image-captioning

Image Captioning (vision-encoder-text-decoder model) training example

The following example showcases how to finetune a vision-encoder-text-decoder model for image captioning using the JAX/Flax backend, leveraging 🤗 Transformers library's FlaxVisionEncoderDecoderModel.

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.

run_image_captioning_flax.py is a lightweight example of how to download and preprocess a dataset from the 🤗 Datasets library or use your own files (jsonlines or csv), then fine-tune one of the architectures above on it.

For custom datasets in jsonlines format please see: https://huggingface.co/docs/datasets/loading_datasets#json-files and you also will find examples of these below.

Download COCO dataset (2017)

This example uses COCO dataset (2017) through a custom dataset script, which requires users to manually download the COCO dataset before training.

mkdir data
cd data
wget http://images.cocodataset.org/zips/train2017.zip
wget http://images.cocodataset.org/zips/val2017.zip
wget http://images.cocodataset.org/zips/test2017.zip
wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip
wget http://images.cocodataset.org/annotations/image_info_test2017.zip
cd ..

Create a model from a vision encoder model and a text decoder model

Next, we create a FlaxVisionEncoderDecoderModel instance from a pre-trained vision encoder (ViT) and a pre-trained text decoder (GPT2):

python3 create_model_from_encoder_decoder_models.py \
    --output_dir model \
    --encoder_model_name_or_path google/vit-base-patch16-224-in21k \
    --decoder_model_name_or_path openai-community/gpt2

Train the model

Finally, we can run the example script to train the model:

python3 run_image_captioning_flax.py \
	--output_dir ./image-captioning-training-results \
	--model_name_or_path model \
	--dataset_name ydshieh/coco_dataset_script \
	--dataset_config_name=2017 \
	--data_dir $PWD/data \
	--image_column image_path \
	--caption_column caption \
	--do_train --do_eval --predict_with_generate \
	--num_train_epochs 1 \
	--eval_steps 500 \
	--learning_rate 3e-5 --warmup_steps 0 \
	--per_device_train_batch_size 32 \
	--per_device_eval_batch_size 32 \
	--overwrite_output_dir \
	--max_target_length 32 \
	--num_beams 8 \
	--preprocessing_num_workers 16 \
	--logging_steps 10 \
	--block_size 16384 \
	--push_to_hub

This should finish in about 1h30 on Cloud TPU, with validation loss and ROUGE2 score of 2.0153 and 14.64 respectively after 1 epoch. Training statistics can be accessed on Models.