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May 8, 2023 12:46
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image_classification.ipynb
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/nyck33/f58234644968903861036cfa95e0224a/image_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "S-yfHUO8DXbj", | |
| "outputId": "c335dfcb-458d-43f9-d1df-33280d795007" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", | |
| "Collecting transformers\n", | |
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| ] | |
| } | |
| ], | |
| "source": [ | |
| "# Transformers installation\n", | |
| "! pip install transformers datasets\n", | |
| "# To install from source instead of the last release, comment the command above and uncomment the following one.\n", | |
| "# ! pip install git+https://github.com/huggingface/transformers.git" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "IRKtcGsiDXbr" | |
| }, | |
| "source": [ | |
| "# Image classification" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "cellView": "form", | |
| "hide_input": true, | |
| "id": "v8eQN572DXbx", | |
| "outputId": "7dd512a3-d19a-4528-c8a9-28eb1313f372" | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/tjAIM7BOYhw?rel=0&controls=0&showinfo=0\" frameborder=\"0\" allowfullscreen></iframe>" | |
| ], | |
| "text/plain": [ | |
| "<IPython.core.display.HTML object>" | |
| ] | |
| }, | |
| "execution_count": null, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "#@title\n", | |
| "from IPython.display import HTML\n", | |
| "\n", | |
| "HTML('<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/tjAIM7BOYhw?rel=0&controls=0&showinfo=0\" frameborder=\"0\" allowfullscreen></iframe>')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "se_qxGMmDXb3" | |
| }, | |
| "source": [ | |
| "Image classification assigns a label or class to an image. Unlike text or audio classification, the inputs are the\n", | |
| "pixel values that comprise an image. There are many applications for image classification, such as detecting damage\n", | |
| "after a natural disaster, monitoring crop health, or helping screen medical images for signs of disease.\n", | |
| "\n", | |
| "This guide illustrates how to:\n", | |
| "\n", | |
| "1. Fine-tune [ViT](https://huggingface.co/docs/transformers/main/en/tasks/model_doc/vit) on the [Food-101](https://huggingface.co/datasets/food101) dataset to classify a food item in an image.\n", | |
| "2. Use your fine-tuned model for inference.\n", | |
| "\n", | |
| "<Tip>\n", | |
| "The task illustrated in this tutorial is supported by the following model architectures:\n", | |
| "\n", | |
| "<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->\n", | |
| "\n", | |
| "[BEiT](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/beit), [BiT](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/bit), [ConvNeXT](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/convnext), [ConvNeXTV2](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/convnextv2), [CvT](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/cvt), [Data2VecVision](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/data2vec-vision), [DeiT](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/deit), [DiNAT](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/dinat), [EfficientFormer](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/efficientformer), [EfficientNet](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/efficientnet), [FocalNet](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/focalnet), [ImageGPT](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/imagegpt), [LeViT](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/levit), [MobileNetV1](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/mobilenet_v1), [MobileNetV2](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/mobilenet_v2), [MobileViT](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/mobilevit), [NAT](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/nat), [Perceiver](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/perceiver), [PoolFormer](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/poolformer), [RegNet](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/regnet), [ResNet](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/resnet), [SegFormer](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/segformer), [Swin Transformer](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/swin), [Swin Transformer V2](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/swinv2), [VAN](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/van), [ViT](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/vit), [ViT Hybrid](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/vit_hybrid), [ViTMSN](https://huggingface.co/docs/transformers/main/en/tasks/../model_doc/vit_msn)\n", | |
| "<!--End of the generated tip-->\n", | |
| "\n", | |
| "</Tip>\n", | |
| "\n", | |
| "Before you begin, make sure you have all the necessary libraries installed:\n", | |
| "\n", | |
| "```bash\n", | |
| "pip install transformers datasets evaluate\n", | |
| "```\n", | |
