mirror of https://github.com/JDAI-CV/fast-reid.git
feat(projects): Add DG-ReID project
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@ -8,6 +8,7 @@ FastReID is a research platform that implements state-of-the-art re-identificati
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## What's New
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- [Sep 2021] [DG-ReID](https://github.com/xiaomingzhid/sskd) is updated, you can check the [paper](https://arxiv.org/pdf/2108.05045.pdf).
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- [June 2021] [Contiguous parameters](https://github.com/PhilJd/contiguous_pytorch_params) is supported, now it can
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accelerate ~20%.
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- [May 2021] Vision Transformer backbone supported, see `configs/Market1501/bagtricks_vit.yml`.
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@ -0,0 +1,89 @@
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# Semi-Supervised Domain Generalizable Person Re-Identification (SSKD)
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## Introduction
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SSKD is implemented based on **FastReID v1.0.0**. You can refer to [sskd github link](https://github.com/xiaomingzhid/sskd) It provides a semi-supervised feature learning framework to learn domain-general representations. The framework is shown in
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<img src="images/framework.png" width="850" >
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## Dataset
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**FastHuman** is very challenging, as it contains more complex application scenarios and large-scale training, testing datasets. It has diverse images from different application scenarios including campus, airport, shopping mall, street, and railway station.
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It contains 447,233 labeled images of 40,061 subjects captured by 82 cameras. The details of FastHuman, you can refer to [paper](https://arxiv.org/pdf/2108.05045.pdf).
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| Source Domain | \#subjects | \#images | \#cameras | collection place |
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| ----- | :------: | :---------: | :----: | :------: |
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| CUHK03| 1,090 | 14,096 | 2 | campus |
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| SAIVT | 152 | 7,150 | 8 | buildings |
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| AirportALERT | 9,651 | 30,243 | 6 | airport |
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|iLIDS| 300 | 4,515 | 2 | airport |
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|PKU | 114 | 1,824 | 2 | campus |
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|PRAI | 1,580 | 39,481| 2 | aerial imagery |
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|SenseReID | 1,718 | 3,338 | 2 | unknown |
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|SYSU | 510 | 30,071 | 4 | campus |
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|Thermalworld | 409 | 8,103 | 1 | unknown |
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|3DPeS | 193 | 1,012 | 1 | outdoor |
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|CAVIARa | 72 | 1,220 | 1 | shopping mall |
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|VIPeR | 632 | 1,264 | 2 | unknown |
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|Shinpuhkan| 24 | 4,501 | 8 | unknown |
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|WildTrack | 313 | 33,979 | 7| outdoor |
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|cuhk-sysu | 11,934| 34,574 | 1| street |
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|LPW | 2,731 | 30,678 | 4 | street |
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|GRID | 1,025 | 1,275 | 8 | underground |
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|Total | 31,423| 246,049 | 57 | - |
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|Unseen Domain| \#subjects | \#images | \#cameras | collection place |
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| ----- | :------: | :---------: | :----: | :------: |
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|Market1501 | 1,501 | 32,217 | 6 | campus |
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|DukeMTMC | 1,812 | 36,441 | 8 | campus |
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|MSMT17 | 4,101 | 126,441| 15| campus |
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|PartialREID | 60 | 600| 6|campus |
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|PartialiLIDS | 119 | 238 | 2 | airport |
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|OccludedREID | 200 | 2,000| 5| campus |
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|CrowdREID | 845 | 3,257 | 11 | railway station|
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|Total | 8,638 | 201,184| 49 | - |
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**YouTube-Human** is a unlabeled human dataset. You can download the Street-View video from YouTube website, and the use the human detection algorithm ([centerX](https://github.com/JDAI-CV/centerX)) to obtain the human images.
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## Training & Evaluation
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The whole training process is divided into two stages:
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- Train a student model (r34-ibn) and a teacher model (r101_ibn), you can run:
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```bash
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python3 projects/Basic_Project/train_net.py --config-file projects/Basic_Project/configs/r34-ibn.yml --num-gpu 4
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python3 projects/Basic_Project/train_net.py --config-file projects/Basic_Project/configs/r101-ibn.yml --num-gpu 4
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```
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- Train the student model based unlabeled dataset and sskd, you can run:
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```bash
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python3 projects/SSKD/train_net.py --config-file projects/SSKD/configs/sskd.yml --num-gpu 4
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```
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### Results
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<img src="images/result1.png" width="550" >
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<img src="images/result2.png" width="500" >
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Other some experimental results you could find in our [arxiv paper](https://arxiv.org/pdf/2108.05045.pdf).
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## Reference Project
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- [fastreid](https://github.com/JDAI-CV/fast-reid)
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- [centerX](https://github.com/JDAI-CV/centerX)
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## Citation
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If you use **fastreid** or **sskd** in your research, please give credit to the following papers:
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```BibTeX
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@article{he2020fastreid,
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title={FastReID: A Pytorch Toolbox for General Instance Re-identification},
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author={He, Lingxiao and Liao, Xingyu and Liu, Wu and Liu, Xinchen and Cheng, Peng and Mei, Tao},
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journal={arXiv preprint arXiv:2006.02631},
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year={2020}
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}
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```
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```BibTeX
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@article{he2021semi,
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title={Semi-Supervised Domain Generalizable Person Re-Identification},
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author={He, Lingxiao and Liu, Wu and Liang, Jian and Zheng, Kecheng and Liao, Xingyu and Cheng, Peng and Mei, Tao},
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journal={arXiv preprint arXiv:2108.05045},
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year={2021}
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}
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```
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@ -14,7 +14,7 @@ Note that these are research projects, and therefore may not have the same level
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- [Attribute Recognition](https://github.com/JDAI-CV/fast-reid/tree/master/projects/FastAttr)
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- [Hyper-Parameters Optimization](https://github.com/JDAI-CV/fast-reid/tree/master/projects/FastTune)
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- [Overhaul Distillation](https://github.com/JDAI-CV/fast-reid/tree/master/projects/FastDistill)
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- Semi-Supervised Domain Generalizable Person Re-Identification [Code](https://github.com/xiaomingzhid/sskd)[paper](https://arxiv.org/pdf/2108.05045.pdf)
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- Semi-Supervised Domain Generalizable Person Re-Identification. [code](https://github.com/xiaomingzhid/sskd) and [paper](https://arxiv.org/pdf/2108.05045.pdf)
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# External Projects
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