{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "NJZUsvUMhFtU", "pycharm": { "name": "#%% md\n" } }, "source": [ "# **Fine-tuning for Image Classification with 🤗 Transformers**\n", "\n", "This notebook shows how to fine-tune any pretrained Vision model for Image Classification on a custom dataset. The idea is to add a randomly initialized classification head on top of a pre-trained encoder, and fine-tune the model altogether on a labeled dataset.\n", "\n", "## ImageFolder\n", "\n", "This notebook leverages the [ImageFolder](https://huggingface.co/docs/datasets/v2.0.0/en/image_process#imagefolder) feature to easily run the notebook on a custom dataset (namely, [EuroSAT](https://github.com/phelber/EuroSAT) in this tutorial). You can either load a `Dataset` from local folders or from local/remote files, like zip or tar.\n", "\n", "## Any model\n", "\n", "This notebook is built to run on any image classification dataset with any vision model checkpoint from the [Model Hub](https://huggingface.co/) as long as that model has a version with a Image Classification head, such as:\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", "- in short, any model supported by [AutoModelForImageClassification](https://huggingface.co/docs/transformers/model_doc/auto#transformers.AutoModelForImageClassification).\n", "\n", "## Data augmentation\n", "\n", "This notebook leverages Kornia's [image augmentations](https://kornia.readthedocs.io/en/latest/augmentation.module.html) for applying data augmentation - note that we do provide alternative notebooks which leverage other libraries, including:\n", "\n", "* [Torchvision](https://github.com/huggingface/notebooks/blob/main/examples/image_classification.ipynb)\n", "* [Albumentations](https://github.com/huggingface/notebooks/blob/main/examples/image_classification_albumentations.ipynb)\n", "* [imgaug](https://github.com/huggingface/notebooks/blob/main/examples/image_classification_imgaug.ipynb). \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.\n", "\n", "In this notebook, we'll fine-tune from the https://huggingface.co/microsoft/swin-tiny-patch4-window7-224 checkpoint, but note that there are many, many more available on the [hub](https://huggingface.co/models?other=vision). We will also use the [datasets](https://huggingface.co/docs/datasets/installation) library to load an image dataset" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "wvLDfqzdhFtb", "pycharm": { "name": "#%%\n" } }, "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": "WOynCHJWhFtc", "pycharm": { "name": "#%% md\n" } }, "source": [ "Before we start, let's install the `kornia`, `datasets` and `transformers` libraries. We'll install `evaluate` to evaluate our model's accuracy during and after training, which requires `sklearn`. Since we'll be working with images, we'll also ensure that `Pillow` is installed." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "krONvnn0hFtd", "outputId": "67318c2f-65f1-4fde-be66-4c6f66cd33c5", "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[K |████████████████████████████████| 565 kB 34.8 MB/s \n", "\u001b[K |████████████████████████████████| 365 kB 71.0 MB/s \n", "\u001b[K |████████████████████████████████| 4.7 MB 61.0 MB/s \n", "\u001b[K |████████████████████████████████| 69 kB 8.6 MB/s \n", "\u001b[K |████████████████████████████████| 120 kB 72.6 MB/s \n", "\u001b[K |████████████████████████████████| 115 kB 54.8 MB/s \n", "\u001b[K |████████████████████████████████| 212 kB 31.8 MB/s \n", "\u001b[K |████████████████████████████████| 127 kB 51.6 MB/s \n", "\u001b[K |████████████████████████████████| 6.6 MB 61.2 MB/s \n", "\u001b[?25h Building wheel for sklearn (setup.py) ... \u001b[?25l\u001b[?25hdone\n" ] } ], "source": [ "!pip install -q kornia datasets transformers evaluate sklearn Pillow" ] }, { "cell_type": "markdown", "metadata": { "id": "dEPOo0jnhFtd", "pycharm": { "name": "#%% md\n" } }, "source": [ "If you're opening this notebook locally, make sure your environment has an install from the last version of those libraries." