# 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.