ViT_multiclass_image_classification_training.ipynb

{
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      "metadata": {
        "id": "pdcMxVGEA9Cd"
      },
      "source": [
        "---\n",
        "## **👀 ViT for Image Classification**\n",
        "---\n",
        "This notebook shows how to fine-tune (train an already existing model with transfer learning) a pretrained Vision Transformer (ViT) checkpoint for a multiclass image classification task and how to prepare the necessary custom dataset. It works by adding a randomly initialized classification head on top of a pre-trained encoder, thus fine-tuning the model on a labeled dataset. It is based on Huggingface’s ViT implementation as showcased in this [blog article](https://huggingface.co/blog/fine-tune-vit).\n",
        "\n",
        "This example uses the\n",
        "https://huggingface.co/microsoft/swin-tiny-patch4-window7-224 checkpoint.\n",
        "More pre-trained models for vision task are available [here](https://huggingface.co/models?other=vision).\n",
        "\n",
        "\n",
        "---\n",
        "## 💾 Dataset\n",
        "---\n",
        "\n",
        "In this notebook we will format our custom dataset according to the [ImageFolder](https://huggingface.co/docs/datasets/v2.0.0/en/image_process#imagefolder) feature. You can load a `Dataset` from local folders or from local/remote files, like zip or tar. Data (the image files) should be sorted and placed in individual directories each named with their corresponging class, (i.e.: \"in\", \"out\") as follow:\n",
        "```.\n",
        "├── in                             # Directory with examples for the \"in\" class\n",
        "│   ├── user4-10fps-960-44.jpg     # Example 1 for the \"in\" class\n",
        "│   ├── user27-10fps-960-1880.jpg  # Example 2 for the \"in\" class\n",
        "│   ├── user43-10fps-960-1914.jpg  # Example 3 for the \"in\" class\n",
        "│   └── ...                        # etc.\n",
        "├── out                            # Directory with examples for the \"out\" class\n",
        "│   ├── user4-10fps-960-209.jpg    # Example 1 for the \"out\" class\n",
        "│   ├── user31-10fps-960-5319.jpg  # Example 2 for the \"out\" class\n",
        "│   ├── user54-10fps-960-9022.jpg  # Example 3 for the \"out\" class\n",
        "│   └── ...                        # etc.\n",
        "└── ...                            # etc. You can have as many classes as you want\n",
        "```\n",
        "\n",
        "---\n",
        "## 🗄 Any model\n",
        "---\n",
        "This example can run any image classification dataset with any vision model checkpoint from [Huggingface’s model hub](https://huggingface.co/) as long as the model has a version with a Image Classification head. That is any model supported by [AutoModelForImageClassification](https://huggingface.co/docs/transformers/model_doc/auto#transformers.AutoModelForImageClassification). Examples include:\n",
        "* [ViT](https://huggingface.co/docs/transformers/model_doc/vit#transformers.ViTForImageClassification)\n",
        "* [Swin Transformer](https://huggingface.co/docs/transformers/model_doc/swin#transformers.SwinForImageClassification)\n",
        "* [ConvNeXT](https://huggingface.co/docs/transformers/master/en/model_doc/convnext#transformers.ConvNextForImageClassification)\n",
        "\n",
        "\n",
        "---\n",
        "## 📈 Data augmentation\n",
        "---\n",
        "The dataset used in the example does not call for data augmentation, because the idea is to classify a finite amount of images whose features are identical to the training set. Therefore, it doesn’t need to be able to generalize beyond the scope of what it has “seen” in the training set. However, for other tasks where the model is meant to infer and classify examples that are significantly different from the training set, the notebook includes an example of data augmentation using Torchvision's [transforms](https://pytorch.org/vision/stable/transforms.html) feature. Other libraries for data augmentation include:\n",
        "* [Albumentations](https://github.com/huggingface/notebooks/blob/main/examples/image_classification_albumentations.ipynb)\n",
        "* [Kornia](https://github.com/huggingface/notebooks/blob/main/examples/image_classification_kornia.ipynb)\n",
        "* [imgaug](https://github.com/huggingface/notebooks/blob/main/examples/image_classification_imgaug.ipynb).\n",
        "\n",
        "Preprocessing images typically comes down to (1) resizing them to a particular size (2) normalizing the color channels (R,G,B) using a mean and standard deviation. These are referred to as **image transformations**.\n",
        "\n",
        "In addition, one typically performs what is called **data augmentation** during training (like random cropping and flipping) to make the model more robust and achieve higher accuracy. Data augmentation is also a great technique to increase the size of the training data.\n",
        "\n",
        "We will use `torchvision.transforms` for the image transformations/data augmentation in this tutorial, but note that one can use any other package (like [albumentations](https://albumentations.ai/), [imgaug](https://github.com/aleju/imgaug), [Kornia](https://kornia.readthedocs.io/en/latest/) etc.).\n",
        "\n",
        "To make sure we (1) resize to the appropriate size (2) use the appropriate image mean and standard deviation for the model architecture we are going to use, we instantiate what is called a feature extractor with the `AutoFeatureExtractor.from_pretrained` method.\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "Depending on the model and the GPU you are using, you might need to adjust the batch size to avoid out-of-memory errors. Set those two parameters, then the rest of the notebook should run smoothly."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "5WMEawzyCEyG"
      },
      "outputs": [],
      "source": [
        "model_checkpoint = \"microsoft/swin-tiny-patch4-window7-224\" # pre-trained model from which to fine-tune\n",
        "batch_size = 32 # batch size for training and evaluation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NlArTG8KChJf"
      },
      "source": [
        "Before we start, let's install the `datasets` and `transformers` libraries."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "L1532RVbJgQV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9b90e6fc-a10f-48cf-80c1-1f4caa657522"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m519.3/519.3 kB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -q datasets transformers\n",
        "! pip install accelerate>=0.20.1\n",
        "#! pip install -U transformers\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "snZ1tmaOC412"
      },
      "source": [
        "**If** you're opening this notebook locally, make sure your environment has an install from the last version of those libraries.\n",
        "\n",
        "To be able to share your model with the community and generate results like the one shown in the picture below via the inference API, there are a few more steps to follow.\n",
        "\n",
        "First you have to store your authentication token from the Hugging Face website (sign up [here](https://huggingface.co/join) if you haven't already!) then execute the following cell and input your token:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
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        },
        "id": "Bkpk_JPlCww8",
        "outputId": "60ff8191-f046-4985-84a0-dceb9c5edc67"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a34bfb4e5f5145e4adaac1af38fc92e7"
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          },
          "metadata": {}
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JJ0986jTDZRC"
      },
      "source": [
        "\n",
        "Then you need to install Git-LFS to upload your model checkpoints:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "t7JEhLjDDcVd"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!sudo apt -qq install git-lfs\n",
        "!git config --global credential.helper store"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XalxdrirGkLl"
      },
      "source": [
        "## Fine-tuning a model on an image classification task"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mcE455KaG687"
      },
      "source": [
        "### Loading the dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RD_G2KJgG_bU"
      },
      "source": [
        "We will use the [🤗 Datasets](https://github.com/huggingface/datasets) library's [ImageFolder](https://huggingface.co/docs/datasets/v2.0.0/en/image_process#imagefolder) feature to download our custom dataset into a DatasetDict.\n",
        "\n",