| "\n", | |
| "We encourage you to log in to your Hugging Face account to upload and share your model with the community. When prompted, enter your token to log in:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 26, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 145, | |
| "referenced_widgets": [ | |
| "d08566f410e14655a4542c27e867eb6b", | |
| "f144ee3c7109406bbadb7cb68bf10fba", | |
| "77c45649d6764f189f532bf1d7716a98", | |
| "30255b89e83a47e2a0f004f8c34b9f0c", | |
| "b3955a4322eb4d7a950c93bd12a1828b", | |
| "5221027706ab4dcb951385f346b3b481", | |
| "37054382e4cc4bad908861e2149e95f7", | |
| "e478660b83f345c486a8c6b6690a7e8e", | |
| "f77624c20ea04920a5841077f7282f9c", | |
| "d64aac7b4dc94c4b9227d9fb6ce99671", | |
| "d575a0f17b2c429cba4fde3e1520bfec", | |
| "568f72a8a047413d8aaef0c41e737804", | |
| "399bacbb4d564f9e92e290df1a5ed495", | |
| "d9b73cc0807549ea802ad39bf7fc95d6", | |
| "038f0e9488a548a2b8a8d94750325791", | |
| "649c04c2b4b24b4fb44c42f8c3f8038a", | |
| "11ded4a0f13d48ceb7862ea6ea2ee174", | |
| "44c63c47216240e5bfed4b84ea0881b9", | |
| "dafbeb976615456996b7c73f77357326", | |
| "e3c6a995ee1542ef8df784f0aa8ccf5b", | |
| "7e49076eb4b4402197b6d44608f119de", | |
| "a9edc3a94f364c3f99a1354c15134c5a", | |
| "e0b0ba9dea6c479e806d3a02efa73c5d", | |
| "caf68b896a774df3a2aa1c5b87ec793f", | |
| "82198843790541b1adb93d4329ef8c40", | |
| "55ee7d15dd624b81b1cc215c204dcbc4", | |
| "69849c3e08014861a1aee3971dc93eaf", | |
| "9ba00a195d904037b0f28ca227e9b1f6", | |
| "df649007d64046efad7838cebb585c6d", | |
| "e378e6ae57274b268362ce47db5b2ae4", | |
| "8ac6c6e797a842c5b081eb193c07e788", | |
| "1b22ebe42a6a4fe9b017001f48b98c61" | |
| ] | |
| }, | |
| "id": "VVDt1QnTDXb6", | |
| "outputId": "0fd4e086-4163-4fe9-dc8d-20bd6b04e41e" | |
| }, | |
| "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": "d08566f410e14655a4542c27e867eb6b" | |
| } | |
| }, | |
| "metadata": {} | |
| } | |
| ], | |
| "source": [ | |
| "from huggingface_hub import notebook_login\n", | |
| "\n", | |
| "notebook_login()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "Hl5Bpc7SDXcA" | |
| }, | |
| "source": [ | |
| "## Load Food-101 dataset" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "cCTFzoZyDXcC" | |
| }, | |
| "source": [ | |
| "Start by loading a smaller subset of the Food-101 dataset from the 🤗 Datasets library. This will give you a chance to\n", | |
| "experiment and make sure everything works before spending more time training on the full dataset." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 7, | |
| "metadata": { | |
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| "id": "o8HMZE9dDXcD", | |
| "outputId": "ab1cfa3d-d5d8-4f03-b012-9dd010b63168" | |
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| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Downloading and preparing dataset food101/default to /root/.cache/huggingface/datasets/food101/default/0.0.0/7cebe41a80fb2da3f08fcbef769c8874073a86346f7fb96dc0847d4dfc318295...\n" | |
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| } | |
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| "metadata": {} | |
| }, | |
| { | |
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| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
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| ], | |
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| "version_major": 2, | |
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| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Dataset food101 downloaded and prepared to /root/.cache/huggingface/datasets/food101/default/0.0.0/7cebe41a80fb2da3f08fcbef769c8874073a86346f7fb96dc0847d4dfc318295. Subsequent calls will reuse this data.\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "from datasets import load_dataset\n", | |
| "\n", | |
| "food = load_dataset(\"food101\", split=\"train[:5000]\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "Eaxvd49fDXcF" | |
| }, | |
| "source": [ | |
| "Split the dataset's `train` split into a train and test set with the [train_test_split](https://huggingface.co/docs/datasets/main/en/package_reference/main_classes#datasets.Dataset.train_test_split) method:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 8, | |
| "metadata": { | |
| "id": "4F1U7mx2DXcK" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "food = food.train_test_split(test_size=0.2)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "VJYyO5jXDXcL" | |
| }, | |
| "source": [ | |
| "Then take a look at an example:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 9, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "6TWhpIHJDXcM", | |
| "outputId": "9477fb4a-0d81-4caa-dd85-16e6ae6aea6f" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=384x512 at 0x7FB8C5DCBA90>,\n", | |
| " 'label': 20}" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 9 | |
| } | |
| ], | |
| "source": [ | |
| "food[\"train\"][0]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "O_KRtnGoDXcO" | |
| }, | |
| "source": [ | |
| "Each example in the dataset has two fields:\n", | |
| "\n", | |
| "- `image`: a PIL image of the food item\n", | |
