Person Re-Identification

Personal project for person reidentification. It uses YOLO-NAS for person detection while Centroid ReID for Person reidentification. The 2048 embeddings produced by Centroid ReID are then compared via Cosine Similarity.

Currently, Centroid ReID achieves SOTA performance on the Market1501 benchmark.

YOLO-NAS also outperforms YOLO-V6 & V8 in terms of mAP.

Video Feed

Based on the image queries, you can place them in either blacklist or whitelist under data. Then, run main.py to run the program.

The script is designed to be multithreaded. I have also created a switch-key. Press a if you would like to disable ReID on the feed.

How To Run

1. git pull https://github.com/harvestingmoon/PersonReID.git

2. pip install -r requirements.txt

3. Configure the path to your image queries via config.yaml

4. Download market1501_resnet50_256_128_epoch_120.ckpt and place it under /logs as well as resnet50-19c8e357.pth and place it under models

5. Place your blacklist and whitelist image queries under /data folder.

6. Run main.py There are mainly 3 files which I have created that made this possible yolo_engine.py , reid_engine.py and main.py

Links to weights

ResNet-50: https://download.pytorch.org/models/resnet50-19c8e357.pth

Trained Model weights for CTL benchmark: https://drive.google.com/drive/folders/1NWD2Q0JGasGm9HTcOy4ZqsIqK4-IfknK

Acknowledgements

Special thank you to the researchers for making the code open source.Below are the links to the original source code as well.

YOLO-NAS/ SuperGradients : https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md

CTL/ Centroids-REID: https://github.com/mikwieczorek/centroids-reid