        "In this case, the EuroSAT dataset is hosted remotely, so we provide the `data_files` argument. Alternatively, if you have local folders with images, you can load them using the `data_dir` argument."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
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        "colab": {
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      "outputs": [
        {
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          "data": {
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              "Downloading data files: 0it [00:00, ?it/s]"
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          "metadata": {}
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        {
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          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
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              "model_id": "bd09bd33d1c14f67a542a6985acee7b8"
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          "metadata": {}
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      ],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "# load a custom dataset from local/remote files or folders using the ImageFolder feature\n",
        "\n",
        "# option 1: local/remote files (supporting the following formats: tar, gzip, zip, xz, rar, zstd)\n",
        "#dataset = load_dataset(\"imagefolder\", data_files=\"/content/ds.zip\")\n",
        "\n",
        "# note that you can also provide several splits:\n",
        "# dataset = load_dataset(\"imagefolder\", data_files={\"train\": [\"/content/BiodivAR-user-test-eye-tracking-data.v1-v1-2023-02-04-1-55pm.folder/train\"], \"test\": [\"/content/BiodivAR-user-test-eye-tracking-data.v1-v1-2023-02-04-1-55pm.folder/test\"]})\n",
        "\n",
        "# note that you can push your dataset to the hub very easily (and reload afterwards using load_dataset)!\n",
        "# dataset.push_to_hub(\"nielsr/eurosat\")\n",
        "# dataset.push_to_hub(\"nielsr/eurosat\", private=True)\n",
        "\n",
        "# option 2: local folder\n",
        "# dataset = load_dataset(\"imagefolder\", data_dir=\"/Users/julienmercier/Downloads/BiodivAR-user-test-eye-tracking-data.v1-v1-2023-02-04-1-55pm.folder/\")\n",
        "\n",
        "# option 3: just load any existing dataset from the hub, like CIFAR-10, FashionMNIST ...\n",
        "dataset = load_dataset(\"julienmercier/mobile-eye-tracking-dataset-v2\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eq8mwsZU2j6t"
      },
      "source": [
        "Let us also load the Accuracy metric, which we'll use to evaluate our model both during and after training."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104,
          "referenced_widgets": [
            "fa5920e2a6034bf7a9428985e1da3c3a",
            "e82e204d474b49058a8c9dc48b08ea48",
            "a3a8c549df2948bdb72928aaf7bde3c2",
            "25211f243a1d44509bb7daa780bdb781",
            "cc5d907320b54b3cbd6c90a328516eee",
            "b0cf4e29ecf743728783deeebbf2db18",
            "b643fce6aebf44fdb060d9eb52617b7f",
            "4c9b8e0cbc6e4b7cb912872c8111f0a5",
            "84876a03279e4968a564b0d4dc0961b5",
            "c81fdc0efbbf42459a918474a69e5ce7",
            "9345f64898f24ed2a72e8251eee896b4"
          ]
        },
        "id": "8UGse36eLeeb",
        "outputId": "bb7b9f11-2f07-453b-d375-845bf2238129"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-7-350fcae91df9>:3: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate\n",
            "  metric = load_metric(\"accuracy\")\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/1.65k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fa5920e2a6034bf7a9428985e1da3c3a"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from datasets import load_metric\n",
        "\n",
        "metric = load_metric(\"accuracy\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r8mTmFdlHOmN"
      },
      "source": [
        "The `dataset` object itself is a [`DatasetDict`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasetdict), which contains one key per split (in this case, only \"train\" for a training split)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7tjOWPQYLq4u",
        "outputId": "cd2436ac-4b15-47ef-fc79-a56da8a8e445"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['image', 'label'],\n",
              "        num_rows: 7845\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nfPPNjthI3u2"
      },
      "source": [
        "To access an actual element, you need to select a split first, then give an index:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BujWoSgyMQlw",
        "outputId": "57bfa911-e17b-4fd4-fbde-5fd711c780d3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=224x224>,\n",
              " 'label': 0}"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "example = dataset[\"train\"][1844]\n",
        "example"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9g0APa21I_Rx"
      },
      "source": [
        "Each example consists of an image and a corresponding label. We can also verify this by checking the features of the dataset:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BnnL3yHBI7Z3",
        "outputId": "6b880a3c-7d6c-4eed-f5d8-11d203b18674"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'image': Image(decode=True, id=None),\n",
              " 'label': ClassLabel(names=['in', 'none', 'out'], id=None)}"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "dataset[\"train\"].features"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SZ7rLOsAkJ8F"
      },
      "source": [
        "The cool thing is that we can directly view the image (as the 'image' field is an [Image feature](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Image)), as follows:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        },
        "id": "32iolZyTkNlI",
        "outputId": "630d9980-91c4-4707-f029-d49554482563"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=100x100>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "example['image'].resize((100, 100))\n",
        "# print the label\n",
        "# example['label']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f8GpxwfnJCkF"
      },
      "source": [
        "As you can see, the `label` field is not an actual string label. By default the `ClassLabel` fields are encoded into integers for convenience:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n33LZz_ZMU3o",
        "outputId": "91fe32d1-fc4e-41b5-e5ef-582446a1a23a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ClassLabel(names=['in', 'none', 'out'], id=None)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "dataset[\"train\"].features[\"label\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5LdpDtScLgeD"
      },
      "source": [
        "Let's create an `id2label` dictionary to decode them back to strings and see what they are. The inverse `label2id` will be useful too, when we load the model later."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "UuyXDtQqNUZW",
        "outputId": "6eb6891d-b892-4e42-c108-bc47659bb9c6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'none'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "labels = dataset[\"train\"].features[\"label\"].names\n",
        "label2id, id2label = dict(), dict()\n",
        "for i, label in enumerate(labels):\n",
        "    label2id[label] = i\n",
        "    id2label[i] = label\n",
        "\n",
        "id2label[1]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4zxoikSOjs0K"
      },
      "source": [
        "### Preprocessing the data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WTupOU88p1lK"
      },
      "source": [
        "Before we can feed these images to our model, we need to preprocess them.\n",
        "\n",
        "\n",
        "This feature extractor is a minimal preprocessor that can be used to prepare images for inference."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503,
          "referenced_widgets": [
            "c5e31f39cb0d47789a008d2b9166ac37",
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            "2740519e317d4e2897e1ffd7b51bbaf9",
            "2cd8fd91aefa4495a744a5b285fcd6df"
          ]
        },
        "id": "G1bX4lGAO_d9",
        "outputId": "d67abe39-e3ab-4d37-e2c8-e46e7a543d2b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)rocessor_config.json:   0%|          | 0.00/255 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c5e31f39cb0d47789a008d2b9166ac37"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/models/vit/feature_extraction_vit.py:28: FutureWarning: The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please use ViTImageProcessor instead.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ViTFeatureExtractor {\n",
              "  \"do_normalize\": true,\n",
              "  \"do_rescale\": true,\n",