| "- `label`: the label class of the food item\n", | |
| "\n", | |
| "To make it easier for the model to get the label name from the label id, create a dictionary that maps the label name\n", | |
| "to an integer and vice versa:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 10, | |
| "metadata": { | |
| "id": "87en90nvDXcR" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "labels = food[\"train\"].features[\"label\"].names\n", | |
| "label2id, id2label = dict(), dict()\n", | |
| "for i, label in enumerate(labels):\n", | |
| " label2id[label] = str(i)\n", | |
| " id2label[str(i)] = label" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "k_OCnSmiDXcS" | |
| }, | |
| "source": [ | |
| "Now you can convert the label id to a label name:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 12, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 35 | |
| }, | |
| "id": "lcw3OWypDXcT", | |
| "outputId": "8fdfa8c3-2844-4e4a-c3f5-acb6873811cd" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "'chicken_wings'" | |
| ], | |
| "application/vnd.google.colaboratory.intrinsic+json": { | |
| "type": "string" | |
| } | |
| }, | |
| "metadata": {}, | |
| "execution_count": 12 | |
| } | |
| ], | |
| "source": [ | |
| "id2label[str(20)]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "5mmGu7dkDXcU" | |
| }, | |
| "source": [ | |
| "## Preprocess" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "xjtaPbyQDXcV" | |
| }, | |
| "source": [ | |
| "The next step is to load a ViT image processor to process the image into a tensor:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 81, | |
| "referenced_widgets": [ | |
| "60d4c4d080d24d8ab4281a76f9c74e64", | |
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| "46b453304b2545cca2cc1deb087e0f0f", | |
| "d6e53f26599d4f7bb6a54b31a59405de", | |
| "aff210c536bb4637b054e8991d3e5766", | |
| "3f0b7f182acf4487b330c00a35a0d75a", | |
| "acbd7a0ac49e44e493aafacd24940990", | |
| "33a3d5c22cd3481196c6e4f7646700a6", | |
| "04953d8f521d4e869874b983ee5bbce6", | |
| "facf2c0254d24ea6a3a6ce2d29f03870", | |
| "932f1662fc1e43acb5fdb3f302bd9d1c", | |
| "208c074eafce4b5d8359551328942689", | |
| "0391d6dc73ee41449f1b8b974bf28e93", | |
| "828328294aad4b7dace470808d199e7f", | |
| "0b5f780e95034dcb9dff0aa1b82e567c", | |
| "aa561a032da24e8c8597375b95b0ceb9", | |
| "91dfdfd6bdca4bbf93d2ddbb65e3f44f", | |
| "9bac672fbe4d408797e33245d15b19ad", | |
| "2dd3b1ab33d3470d872b41dc8e01a4c9", | |
| "19d5eec88d17491bb621faa59eaf2dae", | |
| "cb11b9df604f4d2cb87bd72c70bb8afd", | |
| "b2821942bff849f29d4fe71632e7dae3" | |
| ] | |
| }, | |
| "id": "G6K8XlTNDXcY", | |
| "outputId": "425e0d0e-390e-4c06-bab7-af524b870264" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Downloading (…)rocessor_config.json: 0%| | 0.00/160 [00:00<?, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "60d4c4d080d24d8ab4281a76f9c74e64" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Downloading (…)lve/main/config.json: 0%| | 0.00/502 [00:00<?, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
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| "model_id": "208c074eafce4b5d8359551328942689" | |
| } | |
| }, | |
| "metadata": {} | |
| } | |
| ], | |
| "source": [ | |
| "from transformers import AutoImageProcessor\n", | |
| "\n", | |
| "checkpoint = \"google/vit-base-patch16-224-in21k\"\n", | |
| "image_processor = AutoImageProcessor.from_pretrained(checkpoint)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "9aDhXGdQDXca" | |
| }, | |
| "source": [ | |
| "To avoid overfitting and to make the model more robust, add some data augmentation to the training part of the dataset.\n", | |
| "Here we use Keras preprocessing layers to define the transformations for the training data (includes data augmentation),\n", | |
| "and transformations for the validation data (only center cropping, resizing and normalizing). You can use `tf.image`or\n", | |
| "any other library you prefer." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": { | |
| "id": "-XMQA04NDXcb" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "from tensorflow import keras\n", | |
| "from tensorflow.keras import layers\n", | |
| "\n", | |
| "size = (image_processor.size[\"height\"], image_processor.size[\"width\"])\n", | |
| "\n", | |
| "train_data_augmentation = keras.Sequential(\n", | |
| " [\n", | |
| " layers.RandomCrop(size[0], size[1]),\n", | |
| " layers.Rescaling(scale=1.0 / 127.5, offset=-1),\n", | |
| " layers.RandomFlip(\"horizontal\"),\n", | |
| " layers.RandomRotation(factor=0.02),\n", | |
| " layers.RandomZoom(height_factor=0.2, width_factor=0.2),\n", | |
| " ],\n", | |
| " name=\"train_data_augmentation\",\n", | |
| ")\n", | |
| "\n", | |
| "val_data_augmentation = keras.Sequential(\n", | |
| " [\n", | |
| " layers.CenterCrop(size[0], size[1]),\n", | |
| " layers.Rescaling(scale=1.0 / 127.5, offset=-1),\n", | |
| " ],\n", | |
| " name=\"val_data_augmentation\",\n", | |
| ")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "vrIZHYa9DXcd" | |
| }, | |
| "source": [ | |
