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

Announcement: BLIP is now officially integrated into LAVIS - a one-stop library for language-and-vision research and applications!

This is the PyTorch code of the BLIP paper [blog]. 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
  • Zero-shot video-text retrieval
  • 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
  4. Image-text matching

Try out the Web demo, integrated into Huggingface Spaces 🤗 using Gradio.

Replicate web demo and Docker image is also available at Replicate

Pre-trained checkpoints:

Num. pre-train images BLIP w/ ViT-B BLIP w/ ViT-B and CapFilt-L BLIP w/ ViT-L
14M Download - -
129M Download Download Download

Finetuned checkpoints:

Task BLIP w/ ViT-B BLIP w/ ViT-B and CapFilt-L BLIP w/ ViT-L
Image-Text Retrieval (COCO) Download - Download
Image-Text Retrieval (Flickr30k) Download - Download
Image Captioning (COCO) - Download Download
VQA Download Download -
NLVR2 Download - -

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
  1. 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
  1. 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
  1. 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_capfilt_large.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
  1. 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_capfilt_large.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
  1. 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

Finetune with ViT-L:

In order to finetune a model with ViT-L, simply change the config file to set 'vit' as large. Batch size and learning rate may also need to be adjusted accordingly (please see the paper's appendix for hyper-parameter details). Gradient checkpoint can also be activated in the config file to reduce GPU memory usage.

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

Zero-shot video-text retrieval:

  1. Download MSRVTT dataset following the instructions from https://github.com/salesforce/ALPRO, and set 'video_root' accordingly in configs/retrieval_msrvtt.yaml.
  2. Install decord with 
    pip install decord
  3. To perform zero-shot evaluation, run
python -m torch.distributed.run --nproc_per_node=8 eval_retrieval_video.py

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}.

Image source Filtered web caption Filtered synthetic caption by ViT-B Filtered synthetic caption by ViT-L
CC3M+CC12M+SBU Download Download Download
LAION115M Download Download Download

Citation

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

@inproceedings{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},
      booktitle={ICML},
}

Acknowledgement

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