ProbabilisticTeacher

KO | EN

gitlite — search

#hacktoberfest #react #llm #machine-learning #rust #deep-learning #docker #macos #nodejs #java #windows #awesome #pytorch #open-source

ProbabilisticTeacher

(★ 86)

An official implementation of ICML 2022 paper "Learning Domain Adaptive Object Detection with Probabilistic Teacher"."

ProbabilisticTeacher 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Learning Domain Adaptive Object Detection with Probabilistic Teacher

![pipeline](https://raw.githubusercontent.com/hikvision-research/ProbabilisticTeacher/main/teaser/pt.jpg)

This repo is the official implementation of ICML2022 paper "[Learning Domain Adaptive Object Detection with Probabilistic Teacher](https://arxiv.org/pdf/2206.06293.pdf)" by Meilin Chen, Weijie Chen, Shicai Yang, *et al*. If you have any problem about this work, please feel free to contact Meilin Chen (merlinis-at-zju.edu.cn) or Weijie Chen (chenweijie5-at-hikvision.com).

## Installation

### Prerequisites
```shell
pip install -r requirements.txt
```

### Install Detectron2
Follow the [INSTALL.md](https://github.com/facebookresearch/detectron2/blob/master/INSTALL.md) to install Detectron2. We use version: ```detectron2==0.5```

## Usage

### Data Preparation
Plz refer to [prepare_data.md](docs/prepare_data.md) for datasets preparation.

### Pretrained Model

We used VGG16 pre-trained on ImageNet for all experiments. You can download it to ```/path/to/project```:

- VGG16: [Dropbox](https://www.dropbox.com/s/s3brpk0bdq60nyb/vgg16_caffe.pth?dl=0), [VT Server](https://filebox.ece.vt.edu/~jw2yang/faster-rcnn/pretrained-base-models/vgg16_caffe.pth)

### Training
Plz refer to [get_started.md](docs/get_started.md) for detailed commands.

## Main Results
This code has been further improved,  achiving more superior adaptation performance than the results presented in the paper (about +1~2 mAP gains across the tasks, see exps logs for details).
|Adaptation Tasks |Methods|Model Weights | mAP50                 | Log |
| ---- | -------| ----- |------------------------------|------------------------------|
|CitysScape2FoggyCityscape| PT (ours) | [Google Drive](https://drive.google.com/drive/folders/1rMXAaJpgOOHycnGhL2RwLJaz6dspmb74?usp=sharing) | **31 &rArr; 47.1 (+16.1)**   | [Google Drive](https://drive.google.com/drive/folders/1rMXAaJpgOOHycnGhL2RwLJaz6dspmb74?usp=sharing) |
|CitysScape2BDD100k| PT (ours) | [Google Drive](https://drive.google.com/drive/folders/1rMXAaJpgOOHycnGhL2RwLJaz6dspmb74?usp=sharing) | **26.9 &rArr; 34.9 (+8.0)**  |[Google Drive](https://drive.google.com/drive/folders/1rMXAaJpgOOHycnGhL2RwLJaz6dspmb74?usp=sharing)  |
|KITTI2CitysScape| PT (ours) | [Google Drive](https://drive.google.com/drive/folders/1rMXAaJpgOOHycnGhL2RwLJaz6dspmb74?usp=sharing) | **46.4 &rArr; 60.2 (+13.8)** | [Google Drive](https://drive.google.com/drive/folders/1rMXAaJpgOOHycnGhL2RwLJaz6dspmb74?usp=sharing) |
|Sim10k2CitysScape|PT (ours) | [Google Drive](https://drive.google.com/drive/folders/1rMXAaJpgOOHycnGhL2RwLJaz6dspmb74?usp=sharing) | **44.5 &rArr; 55.1 (+10.6)** | [Google Drive](https://drive.google.com/drive/folders/1rMXAaJpgOOHycnGhL2RwLJaz6dspmb74?usp=sharing) |

## Citation

If you use Probabilistic Teacher in your research or wish to refer to the results published in the paper, please consider citing our paper:
```BibTeX
@inproceedings{chen2022learning,
  title={Learning Domain Adaptive Object Detection with Probabilistic Teacher},
  author={Chen, Meilin and Chen, Weijie and Yang, Shicai and Song, Jie and Wang, Xinchao and Zhang, Lei and Yan, Yunfeng and Qi, Donglian and Zhuang, Yueting and Xie, Di and others},
  booktitle={International Conference on Machine Learning},
  pages={3040--3055},
  year={2022},
  organization={PMLR}
}
```

## License

This project is released under the [Apache 2.0 license](./LICENSE). Other codes from open source repository follows the original distributive licenses.

## Acknowledgement

This project is built upon [Detectron2](https://github.com/facebookresearch/detectron2) and [Unbiased Teacher](https://github.com/facebookresearch/unbiased-teacher), and we'd like to appreciate for their excellent works.

ProbabilisticTeacher

ProbabilisticTeacher 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

ProbabilisticTeacher

ProbabilisticTeacher 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README