| "Next, create functions to apply appropriate transformations to a batch of images, instead of one image at a time." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 13, | |
| "metadata": { | |
| "id": "gYV2EV4uDXce" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import numpy as np\n", | |
| "import tensorflow as tf\n", | |
| "from PIL import Image\n", | |
| "\n", | |
| "\n", | |
| "def convert_to_tf_tensor(image: Image):\n", | |
| " np_image = np.array(image)\n", | |
| " tf_image = tf.convert_to_tensor(np_image)\n", | |
| " # `expand_dims()` is used to add a batch dimension since\n", | |
| " # the TF augmentation layers operates on batched inputs.\n", | |
| " return tf.expand_dims(tf_image, 0)\n", | |
| "\n", | |
| "\n", | |
| "def preprocess_train(example_batch):\n", | |
| " \"\"\"Apply train_transforms across a batch.\"\"\"\n", | |
| " images = [\n", | |
| " train_data_augmentation(convert_to_tf_tensor(image.convert(\"RGB\"))) for image in example_batch[\"image\"]\n", | |
| " ]\n", | |
| " example_batch[\"pixel_values\"] = [tf.transpose(tf.squeeze(image)) for image in images]\n", | |
| " return example_batch\n", | |
| "\n", | |
| "\n", | |
| "def preprocess_val(example_batch):\n", | |
| " \"\"\"Apply val_transforms across a batch.\"\"\"\n", | |
| " images = [\n", | |
| " val_data_augmentation(convert_to_tf_tensor(image.convert(\"RGB\"))) for image in example_batch[\"image\"]\n", | |
| " ]\n", | |
| " example_batch[\"pixel_values\"] = [tf.transpose(tf.squeeze(image)) for image in images]\n", | |
| " return example_batch" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "CF3egQy0DXcf" | |
| }, | |
| "source": [ | |
| "Use 🤗 Datasets [set_transform](https://huggingface.co/docs/datasets/main/en/package_reference/main_classes#datasets.Dataset.set_transform) to apply the transformations on the fly:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 14, | |
| "metadata": { | |
| "id": "ULXXKdc9DXcg" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "food[\"train\"].set_transform(preprocess_train)\n", | |
| "food[\"test\"].set_transform(preprocess_val)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "pA8pAIvUDXcg" | |
| }, | |
| "source": [ | |
| "As a final preprocessing step, create a batch of examples using `DefaultDataCollator`. Unlike other data collators in 🤗 Transformers, the\n", | |
| "`DefaultDataCollator` does not apply additional preprocessing, such as padding." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 15, | |
| "metadata": { | |
| "id": "3jOeqWJcDXch" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "from transformers import DefaultDataCollator\n", | |
| "\n", | |
| "data_collator = DefaultDataCollator(return_tensors=\"tf\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "PrOWH0OlDXci" | |
| }, | |
| "source": [ | |
| "## Evaluate" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "FKE8VtsaDXci" | |
| }, | |
| "source": [ | |
| "Including a metric during training is often helpful for evaluating your model's performance. You can quickly load an\n", | |
| "evaluation method with the 🤗 [Evaluate](https://huggingface.co/docs/evaluate/index) library. For this task, load\n", | |
| "the [accuracy](https://huggingface.co/spaces/evaluate-metric/accuracy) metric (see the 🤗 Evaluate [quick tour](https://huggingface.co/docs/evaluate/a_quick_tour) to learn more about how to load and compute a metric):" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "! pip install evaluate" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "jtCjz-TDGCwe", | |
| "outputId": "dd3fa6c4-5b9d-407c-b84d-13e9db62cc2e" | |
| }, | |
| "execution_count": 17, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", | |
| "Collecting evaluate\n", | |
| " Downloading evaluate-0.4.0-py3-none-any.whl (81 kB)\n", | |
| "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/81.4 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m81.4/81.4 kB\u001b[0m \u001b[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", | |
| "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from evaluate) (1.5.3)\n", | |
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| "Installing collected packages: evaluate\n", | |
| "Successfully installed evaluate-0.4.0\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 18, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 49, | |
| "referenced_widgets": [ | |
| "2f914de573bc42d68d2f47af098547ac", | |
| "a10369c7b8244d65866475139c1832fb", | |
| "110dbcae082c4a12a483b14580222986", | |
| "3b79d42c04bf4ca4b2f82d3423e1f3ac", | |
| "22461583a6fd4c5aba3197b66a4e9b21", | |
| "adb4b38a24cc4fa08b2b15966450ff1c", | |
| "4f9e684f91e24955a95afd5dccecf18f", | |
| "bc0e54056a484904a66524e25e51a33e", | |
| "e772ca50961e4ff3b3d968e1127a505c", | |
| "9ad0a6072b3f4d80afccacc3f6f1b228", | |
| "b230cb86183b4cb09cdb97b32aedcfdb" | |
| ] | |
| }, | |
| "id": "ZI6ffXdXDXci", | |
| "outputId": "fa292afa-cd9f-4176-d6bf-aa7bd9a715a2" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Downloading builder script: 0%| | 0.00/4.20k [00:00<?, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "2f914de573bc42d68d2f47af098547ac" | |
| } | |
| }, | |
| "metadata": {} | |
| } | |
