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| Licensed under the Apache License, Version 2.0 (the "License"); |
| you may not use this file except in compliance with the License. |
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| http://www.apache.org/licenses/LICENSE-2.0 |
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| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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|
|
| # Image Classification training examples |
|
|
| The following example showcases how to train/fine-tune `ViT` for image-classification using the JAX/Flax backend. |
|
|
| JAX/Flax allows you to trace pure functions and compile them into efficient, fused accelerator code on both GPU and TPU. |
| Models written in JAX/Flax are **immutable** and updated in a purely functional |
| way which enables simple and efficient model parallelism. |
|
|
|
|
| In this example we will train/fine-tune the model on the [imagenette](https://github.com/fastai/imagenette) dataset. |
|
|
| ## Prepare the dataset |
|
|
| 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). |
|
|
|
|
| ### Download and extract the data. |
|
|
| ```bash |
| wget https://s3.amazonaws.com/fast-ai-imageclas/imagenette2.tgz |
| tar -xvzf imagenette2.tgz |
| ``` |
|
|
| 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 |
|
|
| ```bash |
| root/dog/xxx.png |
| root/dog/xxy.png |
| root/dog/[...]/xxz.png |
| |
| root/cat/123.png |
| root/cat/nsdf3.png |
| root/cat/[...]/asd932_.png |
| ``` |
|
|
| ## Train the model |
|
|
| Next we can run the example script to fine-tune the model: |
|
|
| ```bash |
| python run_image_classification.py \ |
| --output_dir ./vit-base-patch16-imagenette \ |
| --model_name_or_path google/vit-base-patch16-224-in21k \ |
| --train_dir="imagenette2/train" \ |
| --validation_dir="imagenette2/val" \ |
| --num_train_epochs 5 \ |
| --learning_rate 1e-3 \ |
| --per_device_train_batch_size 128 --per_device_eval_batch_size 128 \ |
| --overwrite_output_dir \ |
| --preprocessing_num_workers 32 \ |
| --push_to_hub |
| ``` |
|
|
| This should finish in ~7mins with 99% validation accuracy. |
