https://github.com/tinyvision/solider-pedestriandetection
https://github.com/tinyvision/solider-pedestriandetection
Last synced: 10 months ago
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- Host: GitHub
- URL: https://github.com/tinyvision/solider-pedestriandetection
- Owner: tinyvision
- License: apache-2.0
- Created: 2023-03-28T02:20:14.000Z (almost 3 years ago)
- Default Branch: master
- Last Pushed: 2023-04-03T07:41:46.000Z (almost 3 years ago)
- Last Synced: 2025-03-27T01:23:51.700Z (11 months ago)
- Language: Python
- Size: 22.6 MB
- Stars: 10
- Watchers: 1
- Forks: 1
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
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README
# SOLIDER on [Pedestrian Detection]
[](https://paperswithcode.com/sota/pedestrian-detection-on-citypersons?p=beyond-appearance-a-semantic-controllable)
This repo provides details about how to use [SOLIDER](https://github.com/tinyvision/SOLIDER) pretrained representation on pedestrian detection task.
We modify the code from [Pedestron](https://github.com/hasanirtiza/Pedestron), and you can refer to the original repo for more details.
## Installation and Datasets
Details of installation and dataset preparation can be found in [Pedestron](https://github.com/hasanirtiza/Pedestron).
## Prepare Pre-trained Models
You can download models from [SOLIDER](https://github.com/tinyvision/SOLIDER), or use [SOLIDER](https://github.com/tinyvision/SOLIDER) to train your own models.
Before training, you should use `convert_model_version.py` to convert the pretrained models to the right version.
## Training
Train with single GPU:
```shell
python tools/train.py ${CONFIG_FILE}
```
Train with multiple GPUs:
```shell
./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [optional arguments]
```
`CONFIG_FILE` can be found in `configs/solider`.
Test:
```shell
sh run.sh
```
## Performance
| Method | Model | CityPersons
(MR-2) |
| ------ | :---: | :---: |
| SOLIDER | Swin Tiny | 10.3/40.8 |
| SOLIDER | Swin Small | 10.0/39.2 |
| SOLIDER | Swin Base | 9.7/39.4 |
- We use the pretrained models from [SOLIDER](https://github.com/tinyvision/SOLIDER).
- The semantic weight is set to 1.0 in these experiments.
## Citation
If you find this code useful for your research, please cite our paper
```
@inproceedings{chen2023beyond,
title={Beyond Appearance: a Semantic Controllable Self-Supervised Learning Framework for Human-Centric Visual Tasks},
author={Weihua Chen and Xianzhe Xu and Jian Jia and Hao Luo and Yaohua Wang and Fan Wang and Rong Jin and Xiuyu Sun},
booktitle={The IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year={2023},
}
```