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We also quickly upload some telemetry - this tells us which examples and software versions are getting used so we know where to prioritize our maintenance efforts. We don't collect (or care about) any personally identifiable information, but if you'd prefer not to be counted, feel free to skip this step or delete this cell entirely." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from transformers.utils import send_example_telemetry\n", "\n", "send_example_telemetry(\"image_classification_kornia_notebook\", framework=\"pytorch\")" ] }, { "cell_type": "markdown", "metadata": { "id": "Km4rEvjJhFtd", "pycharm": { "name": "#%% md\n" } }, "source": [ "## Fine-tuning a model on an image classification task" ] }, { "cell_type": "markdown", "metadata": { "id": "NknH0OJFhFte", "pycharm": { "name": "#%% md\n" } }, "source": [ "In this notebook, we will see how to fine-tune one of the [🤗 Transformers](https://github.com/huggingface/transformers) vision models on an Image Classification dataset.\n", "\n", "Given an image, the goal is to predict an appropriate class for it, like \"tiger\". The screenshot below is taken from a [ViT fine-tuned on ImageNet-1k](https://huggingface.co/google/vit-base-patch16-224) - try out the inference widget!" ] }, { "cell_type": "markdown", "metadata": { "id": "XtNzED6hhFtf", "pycharm": { "name": "#%% md\n" } }, "source": [ "\"drawing\"\n" ] }, { "cell_type": "markdown", "metadata": { "id": "zMOBzRmOhFtf", "pycharm": { "name": "#%% md\n" } }, "source": [ "### Loading the dataset" ] }, { "cell_type": "markdown", "metadata": { "id": "Sin4A8CwhFtf", "pycharm": { "name": "#%% md\n" } }, "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": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 248, "referenced_widgets": [ "7058d1e258de40ebad13ff790915cd3c", "01ea35ba78b440d392b2c5e75ea74464", "e2e16d974fb249dd94a11507f9224a2d", "58f000f788c14a268cc102d5237b8c04", "377190442ab84770b7e3d27199ac8563", "30a8bd848ee1497691b7605d4dd32115", "c1537b4150ab4cfca215d70f6095a110", "aca7889b9fd34829bc7c7ed72c467aa1", "32e6e84f4de54b26a7218bf720324752", "f6693950a02d4f9d933c2e32e323f521", "adaaa2e702174b2b87894c0d3fcb1a57", "4c4fb79433cf49faac5105c5403f4bc8", "e19aa06a93034787a1a422c8297c50cf", "45cc5f862ebc482d9186d3d4349cf602", "c43a2cf9f9ee42cb8a114ced6ecce524", "f1ab9e0ecd59406b825926098912b968", "fb8dda387d89412388702e248a9514df", "d37300abc9474836803687ab739be99d", "8926f783fec742ef954ceb979f5abc70", "c81f799609794db09cb4f5d19c39333c", "78fa30c22d474c3da827b6af042b1e7d", "ffaa450ad4e24d66a466ab53b424260f", "17ad910846c647eea2967a556fa5d636", "d19adaf170c1447f90538b85b2546a7a", "b850715786e741fab64246dd616e4b0b", "775c74c822b94d55897501502b8b3890", "992069d6fbdd4046b6aa734b39a23ed5", "4ba73b8913d8440ea8ddc4c0e08ed145", "f5ded311e5ad49a4b7f77c5da177ffb9", "3653830200414a2b9a5150eea235fcf1", "2b479dce1b3d427483504e57fe4acd7a", "1aa408392e434cde9bdc43e0f670617d", "0a6cef96ab1841068e9027a30e76ed1e", "c6108fb0bf124c3fa22f85bbd4bf9266", "c3d49b63b8204a04ab075188f49b0d93", "603016ca5f7f4e939e5a7f1f57b8dd33", "a73b73fbcc164a81a3a4912f9682ab00", "f05a4a836fb84bf7a5f2e75440bb026d", "6da041a6484b4d1cbfa58cfcd265f88e", "d9c9f4a8aca44e09a8136331e545ea88", "f22ca12d18354eec80befa03d312fc44", "c8eb2750eb2244699dc1492e658a9ccb", "6f52fb52f05d465090227ede5cf2a30c", "4ffe6ca0522542f2a96cd3191d6a99c6", "825be3a2828c46398b79e6279c45236a", "e32780c830504c508b85e67343ab88e9", "0fd3d466727b4ac3bdbe1b32c67b0c26", "8c70d4279bac4e1e9efdc3d3b9d45e34", "a5dabc9865f34c9089b0eeca3897e467", "f0fc57814beb464aa5c0fa560f3fa8a6", "1453dc44aa334c7097608220933e46c2", "432c7d96cdc840d6b54567474d09ab2f", "72adfd5afe784624a673284e17a813c8", "c07bfe12d2c04a0f9c5696797a1d67a5", "fbb13fc905f240dab44f438f11421f89", "9c22864ff042460584353d870e79b5fb", "2ceb2fa333a94f5fb5207b5303bf0105", "3b13fa319738413892aa0175b8d2c79a", "bd3fb4774f2544abbfe84a8f77297022", "e161abad994748e7be3655cffa4f7468", "1aed4493a06e459193d06161e5dfe09e", "36d59e2f979449fa8e27c58f44c1f801", "c243a77c77eb4e51b217b0513c2d8546", "2c21a4a4464343729719d8f3125a7535", "dfae0ee344db4147a758e6b50daa66c8", "7cfa331622ff4187a167c4a981491457" ] }, "id": "6rfbAPGehFtf", "outputId": "402e4107-7880-4174-b75e-09245376cad1", "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING:datasets.builder:Using