              "  \"do_resize\": true,\n",
              "  \"feature_extractor_type\": \"ViTFeatureExtractor\",\n",
              "  \"image_mean\": [\n",
              "    0.485,\n",
              "    0.456,\n",
              "    0.406\n",
              "  ],\n",
              "  \"image_processor_type\": \"ViTFeatureExtractor\",\n",
              "  \"image_std\": [\n",
              "    0.229,\n",
              "    0.224,\n",
              "    0.225\n",
              "  ],\n",
              "  \"resample\": 3,\n",
              "  \"rescale_factor\": 0.00392156862745098,\n",
              "  \"size\": {\n",
              "    \"height\": 224,\n",
              "    \"width\": 224\n",
              "  }\n",
              "}"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "from transformers import AutoFeatureExtractor\n",
        "\n",
        "feature_extractor = AutoFeatureExtractor.from_pretrained(model_checkpoint)\n",
        "feature_extractor"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qUtxmoMvqml1"
      },
      "source": [
        "The Datasets library is made for processing data very easily. We can write custom functions, which can then be applied on an entire dataset (either using [`.map()`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=map#datasets.Dataset.map) or [`.set_transform()`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=set_transform#datasets.Dataset.set_transform)).\n",
        "\n",
        "Here we define 2 separate functions, one for training (which includes data augmentation) and one for validation (which only includes resizing, center cropping and normalizing)."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torchvision.transforms import (\n",
        "    CenterCrop,\n",
        "    Compose,\n",
        "    Normalize,\n",
        "    RandomHorizontalFlip,\n",
        "    RandomResizedCrop,\n",
        "    Resize,\n",
        "    ToTensor,\n",
        ")\n",
        "feature_extractor.size"
      ],
      "metadata": {
        "id": "c0b2b8VnVkJu",
        "outputId": "c3540366-e689-4e05-de66-9c3eafea3348",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'height': 224, 'width': 224}"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "4O_p3WrpRyej"
      },
      "outputs": [],
      "source": [
        "from torchvision.transforms import (\n",
        "    CenterCrop,\n",
        "    Compose,\n",
        "    Normalize,\n",
        "    RandomHorizontalFlip,\n",
        "    RandomResizedCrop,\n",
        "    Resize,\n",
        "    ToTensor,\n",
        ")\n",
        "\n",
        "normalize = Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std)\n",
        "train_transforms = Compose(\n",
        "        [\n",
        "            RandomResizedCrop(feature_extractor.size),\n",
        "            RandomHorizontalFlip(),\n",
        "            ToTensor(),\n",
        "            normalize,\n",
        "        ]\n",
        "    )\n",
        "\n",
        "val_transforms = Compose(\n",
        "        [\n",
        "            Resize(feature_extractor.size['height']),\n",
        "            CenterCrop(feature_extractor.size['height']),\n",
        "            ToTensor(),\n",
        "            normalize,\n",
        "        ]\n",
        "    )\n",
        "\n",
        "def preprocess_train(example_batch):\n",
        "    \"\"\"Apply train_transforms across a batch.\"\"\"\n",
        "    example_batch[\"pixel_values\"] = [\n",
        "        train_transforms(image.convert(\"RGB\")) for image in example_batch[\"image\"]\n",
        "    ]\n",
        "    return example_batch\n",
        "\n",
        "def preprocess_val(example_batch):\n",
        "    \"\"\"Apply val_transforms across a batch.\"\"\"\n",
        "    example_batch[\"pixel_values\"] = [val_transforms(image.convert(\"RGB\")) for image in example_batch[\"image\"]]\n",
        "    return example_batch"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RF4O0KFBGXir"
      },
      "source": [
        "Next, we can preprocess our dataset by applying these functions. We will use the `set_transform` functionality, which allows to apply the functions above on-the-fly (meaning that they will only be applied when the images are loaded in RAM)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "P13tqfFTZ_F4"
      },
      "outputs": [],
      "source": [
        "# split up training into training + validation\n",
        "splits = dataset[\"train\"].train_test_split(test_size=0.05)\n",
        "train_ds = splits['train']\n",
        "val_ds = splits['test']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "TUs56-mprQi1"
      },
      "outputs": [],
      "source": [
        "train_ds.set_transform(preprocess_train)\n",
        "val_ds.set_transform(preprocess_val)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MMw_wQS58a7o"
      },
      "source": [
        "Let's access an element to see that we've added a \"pixel_values\" feature:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ng9TAlDV8d7r",
        "outputId": "14b7b5b7-e465-4958-faae-47eb6f240cc5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ClassLabel(names=['in', 'none', 'out'], id=None)"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ],
      "source": [
        "val_ds.features['image']\n",
        "val_ds.features['label']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HOXmyPQ76Qv9"
      },
      "source": [
        "### Training the model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0a-2YT7O6ayC"
      },
      "source": [
        "Now that our data is ready, we can download the pretrained model and fine-tune it. For classification we use the `AutoModelForImageClassification` class. Calling the `from_pretrained` method on it will download and cache the weights for us. As the label ids and the number of labels are dataset dependent, we pass `label2id`, and `id2label` alongside the `model_checkpoint` here. This will make sure a custom classification head will be created (with a custom number of output neurons).\n",
        "\n",
        "NOTE: in case you're planning to fine-tune an already fine-tuned checkpoint, like [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) (which has already been fine-tuned on ImageNet-1k), then you need to provide the additional argument `ignore_mismatched_sizes=True` to the `from_pretrained` method. This will make sure the output head (with 1000 output neurons) is thrown away and replaced by a new, randomly initialized classification head that includes a custom number of output neurons. You don't need to specify this argument in case the pre-trained model doesn't include a head."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170,
          "referenced_widgets": [
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            "27ff3d5f2e71453ca772594bf267faff"
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        },
        "id": "X9DDujL0q1ac",
        "outputId": "efe8a818-8445-4944-b6a0-1d3539b2da62"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/71.8k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "055e0a069f974e75aaf73fbe7f0c6cc2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/113M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d425f62befec44e1ab6b72880bbcd84b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of SwinForImageClassification were not initialized from the model checkpoint at microsoft/swin-tiny-patch4-window7-224 and are newly initialized because the shapes did not match:\n",
            "- classifier.weight: found shape torch.Size([1000, 768]) in the checkpoint and torch.Size([3, 768]) in the model instantiated\n",
            "- classifier.bias: found shape torch.Size([1000]) in the checkpoint and torch.Size([3]) in the model instantiated\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoModelForImageClassification, TrainingArguments, Trainer\n",
        "\n",
        "model = AutoModelForImageClassification.from_pretrained(\n",
        "    model_checkpoint,\n",
        "    label2id=label2id,\n",
        "    id2label=id2label,\n",
        "    ignore_mismatched_sizes = True, # provide this in case you're planning to fine-tune an already fine-tuned checkpoint\n",
        ")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U8EmET_f6458"
      },
      "source": [
        "The warning is telling us we are throwing away some weights (the weights and bias of the `classifier` layer) and randomly initializing some other (the weights and bias of a new `classifier` layer). This is expected in this case, because we are adding a new head for which we don't have pretrained weights, so the library warns us we should fine-tune this model before using it for inference, which is exactly what we are going to do."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FEfyuq1U8hDT"
