# ovdet

**Repository Path**: yang_he_qing/ovdet

## Basic Information

- **Project Name**: ovdet
- **Description**: fork from https://github.com/wusize/ovdet
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2023-05-26
- **Last Updated**: 2023-05-26

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Aligning Bag of Regions for Open-Vocabulary Object Detection

[//]: # ([![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/aligning-bag-of-regions-for-open-vocabulary/open-vocabulary-object-detection-on-mscoco)](https://paperswithcode.com/sota/open-vocabulary-object-detection-on-mscoco?p=aligning-bag-of-regions-for-open-vocabulary))

## Introduction

This is an official release of the paper **Aligning Bag of Regions for Open-Vocabulary Object Detection**.

> [**Aligning Bag of Regions for Open-Vocabulary Object Detection**](https://arxiv.org/abs/2302.13996),            
> Size Wu, Wenwei Zhang, Sheng Jin, Wentao Liu, Chen Change Loy           
> In: Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023           
> [[arXiv](https://arxiv.org/abs/2302.13996)][[project page(TBD)](https://www.mmlab-ntu.com/)][[Bibetex](https://github.com/wusize/ovdet#citation)]


## Installation

This project is based on [MMDetection 3.x](https://github.com/open-mmlab/mmdetection/tree/3.x)

It requires the following OpenMMLab packages:

- MMEngine >= 0.6.0
- MMCV-full >= v2.0.0rc4
- MMDetection >= v3.0.0rc6
- lvisapi

```bash
pip install torch==1.12.1+cu102 torchvision==0.13.1+cu102 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu102
pip install ftfy
pip install regex
pip install openmim mmengine
mim install "mmcv>=2.0.0rc4"
mim install mmdet
```

## License

This project is released under the [Apache 2.0 license](LICENSE).



## Usage
### Obtain CLIP Checkpoints
We use CLIP's ViT-B-32 model for the implementation of our method. Obtain the state_dict 
of the model from [GoogleDrive](https://drive.google.com/file/d/1ilxBhjb3JXNDar8lKRQ9GA4hTmjxADfu/view?usp=sharing) and 
put it under `checkpoints`. Otherwise, `pip install git+https://github.com/openai/CLIP.git` and
run
```python
import clip
import torch
model, _ = clip.load("ViT-B/32")
torch.save(model.state_dict(), 'checkpoints/clip_vitb32.pth')
```

### Training and Testing

The training and testing on [OV-COCO](configs/baron/ov_coco/README.md) are supported now.


## Citation

```bibtex
@inproceedings{wu2023baron,
    title={Aligning Bag of Regions for Open-Vocabulary Object Detection},
    author={Size Wu and Wenwei Zhang and Sheng Jin and Wentao Liu and Chen Change Loy},
    year={2023},
    booktitle={CVPR},
}
```