custom data configuration default-0537267e6f812d56\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Downloading and preparing dataset imagefolder/default to /root/.cache/huggingface/datasets/imagefolder/default-0537267e6f812d56/0.0.0/0fc50c79b681877cc46b23245a6ef5333d036f48db40d53765a68034bc48faff...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "7058d1e258de40ebad13ff790915cd3c", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading data files: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4c4fb79433cf49faac5105c5403f4bc8", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading data files: 0%| | 0/1 [00:00,\n", " 'label': 6}" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "example = dataset[\"train\"][10]\n", "example" ] }, { "cell_type": "markdown", "metadata": { "id": "z2kp7LEMhFti", "pycharm": { "name": "#%% md\n" } }, "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": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "591Jg2hAhFtj", "outputId": "0e1ad54e-beb5-4c64-af45-8f578074fa4c", "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/plain": [ "{'image': Image(decode=True, id=None),\n", " 'label': ClassLabel(num_classes=10, names=['AnnualCrop', 'Forest', 'HerbaceousVegetation', 'Highway', 'Industrial', 'Pasture', 'PermanentCrop', 'Residential', 'River', 'SeaLake'], id=None)}" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset[\"train\"].features" ] }, { "cell_type": "markdown", "metadata": { "id": "uGJf6VXihFtj", "pycharm": { "name": "#%% md\n" } }, "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": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81 }, "id": "TZnB2NiHhFtj", "outputId": "ed180e6e-9c03-433d-b61f-94925965c0cc", "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "example['image']" ] }, { "cell_type": "markdown", "metadata": { "id": "1X9_obDDhFtj", "pycharm": { "name": "#%% md\n" } }, "source": [ "Let's make it a little bigger as the images in the EuroSAT dataset are of low resolution (64x64 pixels):" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 217 }, "id": "-_DqwhyLhFtj", "outputId": "532fe0ab-e681-4743-c0d3-ce4d91abbaf2", "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "example['image'].resize((200, 200))" ] }, { "cell_type": "markdown", "metadata": { "id": "ReZsyX1HhFtk", "pycharm": { "name": "#%% md\n" } }, "source": [ "Let's print the corresponding label:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "DAKkVypyhFtk", "outputId": "dc8847e6-e181-405e-a8b4-450f227c4894", "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/plain": [ "6" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "example['label']" ] }, { "cell_type": "markdown", "metadata": { "id": "TYcebJ9thFtk", "pycharm": { "name": "#%% md\n" } }, "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": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "n-nFQ_LAhFtk", "outputId": "bb172e98-aa01-4548-d597-aefed4f8622d", "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/plain": [ "ClassLabel(num_classes=10, names=['AnnualCrop', 'Forest', 'HerbaceousVegetation', 'Highway', 'Industrial', 'Pasture', 'PermanentCrop', 'Residential', 'River', 'SeaLake'], id=None)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset[\"train\"].features[\"label\"]" ] }, { "cell_type": "markdown", "metadata": { "id": "9rDFBVD_hFtk", "pycharm": { "name": "#%% md\n" } }, "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": 12, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 