      },
      "source": [
        "To instantiate a `Trainer`, we will need to define the training configuration and the evaluation metric. The most important is the [`TrainingArguments`](https://huggingface.co/transformers/main_classes/trainer.html#transformers.TrainingArguments), which is a class that contains all the attributes to customize the training. It requires one folder name, which will be used to save the checkpoints of the model.\n",
        "\n",
        "Most of the training arguments are pretty self-explanatory, but one that is quite important here is `remove_unused_columns=False`. This one will drop any features not used by the model's call function. By default it's `True` because usually it's ideal to drop unused feature columns, making it easier to unpack inputs into the model's call function. But, in our case, we need the unused features ('image' in particular) in order to create 'pixel_values'."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "id": "xc_MTm0Ks3DF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 565
        },
        "outputId": "6be1f56a-7a62-4325-b727-ff21f776f853"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "ImportError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-75-5d15a3d45947>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mmodel_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_checkpoint\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m args = TrainingArguments(\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0;34mf\"{model_name}-finetuned-mobile-eye-tracking-dataset-v2\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mremove_unused_columns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mevaluation_strategy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"epoch\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/training_args.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, output_dir, overwrite_output_dir, do_train, do_eval, do_predict, evaluation_strategy, prediction_loss_only, per_device_train_batch_size, per_device_eval_batch_size, per_gpu_train_batch_size, per_gpu_eval_batch_size, gradient_accumulation_steps, eval_accumulation_steps, eval_delay, learning_rate, weight_decay, adam_beta1, adam_beta2, adam_epsilon, max_grad_norm, num_train_epochs, max_steps, lr_scheduler_type, warmup_ratio, warmup_steps, log_level, log_level_replica, log_on_each_node, logging_dir, logging_strategy, logging_first_step, logging_steps, logging_nan_inf_filter, save_strategy, save_steps, save_total_limit, save_safetensors, save_on_each_node, no_cuda, use_mps_device, seed, data_seed, jit_mode_eval, use_ipex, bf16, fp16, fp16_opt_level, half_precision_backend, bf16_full_eval, fp16_full_eval, tf32, local_rank, ddp_backend, tpu_num_cores, tpu_metrics_debug, debug, dataloader_drop_last, eval_steps, dataloader_num_workers, past_index, run_name, disable_tqdm, remove_unused_columns, label_names, load_best_model_at_end, metric_for_best_model, greater_is_better, ignore_data_skip, sharded_ddp, fsdp, fsdp_min_num_params, fsdp_config, fsdp_transformer_layer_cls_to_wrap, deepspeed, label_smoothing_factor, optim, optim_args, adafactor, group_by_length, length_column_name, report_to, ddp_find_unused_parameters, ddp_bucket_cap_mb, ddp_broadcast_buffers, dataloader_pin_...\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/training_args.py\u001b[0m in \u001b[0;36m__post_init__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1370\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"pt\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1371\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0mis_torch_available\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1372\u001b[0;31m             \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"cuda\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1373\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mget_xla_device_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"GPU\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1374\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfp16\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfp16_full_eval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/training_args.py\u001b[0m in \u001b[0;36mdevice\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1793\u001b[0m         \"\"\"\n\u001b[1;32m   1794\u001b[0m         \u001b[0mrequires_backends\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"torch\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1795\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_devices\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1796\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1797\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/utils/generic.py\u001b[0m in \u001b[0;36m__get__\u001b[0;34m(self, obj, objtype)\u001b[0m\n\u001b[1;32m     52\u001b[0m         \u001b[0mcached\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcached\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 54\u001b[0;31m             \u001b[0mcached\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     55\u001b[0m             \u001b[0msetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcached\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcached\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/training_args.py\u001b[0m in \u001b[0;36m_setup_devices\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1714\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_sagemaker_mp_enabled\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1715\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_accelerate_available\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmin_version\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"0.20.1\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1716\u001b[0;31m                 raise ImportError(\n\u001b[0m\u001b[1;32m   1717\u001b[0m                     \u001b[0;34m\"Using the `Trainer` with `PyTorch` requires `accelerate>=0.20.1`: Please run `pip install transformers[torch]` or `pip install accelerate -U`\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1718\u001b[0m                 )\n",
            "\u001b[0;31mImportError\u001b[0m: Using the `Trainer` with `PyTorch` requires `accelerate>=0.20.1`: Please run `pip install transformers[torch]` or `pip install accelerate -U`",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ],
          "errorDetails": {
            "actions": [
              {
                "action": "open_url",
                "actionText": "Open Examples",
                "url": "/notebooks/snippets/importing_libraries.ipynb"
              }
            ]
          }
        }
      ],
      "source": [
        "model_name = model_checkpoint.split(\"/\")[-1]\n",
        "args = TrainingArguments(\n",
        "    f\"{model_name}-finetuned-mobile-eye-tracking-dataset-v2\",\n",
        "    remove_unused_columns=False,\n",
        "    evaluation_strategy = \"epoch\",\n",
        "    save_strategy = \"epoch\",\n",
        "    learning_rate=5e-5,\n",
        "    per_device_train_batch_size=batch_size,\n",
        "    gradient_accumulation_steps=4,\n",
        "    per_device_eval_batch_size=batch_size,\n",
        "    num_train_epochs=3,\n",
        "    warmup_ratio=0.1,\n",
        "    logging_steps=10,\n",
        "    load_best_model_at_end=True,\n",
        "    metric_for_best_model=\"accuracy\",\n",
        "    push_to_hub=True,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xi6JYNYs8lJO"
      },
      "source": [
        "Here we set the evaluation to be done at the end of each epoch, tweak the learning rate, use the `batch_size` defined at the top of the notebook and customize the number of epochs for training, as well as the weight decay. Since the best model might not be the one at the end of training, we ask the `Trainer` to load the best model it saved (according to `metric_name`) at the end of training.\n",
        "\n",
        "The last argument `push_to_hub` allows the Trainer to push the model to the [Hub](https://huggingface.co/models) regularly during training. Remove it if you didn't follow the installation steps at the top of the notebook. If you want to save your model locally with a name that is different from the name of the repository, or if you want to push your model under an organization and not your name space, use the `hub_model_id` argument to set the repo name (it needs to be the full name, including your namespace: for instance `\"nielsr/vit-finetuned-cifar10\"` or `\"huggingface/nielsr/vit-finetuned-cifar10\"`)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2VE_HSha9RZk"
      },
      "source": [
