https://github.com/artificialzeng/blip

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

https://github.com/artificialzeng/blip

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

https://github.com/ArtificialZeng/BLIP/blob/main/

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

## Announcement: BLIP is now officially integrated into [LAVIS](https://github.com/salesforce/LAVIS) - a one-stop library for language-and-vision research and applications!



This is the PyTorch code of the BLIP paper [[blog](https://blog.salesforceairesearch.com/blip-bootstrapping-language-image-pretraining/)]. The code has been tested on PyTorch 1.10.
To install the dependencies, run 
pip install -r requirements.txt
 

Catalog:
- [x] Inference demo
- [x] Pre-trained and finetuned checkpoints
- [x] Finetuning code for Image-Text Retrieval, Image Captioning, VQA, and NLVR2
- [x] Pre-training code
- [x] Zero-shot video-text retrieval
- [x] Download of bootstrapped pre-training datasets 


### Inference demo:
Run our interactive demo using [Colab notebook](https://colab.research.google.com/github/salesforce/BLIP/blob/main/demo.ipynb) (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](https://huggingface.co/spaces/Salesforce/BLIP), integrated into [Huggingface Spaces ](https://huggingface.co/spaces) using [Gradio](https://github.com/gradio-app/gradio). 

Replicate web demo and Docker image is also available at [![Replicate](https://replicate.com/salesforce/blip/badge)](https://replicate.com/salesforce/blip)

### 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
 
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_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
 
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_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
 
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 
 

### 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](https://github.com/dmlc/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.

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