37 }, "id": "_JF2bw65hFtl", "outputId": "f5c5f6b3-0b87-456c-9ec9-0f85dedd1185", "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'HerbaceousVegetation'" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "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[2]" ] }, { "cell_type": "markdown", "metadata": { "id": "IBseqwgnhFtl", "pycharm": { "name": "#%% md\n" } }, "source": [ "### Sharing your model" ] }, { "cell_type": "markdown", "metadata": { "id": "i6QkMriphFtl", "pycharm": { "name": "#%% md\n" } }, "source": [ "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": 13, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 300, "referenced_widgets": [ "9a3fd883fbcd4192972daee5e9b724e8", "e4e975ccdf71499e9e7e2628b4d12381", "2263ea69fe2748e59e241121c0ee85db", "ce8ff720c1194f9fa576fa0ab72b9434", "a245a58b01304077b758257436e69b1a", "92f5865c94ce4986a36d71e2cf1db91e", "aa59cf09e9bf4aba951ab61b47c1ed40", "ff6552e277994529b20848de6ad45b99", "6e51c6d618164aeda48942aa1850ecf0", "2fa7bbee9cee40479fd62976954dabe9", "bb520a5722484f39bb93face96e36d69", "907b655aeed94f1c97fb564525e04d01", "013ca1d8272343a2a50a8bc49c22a76b", "98245a2bb52d42369036d025ad91efdd" ] }, "id": "kdSJmmmxhFtm", "outputId": "0d4a1991-cd56-4caf-d516-f8479a8f6238", "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9a3fd883fbcd4192972daee5e9b724e8", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value='
None:\n", " super().__init__()\n", " \n", " @torch.no_grad() # disable gradients for effiency\n", " def forward(self, x: Image) -> torch.Tensor:\n", " x_tmp: np.ndarray = np.array(x) # HxWxC\n", " x_out: torch.Tensor = K.image_to_tensor(x_tmp, keepdim=True) # CxHxW\n", " return x_out.float() / 255.0\n", "\n", "train_transforms = nn.Sequential(\n", " PreProcess(),\n", " K.augmentation.Resize(size=224, side=\"short\"),\n", " K.augmentation.CenterCrop(size=224),\n", " K.augmentation.RandomHorizontalFlip(p=0.5),\n", " K.augmentation.ColorJiggle(),\n", " K.augmentation.Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std),\n", ")\n", "\n", "val_transforms = nn.Sequential(\n", " PreProcess(),\n", " K.augmentation.Resize(size=224, side=\"short\"),\n", " K.augmentation.CenterCrop(size=224),\n", " K.augmentation.Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std),\n", ")\n", "\n", "def preprocess_train(example_batch):\n", " \"\"\"Apply train_transforms across a batch.\"\"\"\n", " example_batch[\"pixel_values\"] = [train_transforms(image).squeeze() for image in example_batch[\"image\"]]\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).squeeze() for image in example_batch[\"image\"]]\n", " return example_batch" ] }, { "cell_type": "markdown", "metadata": { "id": "RF4O0KFBGXir", "pycharm": { "name": "#%% md\n" } }, "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": 17, "metadata": { "id": "P13tqfFTZ_F4", "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "# split up training into training + validation\n", "splits = dataset[\"train\"].train_test_split(test_size=0.1)\n", "train_ds = splits['train']\n", "val_ds = splits['test']" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "id": "TUs56-mprQi1", "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "train_ds.set_transform(preprocess_train)\n", "val_ds.set_transform(preprocess_val)" ] }, { "cell_type": "markdown", "metadata": { "id": "MMw_wQS58a7o", "pycharm": { "name": "#%% md\n" } }, "source": [ "Let's access an element to see that we've added a \"pixel_values\" feature:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Ng9TAlDV8d7r", "outputId": "045cb11f-d5e6-4552-8ef9-918f867b828f", "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/plain": [ "{'image': ,\n", " 'label': 0,\n", " 'pixel_values': tensor([[[ 0.8447, 0.8447, 0.8447, ..., 0.6682, 0.6537, 0.6392],\n", " [ 0.8495, 0.8495, 0.8495, ..., 0.6745, 0.6641, 0.6537],\n", " [ 0.8544, 0.8544, 0.8544, ..., 0.6808, 0.6745, 0.6682],\n", " ...,\n", " [-0.0205, -0.0177, -0.0150, ..., 1.1044, 1.2001, 