        "Next, we need to define a function for how to compute the metrics from the predictions, which will just use the `metric` we loaded earlier. The only preprocessing we have to do is to take the argmax of our predicted logits:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "id": "EVWfiBuv2uCS"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "\n",
        "# the compute_metrics function takes a Named Tuple as input:\n",
        "# predictions, which are the logits of the model as Numpy arrays,\n",
        "# and label_ids, which are the ground-truth labels as Numpy arrays.\n",
        "def compute_metrics(eval_pred):\n",
        "    \"\"\"Computes accuracy on a batch of predictions\"\"\"\n",
        "    predictions = np.argmax(eval_pred.predictions, axis=1)\n",
        "    return metric.compute(predictions=predictions, references=eval_pred.label_ids)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y0PqjzHQVutb"
      },
      "source": [
        "We also define a `collate_fn`, which will be used to batch examples together.\n",
        "Each batch consists of 2 keys, namely `pixel_values` and `labels`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "id": "u0WcwsX7rW9w"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "\n",
        "def collate_fn(examples):\n",
        "    pixel_values = torch.stack([example[\"pixel_values\"] for example in examples])\n",
        "    labels = torch.tensor([example[\"label\"] for example in examples])\n",
        "    return {\"pixel_values\": pixel_values, \"labels\": labels}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yTF0dWw49fB9"
      },
      "source": [
        "Then we just need to pass all of this along with our datasets to the `Trainer`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 246
        },
        "id": "McVoaCPr3Cj-",
        "outputId": "9e7bdb7b-c4ef-4259-9e5e-86645ffa3a02"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-64-595e7e9f60bb>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m trainer = Trainer(\n\u001b[1;32m      2\u001b[0m     \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0mtrain_dataset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain_ds\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0meval_dataset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_ds\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'args' is not defined"
          ]
        }
      ],
      "source": [
        "trainer = Trainer(\n",
        "    model,\n",
        "    args,\n",
        "    train_dataset=train_ds,\n",
        "    eval_dataset=val_ds,\n",
        "    tokenizer=feature_extractor,\n",
        "    compute_metrics=compute_metrics,\n",
        "    data_collator=collate_fn,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ltokP9mO9pjI"
      },
      "source": [
        "You might wonder why we pass along the `feature_extractor` as a tokenizer when we already preprocessed our data. This is only to make sure the feature extractor configuration file (stored as JSON) will also be uploaded to the repo on the hub."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9j6VNsGP97LG"
      },
      "source": [
        "Now we can finetune our model by calling the `train` method:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "398c087633254bcaaf781aa57e982dd6",
            "f200c07beda8456a92e897ee00b5fde5",
            "1f66f9bb6f374902ac87747b55fdede6",
            "fb8b8971f3714b16ac699a0bb9435dae",
            "b14bfd13a6294a9cb6f8226d43b2ba3d",
            "1de73b85fab34677b26258cf81b1f12f",
            "5ad22e5e6c7c464c8ac943b9730954a1",
            "dfe11154745547058a1158a2e8c7d620",
            "950d18b052794bdd85e9981dba17d253",
            "a98957fcd9204fdc83a14bb710da2fd8",
            "6da1bfe89c754d74868af73bff068ebb",
            "898560cd6bca49d7bbc530070737e348",
            "cb6c133b4a524edd98b35f43d4feef0e",
            "85aa3489e82442d38d387e9a1df8d920",
            "51b8c526e3594d3cac19c923160d082c",
            "0f5ce894e0ee47c6af02f709256ac641",
            "56ec636f14e749e1abc2d910c4ee08d7",
            "97d07155fabd42e580e130b4f1b50bff",
            "9d3a80f84461463da2d7135da04d0b8d",
            "dd7bebb0ca9c473aab467b9c22105bf8",
            "536f6919a19d435e9f19055eacdf143e",
            "111d49a2cbd142b79d50252805c79285"
          ]
        },
        "id": "Pps61vF_4QaH",
        "outputId": "b79a2940-d366-49f6-ec9c-d4b862485777"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:309: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  FutureWarning,\n",
            "***** Running training *****\n",
            "  Num examples = 24300\n",
            "  Num Epochs = 3\n",
            "  Instantaneous batch size per device = 32\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 128\n",
            "  Gradient Accumulation steps = 4\n",
            "  Total optimization steps = 570\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='570' max='570' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [570/570 16:11, Epoch 3/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.262100</td>\n",
              "      <td>0.108344</td>\n",
              "      <td>0.962963</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.176900</td>\n",
              "      <td>0.142533</td>\n",
              "      <td>0.950000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.134300</td>\n",
              "      <td>0.066442</td>\n",
              "      <td>0.974444</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 2700\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to swin-tiny-patch4-window7-224-finetuned-eurosat/checkpoint-190\n",
            "Configuration saved in swin-tiny-patch4-window7-224-finetuned-eurosat/checkpoint-190/config.json\n",
            "Model weights saved in swin-tiny-patch4-window7-224-finetuned-eurosat/checkpoint-190/pytorch_model.bin\n",
            "Feature extractor saved in swin-tiny-patch4-window7-224-finetuned-eurosat/checkpoint-190/preprocessor_config.json\n",
            "Feature extractor saved in swin-tiny-patch4-window7-224-finetuned-eurosat/preprocessor_config.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2700\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to swin-tiny-patch4-window7-224-finetuned-eurosat/checkpoint-380\n",
            "Configuration saved in swin-tiny-patch4-window7-224-finetuned-eurosat/checkpoint-380/config.json\n",
            "Model weights saved in swin-tiny-patch4-window7-224-finetuned-eurosat/checkpoint-380/pytorch_model.bin\n",
            "Feature extractor saved in swin-tiny-patch4-window7-224-finetuned-eurosat/checkpoint-380/preprocessor_config.json\n",
            "Feature extractor saved in swin-tiny-patch4-window7-224-finetuned-eurosat/preprocessor_config.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2700\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to swin-tiny-patch4-window7-224-finetuned-eurosat/checkpoint-570\n",
            "Configuration saved in swin-tiny-patch4-window7-224-finetuned-eurosat/checkpoint-570/config.json\n",
            "Model weights saved in swin-tiny-patch4-window7-224-finetuned-eurosat/checkpoint-570/pytorch_model.bin\n",
            "Feature extractor saved in swin-tiny-patch4-window7-224-finetuned-eurosat/checkpoint-570/preprocessor_config.json\n",
            "Feature extractor saved in swin-tiny-patch4-window7-224-finetuned-eurosat/preprocessor_config.json\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n",
            "Loading best model from swin-tiny-patch4-window7-224-finetuned-eurosat/checkpoint-570 (score: 0.9744444444444444).\n",
            "Saving model checkpoint to swin-tiny-patch4-window7-224-finetuned-eurosat\n",
            "Configuration saved in swin-tiny-patch4-window7-224-finetuned-eurosat/config.json\n",
            "Model weights saved in swin-tiny-patch4-window7-224-finetuned-eurosat/pytorch_model.bin\n",
            "Feature extractor saved in swin-tiny-patch4-window7-224-finetuned-eurosat/preprocessor_config.json\n",
            "Saving model checkpoint to swin-tiny-patch4-window7-224-finetuned-eurosat\n",
            "Configuration saved in swin-tiny-patch4-window7-224-finetuned-eurosat/config.json\n",
            "Model weights saved in swin-tiny-patch4-window7-224-finetuned-eurosat/pytorch_model.bin\n",
            "Feature extractor saved in swin-tiny-patch4-window7-224-finetuned-eurosat/preprocessor_config.json\n",
            "Several commits (2) will be pushed upstream.\n",
            "The progress bars may be unreliable.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "398c087633254bcaaf781aa57e982dd6",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Upload file pytorch_model.bin:   0%|          | 3.34k/105M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "898560cd6bca49d7bbc530070737e348",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Upload file runs/Apr12_08-48-13_9520b574893c/events.out.tfevents.1649753401.9520b574893c.77.0:  24%|##4       …"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "To https://huggingface.co/nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat\n",
