contrastive-image-text

TFVisionTextDualEncoder and CLIP model training examples

The following example showcases how to train a CLIP-like vision-text dual encoder model using a pre-trained vision and text encoder.

Such a model can be used for natural language image search and potentially zero-shot image classification. The model is inspired by CLIP, introduced by Alec Radford et al. The idea is to train a vision encoder and a text encoder jointly to project the representation of images and their captions into the same embedding space, such that the caption embeddings are located near the embeddings of the images they describe.

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 ..

Having downloaded COCO dataset manually you should be able to load with the ydshieh/coc_dataset_script dataset loading script:

import os
import datasets

COCO_DIR = os.path.join(os.getcwd(), "data")
ds = datasets.load_dataset("ydshieh/coco_dataset_script", "2017", data_dir=COCO_DIR)

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

We can either load a CLIP-like vision-text dual encoder model from an existing dual encoder model, or by using a pre-trained vision encoder model and a pre-trained text encoder model.

If you wish to load an existing dual encoder model, please use the --model_name_or_path argument. If you want to use separate pre-trained vision and text models, please use the --vision_model_name_or_path and --text_model_name_or_path arguments instead.

Train the model

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

python examples/tensorflow/contrastive-image-text/run_clip.py \
    --output_dir ./clip-roberta-finetuned \
    --vision_model_name_or_path openai/clip-vit-base-patch32 \
    --text_model_name_or_path FacebookAI/roberta-base \
    --data_dir $PWD/data \
    --dataset_name ydshieh/coco_dataset_script \
    --dataset_config_name=2017 \
    --image_column image_path \
    --caption_column caption \
    --remove_unused_columns=False \
    --do_train  --do_eval \
    --per_device_train_batch_size="64" \
    --per_device_eval_batch_size="64" \
    --learning_rate="5e-5" --warmup_steps="0" --weight_decay 0.1 \
    --overwrite_output_dir \
    --push_to_hub