1.2958],\n", " [ 0.0182, 0.0196, 0.0210, ..., 1.0744, 1.1536, 1.2329],\n", " [ 0.0569, 0.0569, 0.0569, ..., 1.0443, 1.1072, 1.1700]],\n", " \n", " [[ 0.5903, 0.5903, 0.5903, ..., 0.0400, 0.0351, 0.0301],\n", " [ 0.5903, 0.5889, 0.5875, ..., 0.0357, 0.0280, 0.0202],\n", " [ 0.5903, 0.5875, 0.5847, ..., 0.0314, 0.0209, 0.0103],\n", " ...,\n", " [-0.2317, -0.2367, -0.2416, ..., 0.2561, 0.3722, 0.4883],\n", " [-0.1971, -0.2020, -0.2070, ..., 0.2090, 0.3041, 0.3993],\n", " [-0.1625, -0.1674, -0.1724, ..., 0.1618, 0.2360, 0.3102]],\n", " \n", " [[ 0.6705, 0.6705, 0.6705, ..., 0.1575, 0.1525, 0.1476],\n", " [ 0.6607, 0.6634, 0.6662, ..., 0.1575, 0.1525, 0.1476],\n", " [ 0.6508, 0.6564, 0.6619, ..., 0.1575, 0.1525, 0.1476],\n", " ...,\n", " [-0.1009, -0.0988, -0.0966, ..., 0.2670, 0.3770, 0.4871],\n", " [-0.0812, -0.0777, -0.0742, ..., 0.2293, 0.3213, 0.4132],\n", " [-0.0615, -0.0566, -0.0517, ..., 0.1916, 0.2655, 0.3393]]])}" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_ds[0]" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "IYZhy_zOswNE", "outputId": "ea2ad8ad-6144-4e25-82bc-b390f82c7599", "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/plain": [ "torch.Size([3, 224, 224])" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_ds[0]['pixel_values'].shape" ] }, { "cell_type": "markdown", "metadata": { "id": "HOXmyPQ76Qv9", "pycharm": { "name": "#%% md\n" } }, "source": [ "### Training the model" ] }, { "cell_type": "markdown", "metadata": { "id": "0a-2YT7O6ayC", "pycharm": { "name": "#%% md\n" } }, "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": 21, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 203, "referenced_widgets": [ "9e1895e9f61f435682c89d3262ea441c", "20c3f774d6d9484e8f1c948f7ca5e046", "c221cfb9f0274eaebe5bd732f3c6b148", "8ffd71d3f46947ba831098300ab5fb94", "a544c18d3b994b14aa4557c718088d4b", "10ea68a58d754fe6bdeff82dc343b52b", "9ffcf926bbf04f82a716ad2ed3a8b6d1", "c0791510a0e74674a5dc5b46b9c2c87e", "b20d698d71e8495c9499b836a1559f3a", "1db4970b87b348148a72525014e2d4dc", "6abd984a1ae442df8d0b43277620fd67", "56d6a39887064be39b64c8c21ce31581", "3bca40ad7c1846348a696ef66f350da7", "3bea54844d55496bab8de6e0e553243a", "dd59d93d59fc4710867bfd69ccae5e6d", "d6d134de78c14314b7627ba0f3b6891e", "268807b9219e4dae9f9073b37d9487fa", "90267331af2d4d91bfd703557e3ff6d4", "257a1e82772c4cfd90c0daef86eed031", "ff9e39b94d064b8d96f1b338c8e9123f", "5b12316940c948f7a443537d7f7d3dcd", "3a237d1e47cb4432aa3bdaeac2c586d1" ] }, "id": "X9DDujL0q1ac", "outputId": "844dbe9d-b72b-4b8e-d514-3c2deeccd593", "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9e1895e9f61f435682c89d3262ea441c", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading config.json: 0%| | 0.00/70.1k [00:00\n", " \n", " \n", " [570/570 29:36, Epoch 3/3]\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
EpochTraining LossValidation LossAccuracy
10.0859000.0968750.968519
20.0664000.0627250.981481
30.0359000.0540410.982963

" ], "text/plain": [ "" ] }, "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-kornia/checkpoint-190\n", "Configuration saved in swin-tiny-patch4-window7-224-finetuned-eurosat-kornia/checkpoint-190/config.json\n", "Model weights saved in swin-tiny-patch4-window7-224-finetuned-eurosat-kornia/checkpoint-190/pytorch_model.bin\n", "Feature extractor saved in swin-tiny-patch4-window7-224-finetuned-eurosat-kornia/checkpoint-190/preprocessor_config.json\n", "Feature extractor saved in swin-tiny-patch4-window7-224-finetuned-eurosat-kornia/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-kornia/checkpoint-380\n", "Configuration saved in swin-tiny-patch4-window7-224-finetuned-eurosat-kornia/checkpoint-380/config.json\n", "Model weights saved in swin-tiny-patch4-window7-224-finetuned-eurosat-kornia/checkpoint-380/pytorch_model.bin\n", "Feature extractor saved in swin-tiny-patch4-window7-224-finetuned-eurosat-kornia/checkpoint-380/preprocessor_config.json\n", "Feature extractor saved in