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* begin script * clean example, add readme * update readme * remove decay mask * remove masking * update readme & make flake happy
101 lines
3.6 KiB
Markdown
101 lines
3.6 KiB
Markdown
<!---
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Copyright 2021 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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-->
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# Image Classification training examples
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The following example showcases how to train/fine-tune `ViT` for image-classification using the JAX/Flax backend.
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JAX/Flax allows you to trace pure functions and compile them into efficient, fused accelerator code on both GPU and TPU.
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Models written in JAX/Flax are **immutable** and updated in a purely functional
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way which enables simple and efficient model parallelism.
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In this example we will train/fine-tune the model on the [imagenette](https://github.com/fastai/imagenette) dataset.
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Let's start by creating a model repository to save the trained model and logs.
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Here we call the model `"vit-base-patch16-imagenette"`, but you can change the model name as you like.
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You can do this either directly on [huggingface.co](https://huggingface.co/new) (assuming that
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you are logged in) or via the command line:
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```
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huggingface-cli repo create vit-base-patch16-imagenette
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```
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Next we clone the model repository to add the tokenizer and model files.
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```
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git clone https://huggingface.co/<your-username>/vit-base-patch16-imagenette
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```
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To ensure that all tensorboard traces will be uploaded correctly, we need to
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track them. You can run the following command inside your model repo to do so.
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```
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cd vit-base-patch16-imagenette
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git lfs track "*tfevents*"
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```
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Great, we have set up our model repository. During training, we will automatically
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push the training logs and model weights to the repo.
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Next, let's add a symbolic link to the `run_image_classification_flax.py`.
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```bash
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export MODEL_DIR="./vit-base-patch16-imagenette
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ln -s ~/transformers/examples/flax/summarization/run_image_classification_flax.py run_image_classification_flax.py
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```
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## Prepare the dataset
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We will use the [imagenette](https://github.com/fastai/imagenette) dataset to train/fine-tune our model. Imagenette is a subset of 10 easily classified classes from Imagenet (tench, English springer, cassette player, chain saw, church, French horn, garbage truck, gas pump, golf ball, parachute).
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### Download and extract the data.
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```bash
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wget https://s3.amazonaws.com/fast-ai-imageclas/imagenette2.tgz
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tar -xvzf imagenette2.tgz
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```
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This will create a `imagenette2` dir with two subdirectories `train` and `val` each with multiple subdirectories per class. The training script expects the following directory structure
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```bash
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root/dog/xxx.png
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root/dog/xxy.png
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root/dog/[...]/xxz.png
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root/cat/123.png
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root/cat/nsdf3.png
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root/cat/[...]/asd932_.png
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```
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## Train the model
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Next we can run the example script to fine-tune the model:
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```bash
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python run_image_classification.py \
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--output_dir ${MODEL_DIR} \
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--model_name_or_path google/vit-base-patch16-224-in21k \
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--train_dir="imagenette2/train" \
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--validation_dir="imagenette2/val" \
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--num_train_epochs 5 \
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--learning_rate 1e-3 \
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--per_device_train_batch_size 128 --per_device_eval_batch_size 128 \
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--overwrite_output_dir \
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--preprocessing_num_workers 32 \
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--push_to_hub
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```
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This should finish in ~7mins with 99% validation accuracy. |