https://github.com/Shengcao-Cao/HASSOD

[NeurIPS 2023] HASSOD: Hierarchical Adaptive Self-Supervised Object Detection
https://github.com/Shengcao-Cao/HASSOD

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[NeurIPS 2023] HASSOD: Hierarchical Adaptive Self-Supervised Object Detection

• Host: GitHub
• URL: https://github.com/Shengcao-Cao/HASSOD
• Owner: Shengcao-Cao
• License: apache-2.0
• Created: 2023-10-28T04:00:49.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-02-08T01:23:37.000Z (9 months ago)
• Last Synced: 2024-06-19T02:49:23.671Z (5 months ago)
• Language: Python
• Homepage: https://hassod-neurips23.github.io/
• Size: 7.76 MB
• Stars: 44
• Watchers: 2
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• Awesome-Transformer-Attention - [Paper - Cao/HASSOD)][[Website](https://hassod-neurips23.github.io/)] (Detection / Other Detection Tasks)

README

        
# HASSOD: Hierarchical Adaptive Self-Supervised Object Detection

This is the official PyTorch implementation of our *NeurIPS 2023* paper:

**HASSOD: Hierarchical Adaptive Self-Supervised Object Detection**

[[Project Page]](https://hassod-neurips23.github.io/) [[Paper-arXiv]](https://arxiv.org/abs/2402.03311) [[Paper-OpenReview]](https://openreview.net/pdf?id=sqkGJjIRfG) [[Video-YouTube]](https://www.youtube.com/watch?v=s8u7tEKg5ew) [[Video-Bilibili]](https://www.bilibili.com/video/BV1pg4y1Z7CK)

[Shengcao Cao](https://shengcao-cao.github.io/), [Dhiraj Joshi](https://research.ibm.com/people/dhiraj-joshi), [Liang-Yan Gui](https://cs.illinois.edu/about/people/faculty/lgui), [Yu-Xiong Wang](https://yxw.web.illinois.edu/)

## 🔎 Overview

![HASSOD-gif](assets/HASSOD.gif)

HASSOD is a fully self-supervised approach for object detection and instance segmentation, demonstrating a significant improvement over the previous state-of-the-art methods by discovering a more comprehensive range of objects. Moreover, HASSOD understands the part-to-whole object composition like humans do, while previous methods cannot. Notably, we improve class-agnostic Mask AR from 20.2 to 22.5 on LVIS, and from 17.0 to 26.0 on SA-1B. 

## 🛠️ Instructions

To use our code and reproduce the results, please follow these detailed documents step by step:

- [Preparation](https://github.com/Shengcao-Cao/HASSOD/blob/main/preparation.md): Prepare the environment, data, and pre-trained models

- [Reproduction](https://github.com/Shengcao-Cao/HASSOD/blob/main/reproduction.md): Produce pseudo-labels and train the object detector (download links included for our pseudo-labels and model)

- [Demo](https://github.com/Shengcao-Cao/HASSOD/blob/main/demo.md): Once the preparation is finished, you can try out the demo code and test our model on any image.

## 🙏 Acknowledgements

Our code is developed based on the following repositories:

- [CutLER](https://github.com/facebookresearch/CutLER)

- [Unbiased Teacher](https://github.com/facebookresearch/unbiased-teacher)

- [Detectron2](https://github.com/facebookresearch/detectron2)

We greatly appreciate their open-source work!

## ⚖️ License

This project is released under the Apache 2.0 license. Other codes from open source repository follows the original distributive licenses.

## 🌟 Citation

If you find our research interesting or use our code, data, or model in your research, please consider citing our work.

```

@inproceedings{cao2023hassod,

    title={{HASSOD}: Hierarchical Adaptive Self-Supervised Object Detection},

    author={Cao, Shengcao and Joshi, Dhiraj and Gui, Liangyan and Wang, Yu-Xiong},

    booktitle={NeurIPS},

    year={2023}

}

```