            "   b46a767..6d6b8dc  main -> main\n",
            "\n",
            "To https://huggingface.co/nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat\n",
            "   6d6b8dc..25dd5d7  main -> main\n",
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "***** train metrics *****\n",
            "  epoch                    =          3.0\n",
            "  total_flos               = 1687935228GF\n",
            "  train_loss               =       0.3276\n",
            "  train_runtime            =   0:16:13.91\n",
            "  train_samples_per_second =       74.852\n",
            "  train_steps_per_second   =        0.585\n"
          ]
        }
      ],
      "source": [
        "train_results = trainer.train()\n",
        "# rest is optional but nice to have\n",
        "trainer.save_model()\n",
        "trainer.log_metrics(\"train\", train_results.metrics)\n",
        "trainer.save_metrics(\"train\", train_results.metrics)\n",
        "trainer.save_state()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vyb-58x_-A0e"
      },
      "source": [
        "We can check with the `evaluate` method that our `Trainer` did reload the best model properly (if it was not the last one):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "id": "niniUAnb5IrR",
        "outputId": "1adeaf58-d2a9-41f1-d0fe-4db079f48aa0"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 2700\n",
            "  Batch size = 32\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='85' max='85' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [85/85 00:15]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "***** eval metrics *****\n",
            "  epoch                   =        3.0\n",
            "  eval_accuracy           =     0.9744\n",
            "  eval_loss               =     0.0664\n",
            "  eval_runtime            = 0:00:16.12\n",
            "  eval_samples_per_second =     167.48\n",
            "  eval_steps_per_second   =      5.273\n"
          ]
        }
      ],
      "source": [
        "metrics = trainer.evaluate()\n",
        "# some nice to haves:\n",
        "trainer.log_metrics(\"eval\", metrics)\n",
        "trainer.save_metrics(\"eval\", metrics)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ymwN-SIR-NDF"
      },
      "source": [
        "You can now upload the result of the training to the Hub, just execute this instruction (note that the Trainer will automatically create a model card as well as Tensorboard logs - see the \"Training metrics\" tab - amazing isn't it?):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 192,
          "referenced_widgets": [
            "bdbc14917682409886e9e1571dd525e7",
            "98fd9150cbf045ac9443dcabe99ca22e",
            "dc22cc2ba2a94f56b56672c6ce19041d",
            "a722d5be4ebc42a38ed208047773c3ac",
            "bcd45d9e90d14473a743ce1e41968560",
            "dcb84839bd7f473a81d382e1fa0d69c8",
            "b13db65ff9d04f6d95eb5761d1df9c78",
            "5ca400da4dc0421288dc4788f0dde96c",
            "9a6e68f68baf4d41ae4668f119e1d461",
            "20648d3f57564cf2a30fab10b12af123",
            "aec38c4c448d4b8083232bb50351a188"
          ]
        },
        "id": "4aNMErFz-GzX",
        "outputId": "aaba3412-c92d-4305-8f5b-f7146ada1a59"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Saving model checkpoint to swin-tiny-patch4-window7-224-finetuned-eurosat\n",
            "Configuration saved in swin-tiny-patch4-window7-224-finetuned-eurosat/config.json\n",
            "Model weights saved in swin-tiny-patch4-window7-224-finetuned-eurosat/pytorch_model.bin\n",
            "Feature extractor saved in swin-tiny-patch4-window7-224-finetuned-eurosat/preprocessor_config.json\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bdbc14917682409886e9e1571dd525e7",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Upload file runs/Apr12_08-48-13_9520b574893c/events.out.tfevents.1649754586.9520b574893c.77.2: 100%|##########…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "To https://huggingface.co/nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat\n",
            "   25dd5d7..2164338  main -> main\n",
            "\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'https://huggingface.co/nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat/commit/2164338db59d40004286bc65800bfa50561ecd3d'"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainer.push_to_hub()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cZQnNUsI-Q4S"
      },
      "source": [
        "You can now share this model with all your friends, family, favorite pets: they can all load it with the identifier `\"your-username/the-name-you-picked\"` so for instance:\n",
        "\n",
        "```python\n",
        "from transformers import AutoModelForImageClassification, AutoFeatureExtractor\n",
        "\n",
        "feature_extractor = AutoFeatureExtractor.from_pretrained(\"nielsr/my-awesome-model\")\n",
        "model = AutoModelForImageClassification.from_pretrained(\"nielsr/my-awesome-model\")\n",
        "\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "049gH1wt-Akp"
      },
      "source": [
        "## Inference\n",
        "\n",
        "Let's say you have a new image, on which you'd like to make a prediction. Let's load a satellite image of a forest (that's not part of the EuroSAT dataset), and see how the model does."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "id": "UX6dwmT7GP91",
        "outputId": "1f8be63a-d0d2-4235-fd7d-08fbe6258792"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=P size=850x589 at 0x7FF2E690A650>"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from PIL import Image\n",
        "import requests\n",
        "\n",
        "url = 'https://huggingface.co/nielsr/convnext-tiny-finetuned-eurostat/resolve/main/forest.png'\n",
        "image = Image.open(requests.get(url, stream=True).raw)\n",
        "image"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "91-Ibh1--oI3"
      },
      "source": [
        "We'll load the feature extractor and model from the hub (here, we use the [Auto Classes](https://huggingface.co/docs/transformers/model_doc/auto#transformers.AutoModelForImageClassification), which will make sure the appropriate classes will be loaded automatically based on the `config.json` and `preprocessor_config.json` files of the repo on the hub):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "1d7d48efc83942f7af9d6ee079829cf5",
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            "0ddaf04b08de467db0d05d1310a061b9",
            "9dfc08df9d3c40e2b58b80ded1757877",
            "439e99396ea94ded839bd248fedd4336",
            "9e99ded657134f558d258ab848ec7ed9",
            "970bfe0340214232a20166834b5467a8",
            "6b461c460eb543ef8a095b685163a135",
            "d11b8b3db3584d289194e55b21e349cd",
            "dbf1ac358f1c42f2a3d4ea06855e6f8c",
            "2f352e7c41354fad8ff728812e57d710",
            "ae47ecc705504c8b970eed5631c3ba70",
            "57c389d6ab5b43c2aff368a2ec0eec9d",
            "d58f48db2d1a45f68089517e1d3d03fb",
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            "73b4904f115a45e68370570fe6029158",
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            "d3ce10fbf95c4ebfb4e735e3f3538c3c",
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            "0bb996ac5dc54a65b9c93c090dd236c5",
            "579bc0c4df494b68b0a9dd37c9b5faf4",
            "8c254fe1a46740e49a6bffc3cbe0713a"
          ]
        },
        "id": "xzwvix8X-st3",
        "outputId": "fbfda8b3-0394-4dd9-e96f-5dd886775b69"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "https://huggingface.co/nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat/resolve/main/preprocessor_config.json not found in cache or force_download set to True, downloading to /root/.cache/huggingface/transformers/tmpqggthctf\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1d7d48efc83942f7af9d6ee079829cf5",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/240 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "storing https://huggingface.co/nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat/resolve/main/preprocessor_config.json in cache at /root/.cache/huggingface/transformers/7b742d61fc51f2ef5f81a75f80b26419c9f5bd86cc3022ed5784d09823f219f2.e34548f8325ec440fcf4990d4a8dbbfd665397400e9a700766de032d2b45cf6b\n",
            "creating metadata file for /root/.cache/huggingface/transformers/7b742d61fc51f2ef5f81a75f80b26419c9f5bd86cc3022ed5784d09823f219f2.e34548f8325ec440fcf4990d4a8dbbfd665397400e9a700766de032d2b45cf6b\n",
