PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation

BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation

This is the PyTorch code of the BLIP paper. The code has been tested on PyTorch 1.10.
To install the dependencies, run

pip install -r requirements.txt

Catalog:

• Inference demo
• Pre-trained and finetuned checkpoints
• Finetuning code for Image-Text Retrieval, Image Captioning, VQA, and NLVR2
• Pre-training code
• Download of bootstrapped pre-training datasets

Inference demo:

Run our interactive demo using Colab notebook (no GPU needed).
The demo includes code for: (1) image captioning, (2) open-ended visual question answering, (3) multimodal / unimodal feature extraction.

Pre-trained checkpoints:

Finetuned checkpoints:

Image-Text Retrieval:

1. Download COCO and Flickr30k datasets from the original websites, and set ‘image_root’ in configs/retrieval_{dataset}.yaml accordingly.
2. To evaluate the finetuned BLIP model on COCO, run:

python -m torch.distributed.run --nproc_per_node=8 train_retrieval.py \
--config ./configs/retrieval_coco.yaml \
--output_dir output/retrieval_coco \
--evaluate

3. To finetune the pre-trained checkpoint using 8 A100 GPUs, first set ‘pretrained’ in configs/retrieval_coco.yaml as “https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base.pth“. Then run:

python -m torch.distributed.run --nproc_per_node=8 train_retrieval.py \
--config ./configs/retrieval_coco.yaml \
--output_dir output/retrieval_coco 

Image-Text Captioning:

1. Download COCO and NoCaps datasets from the original websites, and set ‘image_root’ in configs/caption_coco.yaml and configs/nocaps.yaml accordingly.
2. To evaluate the finetuned BLIP model on COCO, run:

python -m torch.distributed.run --nproc_per_node=8 train_caption.py --evaluate

3. To evaluate the finetuned BLIP model on NoCaps, generate results with: (evaluation needs to be performed on official server)

python -m torch.distributed.run --nproc_per_node=8 eval_nocaps.py 

4. To finetune the pre-trained checkpoint using 8 A100 GPUs, first set ‘pretrained’ in configs/caption_coco.yaml as “https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model*_base.pth“. Then run:

python -m torch.distributed.run --nproc_per_node=8 train_caption.py 

VQA:

1. Download VQA v2 dataset and Visual Genome dataset from the original websites, and set ‘vqa_root’ and ‘vg_root’ in configs/vqa.yaml.
2. To evaluate the finetuned BLIP model, generate results with: (evaluation needs to be performed on official server)

python -m torch.distributed.run --nproc_per_node=8 train_vqa.py --evaluate

3. To finetune the pre-trained checkpoint using 16 A100 GPUs, first set ‘pretrained’ in configs/vqa.yaml as “https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model*_base.pth“. Then run:

python -m torch.distributed.run --nproc_per_node=16 train_vqa.py 

NLVR2:

1. Download NLVR2 dataset from the original websites, and set ‘image_root’ in configs/nlvr.yaml.
2. To evaluate the finetuned BLIP model, run

python -m torch.distributed.run --nproc_per_node=8 train_nlvr.py --evaluate

3. To finetune the pre-trained checkpoint using 16 A100 GPUs, first set ‘pretrained’ in configs/nlvr.yaml as “https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base.pth“. Then run:

python -m torch.distributed.run --nproc_per_node=16 train_nlvr.py 

Pre-train:

1. Prepare training json files where each json file contains a list. Each item in the list is a dictonary with two key-value pairs: {‘image’: path_of_image, ‘caption’: text_of_image}.
2. In configs/pretrain.yaml, set ‘train_file’ as the paths for the json files .
3. Pre-train the model using 8 A100 GPUs:

python -m torch.distributed.run --nproc_per_node=8 pretrain.py --config ./configs/Pretrain.yaml --output_dir output/Pretrain 

Pre-training datasets download:

We provide bootstrapped pre-training datasets as json files. Each json file contains a list. Each item in the list is a dictonary with two key-value pairs: {‘url’: url_of_image, ‘caption’: text_of_image}.

Citation

If you find this code to be useful for your research, please consider citing.

@misc{li2022blip,
      title={BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation}, 
      author={Junnan Li and Dongxu Li and Caiming Xiong and Steven Hoi},
      year={2022},
      eprint={2201.12086},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Acknowledgement

The implementation of BLIP relies on resources from ALBEF, Huggingface Transformers, and timm. We thank the original authors for their open-sourcing.

GitHub

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