(\"No Fracture Detected !!\")"
+      ],
+      "metadata": {
+        "id": "XQfNXt9wQ20B"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.colab.patches import cv2_imshow\n",
+        "font = cv2.FONT_HERSHEY_SIMPLEX\n",
+        "font_scale = 0.7\n",
+        "font_thickness = 2\n",
+        "\n",
+        "img = cv2.imread(test[idx])\n",
+        "cv2.rectangle(img, (x-w//2, y-h//2), ((x-w//2)+ w ,(y-h//2)+ h), (0, 0, 255),2)\n",
+        "cv2.putText(img , class_name , (x-w//2, y-h//2-5) , font , font_scale ,(0, 255, 0), font_thickness)\n",
+        "cv2_imshow(img)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 529
+        },
+        "id": "uPWTALaYTqdf",
+        "outputId": "dfedcb5e-75af-4c06-81a4-605dad8cac94"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<PIL.Image.Image image mode=RGB size=509x512>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Bone Fracture Detection/README.md b/Bone Fracture Detection/README.md
new file mode 100644
index 000000000..d3fc3f7c0
--- /dev/null
+++ b/Bone Fracture Detection/README.md	
@@ -0,0 +1,63 @@
+## **Bone Fracture Detection Using Object Detection Algorithms**
+
+### 🎯 **`Goal`**
+---
+The Goal of the project is to detect Bone fractures in a given X-ray image using state-of-the-art models which are pretrained over huge datasets
+
+### 🧵 **`Dataset`**
+---
+I have used Bone Fracture Detection dataset of Kaggle
+
+### 🧾 **`Description`** 
+---
+This Code base implements two object detection models that are used to localize fractures by drawing boxes around them and classify them into appropriate types (as given in the dataset description). These models have the potential to do human level tasks , when trained over large dataset with proper tuning of the parameters.
+
+
+### 🧮 **`What have I done`**
+
+We used two powerful models for spotting objects: YOLOv8 by Ultralytics and Faster-RCNN by Facebook's Detectron. Each has its own way of handling data and training.
+
+#### **Choosing Models and Getting Data Ready**
+
+- **YOLOv8 (Ultralytics):**
+  - It's fast and accurate for spotting objects.
+  - The data we had was already in a good format for this model, so we didn't need to do much to it before using it.
+
+- **Faster-RCNN (Facebook's Detectron):**
+  - This one is great for detailed object spotting.
+  - We had to make sure our data matched up with the annotations required by the model. Detectron helps with this by automatically organizing the annotations.
+
+#### **Getting Data Ready and Training**
+
+- We got our data set up and tweaked it to fit each model's needs.
+- YOLOv8 needed less tweaking since our data was already in a good format.
+- For Faster-RCNN, we had to make sure the data and annotations were aligned properly, but Detectron helped with this part.
+
+#### **Training and Checking Performance**
+
+- We adjusted settings like how fast the model learns, how many times it looks at the data (epochs), and how much data it looks at each time (batch size).
+- Detectron made it easy to see how well our models were doing by automatically checking their performance and storing the best versions.
+
+#### **Sharing the Models**
+
+- Once our models were trained, we saved them in a way that makes them easy to share and use in other places, like pickle files.
+
+By tailoring our approach to each model's requirements and using helpful tools like Detectron, we made the most of YOLOv8 and Faster-RCNN for spotting objects accurately and efficiently.
+### 🚀 **`Models Implemented`**
+ ---
+I have used two different models 
+- YOLOv8 Model
+- Faster-RCNN pretrained over ResnetXt 
+
+
+### 📢 **`Conclusion`**
+---
+In conclusion, our journey in leveraging object detection models, YOLOv8 and Faster-RCNN, has been marked by thorough research and rigorous testing. By aligning data processing with model requirements and optimizing parameters, we achieved efficient and accurate results. Moving forward, exploring diverse hyperparameter tuning strategies and integrating real-time datasets could enhance performance and robustness.
+
+### ✒️  **`Author`**
+---
+`Bingumalla Likith |
+GSSoC 24 Contributor|
+Issue Number #457`
+
+[![LinkedIn](https://img.shields.io/badge/linkedin-%230077B5.svg?style=for-the-badge&logo=linkedin&logoColor=white)](www.linkedin.com/in/bingumalla-likith-2633392b9)  [![GitHub](https://img.shields.io/badge/github-%23121011.svg?style=for-the-badge&logo=github&logoColor=white)](https://github.com/binguliki)
\ No newline at end of file