README.md

# BLIP-2

> [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](http://arxiv.org/abs/2301.12597)

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## Abstract

The cost of vision-and-language pre-training has become increasingly prohibitive due to end-toend training of large-scale models. This paper proposes BLIP-2, a generic and efficient pretraining strategy that bootstraps vision-language pre-training from off-the-shelf frozen pre-trained image encoders and frozen large language models. BLIP-2 bridges the modality gap with a lightweight Querying Transformer, which is pretrained in two stages. The first stage bootstraps vision-language representation learning from a frozen image encoder. The second stage bootstraps vision-to-language generative learning from a frozen language model. BLIP-2 achieves state-of-the-art performance on various visionlanguage tasks, despite having significantly fewer trainable parameters than existing methods. For example, our model outperforms Flamingo80B by 8.7% on zero-shot VQAv2 with 54x fewer trainable parameters. We also demonstrate the model’s emerging capabilities of zero-shot image-to-text generation that can follow natural language instructions.

<div align=center>
<img src="https://user-images.githubusercontent.com/30762564/236385045-dc22a621-0a9c-4352-afa4-ca3888044850.png" width="70%"/>
</div>

## How to use it?

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**Use the model**

```python
from mmpretrain import inference_model

result = inference_model('blip2-opt2.7b_3rdparty-zeroshot_caption', 'demo/cat-dog.png')
print(result)
# {'pred_caption': 'a dog and a cat sitting on a blanket'}
```

**Test Command**

Prepare your dataset according to the [docs](https://mmpretrain.readthedocs.io/en/latest/user_guides/dataset_prepare.html#prepare-dataset).

Test:

```shell
python tools/test.py configs/blip2/blip2_8xb32_retrieval.py https://download.openmmlab.com/mmclassification/v1/blip2/blip2_3rdparty_pretrain_20230505-f7ef4390.pth
```

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## Models and results

### Image Caption on COCO

| Model                                       | Params (M) | BLEU-4 | CIDER  |                   Config                   |                                           Download                                            |
| :------------------------------------------ | :--------: | :----: | :----: | :----------------------------------------: | :-------------------------------------------------------------------------------------------: |
| `blip2-opt2.7b_3rdparty-zeroshot_caption`\* |  3770.47   | 32.90  | 111.10 | [config](./blip2-opt2.7b_8xb32_caption.py) | [model](https://download.openmmlab.com/mmclassification/v1/blip2/blip2-opt2.7b_3rdparty_pretrain_20230505-b51db4e1.pth) |

### Visual Question Answering on VQAv2

| Model                                   | Params (M) | Accuracy |                 Config                 |                                                 Download                                                  |
| :-------------------------------------- | :--------: | :------: | :------------------------------------: | :-------------------------------------------------------------------------------------------------------: |
| `blip2-opt2.7b_3rdparty-zeroshot_vqa`\* |  3770.47   |  53.50   | [config](./blip2-opt2.7b_8xb16_vqa.py) | [model](https://download.openmmlab.com/mmclassification/v1/blip2/blip2-opt2.7b_3rdparty_pretrain_20230505-b51db4e1.pth) |

### Image-To-Text Retrieval on COCO

| Model                        | Params (M) | Recall@1 |                Config                |                                                    Download                                                     |
| :--------------------------- | :--------: | :------: | :----------------------------------: | :-------------------------------------------------------------------------------------------------------------: |
| `blip2_3rdparty_retrieval`\* |  1173.19   |  85.40   | [config](./blip2_8xb32_retrieval.py) | [model](https://download.openmmlab.com/mmclassification/v1/blip2/blip2_3rdparty_pretrain_20230505-f7ef4390.pth) |

*Models with * are converted from the [official repo](https://github.com/salesforce/LAVIS). The config files of these models are only for inference. We haven't reproduce the training results.*

## Citation

```bibtex
@article{beitv2,
    title={Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models},
    author={Li, Junnan and Li, Dongxu and Savarese, Silvio and Hoi, Steven},
    year={2023},
    eprint={2301.12597},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}
```