| ], | |
| "source": [ | |
| "import evaluate\n", | |
| "\n", | |
| "accuracy = evaluate.load(\"accuracy\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "WDCmO9vXDXcj" | |
| }, | |
| "source": [ | |
| "Then create a function that passes your predictions and labels to [compute](https://huggingface.co/docs/evaluate/main/en/package_reference/main_classes#evaluate.EvaluationModule.compute) to calculate the accuracy:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 19, | |
| "metadata": { | |
| "id": "1JQNlnbLDXck" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import numpy as np\n", | |
| "\n", | |
| "\n", | |
| "def compute_metrics(eval_pred):\n", | |
| " predictions, labels = eval_pred\n", | |
| " predictions = np.argmax(predictions, axis=1)\n", | |
| " return accuracy.compute(predictions=predictions, references=labels)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "S1wt98DQDXck" | |
| }, | |
| "source": [ | |
| "Your `compute_metrics` function is ready to go now, and you'll return to it when you set up your training." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "pEMk3wGFDXcl" | |
| }, | |
| "source": [ | |
| "## Train" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "7YASW86gDXcn" | |
| }, | |
| "source": [ | |
| "<Tip>\n", | |
| "\n", | |
| "If you are unfamiliar with fine-tuning a model with Keras, check out the [basic tutorial](https://huggingface.co/docs/transformers/main/en/tasks/./training#train-a-tensorflow-model-with-keras) first!\n", | |
| "\n", | |
| "</Tip>\n", | |
| "\n", | |
| "To fine-tune a model in TensorFlow, follow these steps:\n", | |
| "1. Define the training hyperparameters, and set up an optimizer and a learning rate schedule.\n", | |
| "2. Instantiate a pre-treined model.\n", | |
| "3. Convert a 🤗 Dataset to a `tf.data.Dataset`.\n", | |
| "4. Compile your model.\n", | |
| "5. Add callbacks and use the `fit()` method to run the training.\n", | |
| "6. Upload your model to 🤗 Hub to share with the community.\n", | |
| "\n", | |
| "Start by defining the hyperparameters, optimizer and learning rate schedule:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 20, | |
| "metadata": { | |
| "id": "b5wTHrX-DXco" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "from transformers import create_optimizer\n", | |
| "\n", | |
| "batch_size = 16\n", | |
| "num_epochs = 5\n", | |
| "num_train_steps = len(food[\"train\"]) * num_epochs\n", | |
| "learning_rate = 3e-5\n", | |
| "weight_decay_rate = 0.01\n", | |
| "\n", | |
| "optimizer, lr_schedule = create_optimizer(\n", | |
| " init_lr=learning_rate,\n", | |
| " num_train_steps=num_train_steps,\n", | |
| " weight_decay_rate=weight_decay_rate,\n", | |
| " num_warmup_steps=0,\n", | |
| ")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "29lRyYs_DXcp" | |
| }, | |
| "source": [ | |
| "Then, load ViT with [TFAutoModelForImageClassification](https://huggingface.co/docs/transformers/main/en/model_doc/auto#transformers.TFAutoModelForImageClassification) along with the label mappings:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 21, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 158, | |
| "referenced_widgets": [ | |
| "4d645e808ca44cd495b2967dff7ae859", | |
| "dc8476ce11c94ff69bc1f322261bca8d", | |
| "8e64d511ee754df0a40673f3f0c7b566", | |
| "997dfdb4166b42a784e527a4b63f1f55", | |
| "6c7490859ce9463cbe2fccb1e356378a", | |
| "6fb94a656c03425c875f44534e88e9de", | |
| "ac1a6b9598ed4919b0cfdced996019e1", | |
| "a8edaf96dfc74ad298858236c503b5cf", | |
| "e84d9ad2f43b4064b83b005fb24f68d3", | |
| "576b73ad99c44be5a7521e046ed35a25", | |
| "b14b9b7ce3fc487585f563dc0185d27c" | |
| ] | |
| }, | |
| "id": "TtoUNYaXDXcq", | |
| "outputId": "6db7c08e-7dc0-4c1a-80b3-40739803da70" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Downloading tf_model.h5: 0%| | 0.00/346M [00:00<?, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "4d645e808ca44cd495b2967dff7ae859" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "Some layers from the model checkpoint at google/vit-base-patch16-224-in21k were not used when initializing TFViTForImageClassification: ['vit/pooler/dense/bias:0', 'vit/pooler/dense/kernel:0']\n", | |
| "- This IS expected if you are initializing TFViTForImageClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", | |
| "- This IS NOT expected if you are initializing TFViTForImageClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n", | |
| "Some layers of TFViTForImageClassification were not initialized from the model checkpoint at google/vit-base-patch16-224-in21k and are newly initialized: ['classifier']\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 TFAutoModelForImageClassification\n", | |
| "\n", | |
| "model = TFAutoModelForImageClassification.from_pretrained(\n", | |
| " checkpoint,\n", | |
| " id2label=id2label,\n", | |
| " label2id=label2id,\n", | |
| ")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "hNTRTWfaDXcq" | |
| }, | |
| "source": [ | |
| "Convert your datasets to the `tf.data.Dataset` format using the [to_tf_dataset](https://huggingface.co/docs/datasets/main/en/package_reference/main_classes#datasets.Dataset.to_tf_dataset) and your `data_collator`:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 22, | |
