transformers/examples/pytorch/image-classification

Image classification examples

This directory contains 2 scripts that showcase how to fine-tune any model supported by the AutoModelForImageClassification API (such as ViT, ConvNeXT, ResNet, Swin Transformer...) using PyTorch. They can be used to fine-tune models on both datasets from the hub as well as on your own custom data.

Try out the inference widget here: https://huggingface.co/google/vit-base-patch16-224

Content:

• PyTorch version, Trainer
• PyTorch version, no Trainer

PyTorch version, Trainer

Based on the script run_image_classification.py.

The script leverages the 🤗 Trainer API to automatically take care of the training for you, running on distributed environments right away.

Using datasets from Hub

Here we show how to fine-tune a Vision Transformer (ViT) on the beans dataset, to classify the disease type of bean leaves.

python run_image_classification.py \
    --dataset_name beans \
    --output_dir ./beans_outputs/ \
    --remove_unused_columns False \
    --label_column_name labels \
    --do_train \
    --do_eval \
    --push_to_hub \
    --push_to_hub_model_id vit-base-beans \
    --learning_rate 2e-5 \
    --num_train_epochs 5 \
    --per_device_train_batch_size 8 \
    --per_device_eval_batch_size 8 \
    --logging_strategy steps \
    --logging_steps 10 \
    --eval_strategy epoch \
    --save_strategy epoch \
    --load_best_model_at_end True \
    --save_total_limit 3 \
    --seed 1337

👀 See the results here: nateraw/vit-base-beans.

Note that you can replace the model and dataset by simply setting the model_name_or_path and dataset_name arguments respectively, with any model or dataset from the hub. For an overview of all possible arguments, we refer to the docs of the TrainingArguments, which can be passed as flags.

If your model classification head dimensions do not fit the number of labels in the dataset, you can specify --ignore_mismatched_sizes to adapt it.

Using your own data

To use your own dataset, there are 2 ways:

• you can either provide your own folders as --train_dir and/or --validation_dir arguments
• you can upload your dataset to the hub (possibly as a private repo, if you prefer so), and simply pass the --dataset_name argument.

Below, we explain both in more detail.

Provide them as folders

If you provide your own folders with images, the script expects the following directory structure:

root/dog/xxx.png
root/dog/xxy.png
root/dog/[...]/xxz.png

root/cat/123.png
root/cat/nsdf3.png
root/cat/[...]/asd932_.png

In other words, you need to organize your images in subfolders, based on their class. You can then run the script like this:

python run_image_classification.py \
    --train_dir <path-to-train-root> \
    --output_dir ./outputs/ \
    --remove_unused_columns False \
    --do_train \
    --do_eval

Internally, the script will use the ImageFolder feature which will automatically turn the folders into 🤗 Dataset objects.

💡 The above will split the train dir into training and evaluation sets

• To control the split amount, use the --train_val_split flag.
• To provide your own validation split in its own directory, you can pass the --validation_dir <path-to-val-root> flag.

Upload your data to the hub, as a (possibly private) repo

It's very easy (and convenient) to upload your image dataset to the hub using the ImageFolder feature available in 🤗 Datasets. Simply do the following:

from datasets import load_dataset

# example 1: local folder
dataset = load_dataset("imagefolder", data_dir="path_to_your_folder")

# example 2: local files (supported formats are tar, gzip, zip, xz, rar, zstd)
dataset = load_dataset("imagefolder", data_files="path_to_zip_file")

# example 3: remote files (supported formats are tar, gzip, zip, xz, rar, zstd)
dataset = load_dataset("imagefolder", data_files="https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip")

# example 4: providing several splits
dataset = load_dataset("imagefolder", data_files={"train": ["path/to/file1", "path/to/file2"], "test": ["path/to/file3", "path/to/file4"]})

ImageFolder will create a label column, and the label name is based on the directory name.

Next, push it to the hub!

# assuming you have ran the huggingface-cli login command in a terminal
dataset.push_to_hub("name_of_your_dataset")

# if you want to push to a private repo, simply pass private=True:
dataset.push_to_hub("name_of_your_dataset", private=True)

and that's it! You can now train your model by simply setting the --dataset_name argument to the name of your dataset on the hub (as explained in Using datasets from the 🤗 hub).

More on this can also be found in this blog post.

Sharing your model on 🤗 Hub

0. If you haven't already, sign up for a 🤗 account

1. Make sure you have git-lfs installed and git set up.

$ apt install git-lfs
$ git config --global user.email "you@example.com"
$ git config --global user.name "Your Name"

2. Log in with your HuggingFace account credentials using huggingface-cli:

$ huggingface-cli login
# ...follow the prompts

3. When running the script, pass the following arguments:

python run_image_classification.py \
    --push_to_hub \
    --push_to_hub_model_id <name-your-model> \
    ...

PyTorch version, no Trainer

Based on the script run_image_classification_no_trainer.py.

Like run_image_classification.py, this script allows you to fine-tune any of the models on the hub on an image classification task. The main difference is that this script exposes the bare training loop, to allow you to quickly experiment and add any customization you would like.

It offers less options than the script with Trainer (for instance you can easily change the options for the optimizer or the dataloaders directly in the script) but still run in a distributed setup, and supports mixed precision by the means of the 🤗 Accelerate library. You can use the script normally after installing it:

pip install git+https://github.com/huggingface/accelerate

You can then use your usual launchers to run in it in a distributed environment, but the easiest way is to run

accelerate config

and reply to the questions asked. Then

accelerate test

that will check everything is ready for training. Finally, you can launch training with

accelerate launch run_image_classification_no_trainer.py --image_column_name img

This command is the same and will work for:

• single/multiple CPUs
• single/multiple GPUs
• TPUs

Note that this library is in alpha release so your feedback is more than welcome if you encounter any problem using it.

Regarding using custom data with this script, we refer to using your own data.