            "loading feature extractor configuration file https://huggingface.co/nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat/resolve/main/preprocessor_config.json from cache at /root/.cache/huggingface/transformers/7b742d61fc51f2ef5f81a75f80b26419c9f5bd86cc3022ed5784d09823f219f2.e34548f8325ec440fcf4990d4a8dbbfd665397400e9a700766de032d2b45cf6b\n",
            "Feature extractor ViTFeatureExtractor {\n",
            "  \"do_normalize\": true,\n",
            "  \"do_resize\": true,\n",
            "  \"feature_extractor_type\": \"ViTFeatureExtractor\",\n",
            "  \"image_mean\": [\n",
            "    0.485,\n",
            "    0.456,\n",
            "    0.406\n",
            "  ],\n",
            "  \"image_std\": [\n",
            "    0.229,\n",
            "    0.224,\n",
            "    0.225\n",
            "  ],\n",
            "  \"resample\": 3,\n",
            "  \"size\": 224\n",
            "}\n",
            "\n",
            "https://huggingface.co/nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat/resolve/main/config.json not found in cache or force_download set to True, downloading to /root/.cache/huggingface/transformers/tmpzdd89w3g\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2f352e7c41354fad8ff728812e57d710",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.24k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "storing https://huggingface.co/nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat/resolve/main/config.json in cache at /root/.cache/huggingface/transformers/83e4a1dea85e8e284e4da8ae1e3cf950c2c7e74d65a5a188049b3371fcd151bd.f1ed4852dd8f4c3d0c565427607bc41fff51b58ac73a0970bec8456e5c64cea0\n",
            "creating metadata file for /root/.cache/huggingface/transformers/83e4a1dea85e8e284e4da8ae1e3cf950c2c7e74d65a5a188049b3371fcd151bd.f1ed4852dd8f4c3d0c565427607bc41fff51b58ac73a0970bec8456e5c64cea0\n",
            "loading configuration file https://huggingface.co/nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/83e4a1dea85e8e284e4da8ae1e3cf950c2c7e74d65a5a188049b3371fcd151bd.f1ed4852dd8f4c3d0c565427607bc41fff51b58ac73a0970bec8456e5c64cea0\n",
            "Model config SwinConfig {\n",
            "  \"_name_or_path\": \"nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat\",\n",
            "  \"architectures\": [\n",
            "    \"SwinForImageClassification\"\n",
            "  ],\n",
            "  \"attention_probs_dropout_prob\": 0.0,\n",
            "  \"depths\": [\n",
            "    2,\n",
            "    2,\n",
            "    6,\n",
            "    2\n",
            "  ],\n",
            "  \"drop_path_rate\": 0.1,\n",
            "  \"embed_dim\": 96,\n",
            "  \"encoder_stride\": 32,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.0,\n",
            "  \"hidden_size\": 768,\n",
            "  \"id2label\": {\n",
            "    \"0\": \"AnnualCrop\",\n",
            "    \"1\": \"Forest\",\n",
            "    \"2\": \"HerbaceousVegetation\",\n",
            "    \"3\": \"Highway\",\n",
            "    \"4\": \"Industrial\",\n",
            "    \"5\": \"Pasture\",\n",
            "    \"6\": \"PermanentCrop\",\n",
            "    \"7\": \"Residential\",\n",
            "    \"8\": \"River\",\n",
            "    \"9\": \"SeaLake\"\n",
            "  },\n",
            "  \"image_size\": 224,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"label2id\": {\n",
            "    \"AnnualCrop\": 0,\n",
            "    \"Forest\": 1,\n",
            "    \"HerbaceousVegetation\": 2,\n",
            "    \"Highway\": 3,\n",
            "    \"Industrial\": 4,\n",
            "    \"Pasture\": 5,\n",
            "    \"PermanentCrop\": 6,\n",
            "    \"Residential\": 7,\n",
            "    \"River\": 8,\n",
            "    \"SeaLake\": 9\n",
            "  },\n",
            "  \"layer_norm_eps\": 1e-05,\n",
            "  \"mlp_ratio\": 4.0,\n",
            "  \"model_type\": \"swin\",\n",
            "  \"num_channels\": 3,\n",
            "  \"num_heads\": [\n",
            "    3,\n",
            "    6,\n",
            "    12,\n",
            "    24\n",
            "  ],\n",
            "  \"num_layers\": 4,\n",
            "  \"patch_size\": 4,\n",
            "  \"path_norm\": true,\n",
            "  \"problem_type\": \"single_label_classification\",\n",
            "  \"qkv_bias\": true,\n",
            "  \"torch_dtype\": \"float32\",\n",
            "  \"transformers_version\": \"4.18.0\",\n",
            "  \"use_absolute_embeddings\": false,\n",
            "  \"window_size\": 7\n",
            "}\n",
            "\n",
            "https://huggingface.co/nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat/resolve/main/pytorch_model.bin not found in cache or force_download set to True, downloading to /root/.cache/huggingface/transformers/tmpkh0vdu53\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d3ce10fbf95c4ebfb4e735e3f3538c3c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/105M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "storing https://huggingface.co/nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat/resolve/main/pytorch_model.bin in cache at /root/.cache/huggingface/transformers/3daadbe0cabef18dc0e2232ae080d135a9d4ee6b1dc7675725ef38bedb990b81.818e63819e125637bd8a94f43b6899d1552f0b45884f1c28c458a5cb55dfa9e5\n",
            "creating metadata file for /root/.cache/huggingface/transformers/3daadbe0cabef18dc0e2232ae080d135a9d4ee6b1dc7675725ef38bedb990b81.818e63819e125637bd8a94f43b6899d1552f0b45884f1c28c458a5cb55dfa9e5\n",
            "loading weights file https://huggingface.co/nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat/resolve/main/pytorch_model.bin from cache at /root/.cache/huggingface/transformers/3daadbe0cabef18dc0e2232ae080d135a9d4ee6b1dc7675725ef38bedb990b81.818e63819e125637bd8a94f43b6899d1552f0b45884f1c28c458a5cb55dfa9e5\n",
            "All model checkpoint weights were used when initializing SwinForImageClassification.\n",
            "\n",
            "All the weights of SwinForImageClassification were initialized from the model checkpoint at nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use SwinForImageClassification for predictions without further training.\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoModelForImageClassification, AutoFeatureExtractor\n",
        "\n",
        "repo_name = \"nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat\"\n",
        "\n",
        "feature_extractor = AutoFeatureExtractor.from_pretrained(repo_name)\n",
        "model = AutoModelForImageClassification.from_pretrained(repo_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "298o50gr-Rwt",
        "outputId": "b5f46085-b7c8-4f6b-970e-69a6ad5a9b2d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "torch.Size([1, 3, 224, 224])\n"
          ]
        }
      ],
      "source": [
        "# prepare image for the model\n",
        "encoding = feature_extractor(image.convert(\"RGB\"), return_tensors=\"pt\")\n",
        "print(encoding.pixel_values.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "33E44G86_RtL"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "\n",
        "# forward pass\n",
        "with torch.no_grad():\n",
        "  outputs = model(**encoding)\n",
        "  logits = outputs.logits"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4ctUvqfs_Yyn",
        "outputId": "74540ee4-c1af-4147-f221-15e213b5a7e8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Predicted class: Forest\n"
          ]
        }
      ],
      "source": [
        "predicted_class_idx = logits.argmax(-1).item()\n",
        "print(\"Predicted class:\", model.config.id2label[predicted_class_idx])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N3yJFIIP_k01"
      },
      "source": [
        "Looks like our model got it correct!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-2A5W8dF_qYt"
      },
      "source": [
        "## Pipeline API\n",
        "\n",
        "An alternative way to quickly perform inference with any model on the hub is by leveraging the [Pipeline API](https://huggingface.co/docs/transformers/main_classes/pipelines), which abstracts away all the steps we did manually above for us. It will perform the preprocessing, forward pass and postprocessing all in a single object.\n",
        "\n",
        "Let's showcase this for our trained model:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I7mz7QTo_jWa",
        "outputId": "7ac34120-d5ab-4d28-e4dd-c47dff95520f"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "loading configuration file https://huggingface.co/nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/83e4a1dea85e8e284e4da8ae1e3cf950c2c7e74d65a5a188049b3371fcd151bd.f1ed4852dd8f4c3d0c565427607bc41fff51b58ac73a0970bec8456e5c64cea0\n",
            "Model config SwinConfig {\n",