| "metadata": { | |
| "id": "EXsITQtODXcr" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# converting our train dataset to tf.data.Dataset\n", | |
| "tf_train_dataset = food[\"train\"].to_tf_dataset(\n", | |
| " columns=\"pixel_values\", label_cols=\"label\", shuffle=True, batch_size=batch_size, collate_fn=data_collator\n", | |
| ")\n", | |
| "\n", | |
| "# converting our test dataset to tf.data.Dataset\n", | |
| "tf_eval_dataset = food[\"test\"].to_tf_dataset(\n", | |
| " columns=\"pixel_values\", label_cols=\"label\", shuffle=True, batch_size=batch_size, collate_fn=data_collator\n", | |
| ")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "aBrSBtNfDXct" | |
| }, | |
| "source": [ | |
| "Configure the model for training with `compile()`:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 23, | |
| "metadata": { | |
| "id": "fITuCpGMDXcu" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "from tensorflow.keras.losses import SparseCategoricalCrossentropy\n", | |
| "\n", | |
| "loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n", | |
| "model.compile(optimizer=optimizer, loss=loss)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "0D9DIBSVDXcv" | |
| }, | |
| "source": [ | |
| "To compute the accuracy from the predictions and push your model to the 🤗 Hub, use [Keras callbacks](https://huggingface.co/docs/transformers/main/en/tasks/../main_classes/keras_callbacks).\n", | |
| "Pass your `compute_metrics` function to [KerasMetricCallback](https://huggingface.co/docs/transformers/main/en/tasks/../main_classes/keras_callbacks#transformers.KerasMetricCallback),\n", | |
| "and use the [PushToHubCallback](https://huggingface.co/docs/transformers/main/en/tasks/../main_classes/keras_callbacks#transformers.PushToHubCallback) to upload the model:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 27, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "I5KPIj2iDXcw", | |
| "outputId": "ca7a1bd5-cde2-4b2c-ce1d-e95fa429db5a" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "Cloning https://huggingface.co/mLStudent33/food_classifier into local empty directory.\n", | |
| "WARNING:huggingface_hub.repository:Cloning https://huggingface.co/mLStudent33/food_classifier into local empty directory.\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "from transformers.keras_callbacks import KerasMetricCallback, PushToHubCallback\n", | |
| "\n", | |
| "metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_eval_dataset)\n", | |
| "push_to_hub_callback = PushToHubCallback(\n", | |
| " output_dir=\"food_classifier\",\n", | |
| " tokenizer=image_processor,\n", | |
| " save_strategy=\"no\",\n", | |
| ")\n", | |
| "callbacks = [metric_callback, push_to_hub_callback]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "lhrWoqkxDXcw" | |
| }, | |
| "source": [ | |
| "Finally, you are ready to train your model! Call `fit()` with your training and validation datasets, the number of epochs,\n", | |
| "and your callbacks to fine-tune the model:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 33, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 354 | |
| }, | |
| "id": "hSTL1JQUDXcx", | |
| "outputId": "497039f4-6293-4f06-e42b-2a6e239b059a" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Epoch 1/5\n", | |
| "142/250 [================>.............] - ETA: 2:22 - loss: 0.2991" | |
| ] | |
| }, | |
| { | |
| "output_type": "error", | |
| "ename": "KeyboardInterrupt", | |
| "evalue": "ignored", | |
| "traceback": [ | |
| "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", | |
| "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", | |
| "\u001b[0;32m<ipython-input-33-31b3a59a44c5>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtf_train_dataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtf_eval_dataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_epochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", | |
| "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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 66\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 67\u001b[0m \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\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/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m 1683\u001b[0m ):\n\u001b[1;32m 1684\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1685\u001b[0;31m \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\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 1686\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1687\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\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/usr/local/lib/python3.10/dist-packages/tensorflow/python/util/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 149\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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 151\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 152\u001b[0m \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\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/tensorflow/python/eager/polymorphic_function/polymorphic_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 892\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 893\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_compile\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--> 894\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\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 895\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 896\u001b[0m \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\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/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 924\u001b[0m \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 925\u001b[0m \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 926\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_no_variable_creation_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 927\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_variable_creation_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\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 928\u001b[0m \u001b[0;31m# Release the lock early so that multiple threads can perform the call\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/tensorflow/python/eager/polymorphic_function/tracing_compiler.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 141\u001b[0m (concrete_function,\n\u001b[1;32m 142\u001b[0m filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[0;32m--> 143\u001b[0;31m return concrete_function._call_flat(\n\u001b[0m\u001b[1;32m 144\u001b[0m filtered_flat_args, captured_inputs=concrete_function.captured_inputs) # pylint: disable=protected-access\n\u001b[1;32m 145\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/polymorphic_function/monomorphic_function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m 1755\u001b[0m and executing_eagerly):\n\u001b[1;32m 1756\u001b[0m \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1757\u001b[0;31m return self._build_call_outputs(self._inference_function.call(\n\u001b[0m\u001b[1;32m 1758\u001b[0m ctx, args, cancellation_manager=cancellation_manager))\n\u001b[1;32m 1759\u001b[0m forward_backward = self._select_forward_and_backward_functions(\n", | |
| "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/polymorphic_function/monomorphic_function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m 379\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0m_InterpolateFunctionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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 380\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcancellation_manager\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--> 381\u001b[0;31m outputs = execute.execute(\n\u001b[0m\u001b[1;32m 382\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\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 383\u001b[0m \u001b[0mnum_outputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_outputs\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/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 51\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\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---> 52\u001b[0;31m tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[1;32m 53\u001b[0m inputs, attrs, num_outputs)\n\u001b[1;32m 54\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;31mKeyboardInterrupt\u001b[0m: " | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "model.fit(tf_train_dataset, validation_data=tf_eval_dataset, epochs=num_epochs, callbacks=callbacks)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "RtoglFSaDXc_" | |
| }, | |
| "source": [ | |
| "Congratulations! You have fine-tuned your model and shared it on the 🤗 Hub. You can now use it for inference!\n", | |
| "\n", | |
| "\n", | |
| "<Tip>\n", | |
| "\n", | |
| "For a more in-depth example of how to finetune a model for image classification, take a look at the corresponding [PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb).\n", | |
| "\n", | |
| "</Tip>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "WJ5qzP6qDXdA" | |
| }, | |
| "source": [ | |
| "## Inference" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "vJmJyoTgDXdA" | |
| }, | |
| "source": [ | |
| "Great, now that you've fine-tuned a model, you can use it for inference!\n", | |
| "\n", | |
| "Load an image you'd like to run inference on:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "l37Wj7w2DXdB" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "ds = load_dataset(\"food101\", split=\"validation[:10]\")\n", | |
| "image = ds[\"image\"][0]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "Pww24FLjDXdC" | |
| }, | |
| "source": [ | |
| "<div class=\"flex justify-center\">\n", | |
| " <img src=\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png\" alt=\"image of beignets\"/>\n", | |
| "</div>\n", | |
| "\n", | |