swin-tiny-patch4-window7-224-finetuned-eurosat-kornia/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-kornia/checkpoint-570\n", "Configuration saved in swin-tiny-patch4-window7-224-finetuned-eurosat-kornia/checkpoint-570/config.json\n", "Model weights saved in swin-tiny-patch4-window7-224-finetuned-eurosat-kornia/checkpoint-570/pytorch_model.bin\n", "Feature extractor saved in swin-tiny-patch4-window7-224-finetuned-eurosat-kornia/checkpoint-570/preprocessor_config.json\n", "Feature extractor saved in swin-tiny-patch4-window7-224-finetuned-eurosat-kornia/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-kornia/checkpoint-570 (score: 0.9829629629629629).\n", "Saving model checkpoint to swin-tiny-patch4-window7-224-finetuned-eurosat-kornia\n", "Configuration saved in swin-tiny-patch4-window7-224-finetuned-eurosat-kornia/config.json\n", "Model weights saved in swin-tiny-patch4-window7-224-finetuned-eurosat-kornia/pytorch_model.bin\n", "Feature extractor saved in swin-tiny-patch4-window7-224-finetuned-eurosat-kornia/preprocessor_config.json\n", "Saving model checkpoint to swin-tiny-patch4-window7-224-finetuned-eurosat-kornia\n", "Configuration saved in swin-tiny-patch4-window7-224-finetuned-eurosat-kornia/config.json\n", "Model weights saved in swin-tiny-patch4-window7-224-finetuned-eurosat-kornia/pytorch_model.bin\n", "Feature extractor saved in swin-tiny-patch4-window7-224-finetuned-eurosat-kornia/preprocessor_config.json\n", "Several commits (2) will be pushed upstream.\n", "WARNING:huggingface_hub.repository:Several commits (2) will be pushed upstream.\n", "The progress bars may be unreliable.\n", "WARNING:huggingface_hub.repository:The progress bars may be unreliable.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a926f15ee449457fbf325846e6a8c738", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Upload file pytorch_model.bin: 0%| | 3.34k/105M [00:00 main\n", "\n", "WARNING:huggingface_hub.repository:To https://huggingface.co/nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat-kornia\n", " 2d806d2..8e80863 main -> main\n", "\n", "To https://huggingface.co/nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat-kornia\n", " 8e80863..93b8c0f main -> main\n", "\n", "WARNING:huggingface_hub.repository:To https://huggingface.co/nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat-kornia\n", " 8e80863..93b8c0f main -> main\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "***** train metrics *****\n", " epoch = 3.0\n", " total_flos = 1687935228GF\n", " train_loss = 0.1976\n", " train_runtime = 0:29:42.28\n", " train_samples_per_second = 40.903\n", " train_steps_per_second = 0.32\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", "pycharm": { "name": "#%% md\n" } }, "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": 216 }, "id": "niniUAnb5IrR", "outputId": "d6042a8c-5766-4542-f3bd-50e17a6a88dc", "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "***** Running Evaluation *****\n", " Num examples = 2700\n", " Batch size = 32\n" ] }, { "data": { "text/html": [ "\n", "

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\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "***** eval metrics *****\n", " epoch = 3.0\n", " eval_accuracy = 0.983\n", " eval_loss = 0.054\n", " eval_runtime = 0:00:20.96\n", " eval_samples_per_second = 128.761\n", " eval_steps_per_second = 4.054\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", "pycharm": { "name": "#%% md\n" } }, "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": 246, "referenced_widgets": [ "a7a8d06dc4d146dcb67c768e12bcdc53", "32955c73047f4fc7b656e381852614c7", "0f2c3be3663e4357935be76395e9abd3", "9503d58abcab45eea56984f00cb8562c", "7a23bbe2ce824c699691120ea84220f4", "ce93940729774f6aa246606efe7cc5eb", "db8ca3f6861d4b6eb619d33306aeeda8", "22921fe1bb3b4a9cb4cbf23eb4ea4930", "c21ffa7c0ba64aaa8f1187923f788572", "13952af7202349c2bce9a66aec24c452", "d3d37d996cbc4fbf8b0e032af39644e2" ] }, "id": "4aNMErFz-GzX", "outputId": "773c09ff-cee2-4662-93ce-ea56717d28cd", "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Saving