            "  \"_name_or_path\": \"nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat\",\n",
            "  \"architectures\": [\n",
            "    \"SwinForImageClassification\"\n",
            "  ],\n",
            "  \"attention_probs_dropout_prob\": 0.0,\n",
            "  \"depths\": [\n",
            "    2,\n",
            "    2,\n",
            "    6,\n",
            "    2\n",
            "  ],\n",
            "  \"drop_path_rate\": 0.1,\n",
            "  \"embed_dim\": 96,\n",
            "  \"encoder_stride\": 32,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.0,\n",
            "  \"hidden_size\": 768,\n",
            "  \"id2label\": {\n",
            "    \"0\": \"AnnualCrop\",\n",
            "    \"1\": \"Forest\",\n",
            "    \"2\": \"HerbaceousVegetation\",\n",
            "    \"3\": \"Highway\",\n",
            "    \"4\": \"Industrial\",\n",
            "    \"5\": \"Pasture\",\n",
            "    \"6\": \"PermanentCrop\",\n",
            "    \"7\": \"Residential\",\n",
            "    \"8\": \"River\",\n",
            "    \"9\": \"SeaLake\"\n",
            "  },\n",
            "  \"image_size\": 224,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"label2id\": {\n",
            "    \"AnnualCrop\": 0,\n",
            "    \"Forest\": 1,\n",
            "    \"HerbaceousVegetation\": 2,\n",
            "    \"Highway\": 3,\n",
            "    \"Industrial\": 4,\n",
            "    \"Pasture\": 5,\n",
            "    \"PermanentCrop\": 6,\n",
            "    \"Residential\": 7,\n",
            "    \"River\": 8,\n",
            "    \"SeaLake\": 9\n",
            "  },\n",
            "  \"layer_norm_eps\": 1e-05,\n",
            "  \"mlp_ratio\": 4.0,\n",
            "  \"model_type\": \"swin\",\n",
            "  \"num_channels\": 3,\n",
            "  \"num_heads\": [\n",
            "    3,\n",
            "    6,\n",
            "    12,\n",
            "    24\n",
            "  ],\n",
            "  \"num_layers\": 4,\n",
            "  \"patch_size\": 4,\n",
            "  \"path_norm\": true,\n",
            "  \"problem_type\": \"single_label_classification\",\n",
            "  \"qkv_bias\": true,\n",
            "  \"torch_dtype\": \"float32\",\n",
            "  \"transformers_version\": \"4.18.0\",\n",
            "  \"use_absolute_embeddings\": false,\n",
            "  \"window_size\": 7\n",
            "}\n",
            "\n",
            "loading configuration file https://huggingface.co/nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/83e4a1dea85e8e284e4da8ae1e3cf950c2c7e74d65a5a188049b3371fcd151bd.f1ed4852dd8f4c3d0c565427607bc41fff51b58ac73a0970bec8456e5c64cea0\n",
            "Model config SwinConfig {\n",
            "  \"_name_or_path\": \"nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat\",\n",
            "  \"architectures\": [\n",
            "    \"SwinForImageClassification\"\n",
            "  ],\n",
            "  \"attention_probs_dropout_prob\": 0.0,\n",
            "  \"depths\": [\n",
            "    2,\n",
            "    2,\n",
            "    6,\n",
            "    2\n",
            "  ],\n",
            "  \"drop_path_rate\": 0.1,\n",
            "  \"embed_dim\": 96,\n",
            "  \"encoder_stride\": 32,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.0,\n",
            "  \"hidden_size\": 768,\n",
            "  \"id2label\": {\n",
            "    \"0\": \"AnnualCrop\",\n",
            "    \"1\": \"Forest\",\n",
            "    \"2\": \"HerbaceousVegetation\",\n",
            "    \"3\": \"Highway\",\n",
            "    \"4\": \"Industrial\",\n",
            "    \"5\": \"Pasture\",\n",
            "    \"6\": \"PermanentCrop\",\n",
            "    \"7\": \"Residential\",\n",
            "    \"8\": \"River\",\n",
            "    \"9\": \"SeaLake\"\n",
            "  },\n",
            "  \"image_size\": 224,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"label2id\": {\n",
            "    \"AnnualCrop\": 0,\n",
            "    \"Forest\": 1,\n",
            "    \"HerbaceousVegetation\": 2,\n",
            "    \"Highway\": 3,\n",
            "    \"Industrial\": 4,\n",
            "    \"Pasture\": 5,\n",
            "    \"PermanentCrop\": 6,\n",
            "    \"Residential\": 7,\n",
            "    \"River\": 8,\n",
            "    \"SeaLake\": 9\n",
            "  },\n",
            "  \"layer_norm_eps\": 1e-05,\n",
            "  \"mlp_ratio\": 4.0,\n",
            "  \"model_type\": \"swin\",\n",
            "  \"num_channels\": 3,\n",
            "  \"num_heads\": [\n",
            "    3,\n",
            "    6,\n",
            "    12,\n",
            "    24\n",
            "  ],\n",
            "  \"num_layers\": 4,\n",
            "  \"patch_size\": 4,\n",
            "  \"path_norm\": true,\n",
            "  \"problem_type\": \"single_label_classification\",\n",
            "  \"qkv_bias\": true,\n",
            "  \"torch_dtype\": \"float32\",\n",
            "  \"transformers_version\": \"4.18.0\",\n",
            "  \"use_absolute_embeddings\": false,\n",
            "  \"window_size\": 7\n",
            "}\n",
            "\n",
            "loading weights file https://huggingface.co/nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat/resolve/main/pytorch_model.bin from cache at /root/.cache/huggingface/transformers/3daadbe0cabef18dc0e2232ae080d135a9d4ee6b1dc7675725ef38bedb990b81.818e63819e125637bd8a94f43b6899d1552f0b45884f1c28c458a5cb55dfa9e5\n",
            "All model checkpoint weights were used when initializing SwinForImageClassification.\n",
            "\n",
            "All the weights of SwinForImageClassification were initialized from the model checkpoint at nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use SwinForImageClassification for predictions without further training.\n",
            "loading feature extractor configuration file https://huggingface.co/nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat/resolve/main/preprocessor_config.json from cache at /root/.cache/huggingface/transformers/7b742d61fc51f2ef5f81a75f80b26419c9f5bd86cc3022ed5784d09823f219f2.e34548f8325ec440fcf4990d4a8dbbfd665397400e9a700766de032d2b45cf6b\n",
            "Feature extractor ViTFeatureExtractor {\n",
            "  \"do_normalize\": true,\n",
            "  \"do_resize\": true,\n",
            "  \"feature_extractor_type\": \"ViTFeatureExtractor\",\n",
            "  \"image_mean\": [\n",
            "    0.485,\n",
            "    0.456,\n",
            "    0.406\n",
            "  ],\n",
            "  \"image_std\": [\n",
            "    0.229,\n",
            "    0.224,\n",
            "    0.225\n",
            "  ],\n",
            "  \"resample\": 3,\n",
            "  \"size\": 224\n",
            "}\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "pipe = pipeline(\"image-classification\", \"nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fPiuLDx3_9SY",
        "outputId": "eacd1210-9a4c-4130-c29e-6d6152d62f34"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[{'label': 'Forest', 'score': 0.7000269889831543},\n",
              " {'label': 'HerbaceousVegetation', 'score': 0.14589950442314148},\n",
              " {'label': 'Pasture', 'score': 0.10370415449142456},\n",
              " {'label': 'Highway', 'score': 0.014327816665172577},\n",
              " {'label': 'Residential', 'score': 0.0139168007299304}]"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pipe(image)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BVXM6-g4AJmy"
      },
      "source": [
        "As we can see, it does not only show the class label with the highest probability, but does return the top 5 labels, with their corresponding scores. Note that the pipelines also work with local models and feature extractors:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "B8kmO1NMAAXs"
      },
      "outputs": [],
      "source": [
        "pipe = pipeline(\"image-classification\",\n",
        "                model=model,\n",
        "                feature_extractor=feature_extractor)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        },
        "id": "NfFH9eLMAdCX",
        "outputId": "893edf83-1889-488c-9c97-8e0a27fadd3f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[{'label': 'Forest', 'score': 0.7000269889831543},\n",
              " {'label': 'HerbaceousVegetation', 'score': 0.14589950442314148},\n",
              " {'label': 'Pasture', 'score': 0.10370415449142456},\n",
              " {'label': 'Highway', 'score': 0.014327816665172577},\n",
              " {'label': 'Residential', 'score': 0.0139168007299304}]"
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
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        "pipe(image)"
      ]
    },
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