| "The simplest way to try out your finetuned model for inference is to use it in a [pipeline()](https://huggingface.co/docs/transformers/main/en/main_classes/pipelines#transformers.pipeline). Instantiate a `pipeline` for image classification with your model, and pass your image to it:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 34, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "4-e-o6P7DXdE", | |
| "outputId": "8d6b7348-537b-4719-f39a-0caf34ea6dc2" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "All model checkpoint layers were used when initializing TFViTForImageClassification.\n", | |
| "\n", | |
| "All the layers of TFViTForImageClassification were initialized from the model checkpoint at food_classifier.\n", | |
| "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFViTForImageClassification for predictions without further training.\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "[{'score': 0.9078986644744873, 'label': 'beignets'},\n", | |
| " {'score': 0.008867640048265457, 'label': 'chicken_wings'},\n", | |
| " {'score': 0.008824972435832024, 'label': 'prime_rib'},\n", | |
| " {'score': 0.006528730038553476, 'label': 'bruschetta'},\n", | |
| " {'score': 0.006001484580338001, 'label': 'hamburger'}]" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 34 | |
| } | |
| ], | |
| "source": [ | |
| "from transformers import pipeline\n", | |
| "\n", | |
| "classifier = pipeline(\"image-classification\", model=\"food_classifier\")\n", | |
| "classifier(image)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "ZZ_yUf91DXdH" | |
| }, | |
| "source": [ | |
| "You can also manually replicate the results of the `pipeline` if you'd like:\n", | |
| "\n", | |
| "\n", | |
| "Load an image processor to preprocess the image and return the `input` as TensorFlow tensors:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 37, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 49, | |
| "referenced_widgets": [ | |
| "f8589e5e4e9d4aae83fc5d9b3692eb90", | |
| "fb85f49c1abb44469f84f8267df5e044", | |
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| "505cc3c1e4d544909eac37e389784052", | |
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| "9c2477b71b8c4a0abc4219475409a19d", | |
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| ] | |
| }, | |
| "id": "lMJeYkrRDXdH", | |
| "outputId": "99f7a527-f589-4aac-c24f-25689aeacc90" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Downloading (…)rocessor_config.json: 0%| | 0.00/325 [00:00<?, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
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| } | |
| }, | |
| "metadata": {} | |
| } | |
| ], | |
| "source": [ | |
| "from transformers import AutoImageProcessor\n", | |
| "\n", | |
| "image_processor = AutoImageProcessor.from_pretrained(\"mLStudent33/food_classifier\")\n", | |
| "inputs = image_processor(image, return_tensors=\"tf\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "muugY6rQDXdI" | |
| }, | |
| "source": [ | |
| "Pass your inputs to the model and return the logits:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 39, | |
| "metadata": { | |
| "colab": { | |
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| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Downloading (…)lve/main/config.json: 0%| | 0.00/5.55k [00:00<?, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
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| }, | |
| "metadata": {} | |
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| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Downloading tf_model.h5: 0%| | 0.00/344M [00:00<?, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "3719070dc5bb4372a94adf289491e416" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "All model checkpoint layers were used when initializing TFViTForImageClassification.\n", | |
| "\n", | |
| "All the layers of TFViTForImageClassification were initialized from the model checkpoint at mLStudent33/food_classifier.\n", | |
| "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFViTForImageClassification for predictions without further training.\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "from transformers import TFAutoModelForImageClassification\n", | |
| "\n", | |
| "model = TFAutoModelForImageClassification.from_pretrained(\"mLStudent33/food_classifier\")\n", | |
| "logits = model(**inputs).logits" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "utb5CxqjDXdL" | |
| }, | |
| "source": [ | |
| "Get the predicted label with the highest probability, and use the model's `id2label` mapping to convert it to a label:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 40, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 35 | |
| }, | |
| "id": "YF0uqCxdDXdM", | |
| "outputId": "4219bde9-c3b4-4013-e1b4-62ed6692ab01" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "'beignets'" | |
| ], | |
| "application/vnd.google.colaboratory.intrinsic+json": { | |
| "type": "string" | |
| } | |
| }, | |
| "metadata": {}, | |
| "execution_count": 40 | |
| } | |
| ], | |
| "source": [ | |
| "predicted_class_id = int(tf.math.argmax(logits, axis=-1)[0])\n", | |
| "model.config.id2label[predicted_class_id]" | |
| ] | |
| }, | |
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