model checkpoint to swin-tiny-patch4-window7-224-finetuned-eurosat-kornia\n", "Configuration saved in swin-tiny-patch4-window7-224-finetuned-eurosat-kornia/config.json\n", "Model weights saved in swin-tiny-patch4-window7-224-finetuned-eurosat-kornia/pytorch_model.bin\n", "Feature extractor saved in swin-tiny-patch4-window7-224-finetuned-eurosat-kornia/preprocessor_config.json\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a7a8d06dc4d146dcb67c768e12bcdc53", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Upload file runs/Aug29_08-52-09_cc75a613d50e/events.out.tfevents.1661765112.cc75a613d50e.286.2: 100%|#########…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "To https://huggingface.co/nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat-kornia\n", " 93b8c0f..1ee18f0 main -> main\n", "\n", "WARNING:huggingface_hub.repository:To https://huggingface.co/nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat-kornia\n", " 93b8c0f..1ee18f0 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-kornia/commit/1ee18f06019d72833eb5a81e08141f161ff121bd'" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "trainer.push_to_hub()" ] }, { "cell_type": "markdown", "metadata": { "id": "cZQnNUsI-Q4S", "pycharm": { "name": "#%% md\n" } }, "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", "pycharm": { "name": "#%% md\n" } }, "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 highway, and see how the model does." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81 }, "id": "UX6dwmT7GP91", "outputId": "29c1a967-680a-477d-80c9-b2536ad00787", "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from PIL import Image\n", "import requests\n", "\n", "url = \"https://datasets-server.huggingface.co/assets/nielsr/eurosat-demo/--/nielsr--eurosat-demo/train/0/image/image.jpg\"\n", "image = Image.open(requests.get(url, stream=True).raw)\n", "image" ] }, { "cell_type": "markdown", "metadata": { "id": "91-Ibh1--oI3", "pycharm": { "name": "#%% md\n" } }, "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/" }, "id": "xzwvix8X-st3", "outputId": "ddb4e9fc-7237-4aac-f9e9-e5e94735e906", "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "loading feature extractor configuration file swin-tiny-patch4-window7-224-finetuned-eurosat-kornia/preprocessor_config.json\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", "loading configuration file swin-tiny-patch4-window7-224-finetuned-eurosat-kornia/config.json\n", "Model config SwinConfig {\n", " \"_name_or_path\": \"swin-tiny-patch4-window7-224-finetuned-eurosat-kornia\",\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\": \"4.21.2\",\n", " \"use_absolute_embeddings\": false,\n", " \"window_size\": 7\n", "}\n", "\n", "loading weights file swin-tiny-patch4-window7-224-finetuned-eurosat-kornia/pytorch_model.bin\n", "All model checkpoint weights were used when initializing SwinForImageClassification.\n", "\n", "All the weights of SwinForImageClassification were initialized from the model checkpoint at swin-tiny-patch4-window7-224-finetuned-eurosat-kornia.\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 = \"swin-tiny-patch4-window7-224-finetuned-eurosat-kornia\"\n", "\n", "feature_extractor = AutoFeatureExtractor.from_pretrained(repo_name)\n", "model = AutoModelForImageClassification.from_pretrained(repo_name)" ] }, { "cell_type": "markdown", "metadata": { "id": "7oDoe_38AY3X", "pycharm": { "name": "#%% md\n" } }, "source": [ "We'll apply the exact same transformations as we did for validation. This involves 1) rescaling 2) resizing the shorter edge 3) center cropping 4) normalizing." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "OOKhRKmh9tsw", "outputId": "82290932-b5ce-4ee7-e3ea-f5f21b757eda", "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([1, 3, 224, 224])\n" ] } ], "source": [ "# prepare image for the model\n", "pixel_values = val_transforms(image.convert(\"RGB\"))\n", "print(pixel_values.shape)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "33E44G86_RtL", "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "import torch\n", "\n", "# forward pass\n", "with torch.no_grad():\n", " outputs = model(pixel_values)\n", " logits = outputs.logits" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "4ctUvqfs_Yyn", "outputId": "67d2c1c5-5eae-4a9d-cfde-d0a3d788c257", "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Predicted class: Highway\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", "pycharm": { "name": "#%% md\n" } }, "source": [ "Looks like our model got it correct! " ] }, { "cell_type": "markdown", "metadata": { "id": "-2A5W8dF_qYt", "pycharm": { "name": "#%% md\n" } }, "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": "066ae7a2-ce84-4ba0-be72-7ea1f8140d4d", "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "loading configuration file https://huggingface.co/nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat-kornia/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/b3eaaac5181ffa5c5bf07a7166dc39e66d48c9d6b8fc1ed94c5d31caa6fca218.8b60c7d323a7d1dee3b68511fce0682465c32224e710bda6b83ab1d60e17ea24\n", "Model config SwinConfig {\n", " \"_name_or_path\": \"nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat-kornia\",\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\": \"4.21.2\",\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-kornia/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/b3eaaac5181ffa5c5bf07a7166dc39e66d48c9d6b8fc1ed94c5d31caa6fca218.8b60c7d323a7d1dee3b68511fce0682465c32224e710bda6b83ab1d60e17ea24\n", "Model config SwinConfig {\n", " \"_name_or_path\": \"nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat-kornia\",\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\": \"4.21.2\",\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-kornia/resolve/main/pytorch_model.bin from cache at /root/.cache/huggingface/transformers/6074412634e7eb1a015c2e068f75f1f306df57ceddda5daec3487ae26359285f.198486060ca3b4b8c07b4fa2119668ff245cf1423d8e95dc36a0c9a6eb924df7\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-kornia.\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-kornia/resolve/main/preprocessor_config.json from cache at /root/.cache/huggingface/transformers/efa8c41be897930b453bf1928ffd14090191048bb8e175a096776154d47c795a.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-kornia\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "fPiuLDx3_9SY", "outputId": "180093bc-ee15-4c05-c305-6a09c8e140b9", "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/plain": [ "[{'score': 0.9998598098754883, 'label': 'Highway'},\n", " {'score': 0.00011431645543780178, 'label': 'River'},\n", " {'score': 5.5520140449516475e-06, 'label': 'AnnualCrop'},\n", " {'score': 4.760188403452048e-06, 'label': 'HerbaceousVegetation'},\n", " {'score': 4.518807600106811e-06, 'label': 'Residential'}]" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pipe(image)" ] }, { "cell_type": "markdown", "metadata": { "id": "BVXM6-g4AJmy", "pycharm": { "name": "#%% md\n" } }, "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", "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "pipe = pipeline(\"image-classification\", \n", " model=model,\n", " feature_extractor=feature_extractor)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "NfFH9eLMAdCX", "outputId": "a1b96de3-285b-4208-9fe2-16b6802c6e20", "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/plain": [ "[{'score': 0.9998598098754883, 'label': 'Highway'},\n", " {'score': 0.00011431645543780178, 'label': 'River'},\n", " {'score': 5.5520140449516475e-06, 'label': 'AnnualCrop'},\n", " {'score': 4.760188403452048e-06, 'label': 'HerbaceousVegetation'},\n", " {'score': 4.518807600106811e-06, 'label': 'Residential'}]" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], 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