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MOT using deepsort and yolov3 with pytorch
| Date | Stars |
|---|---|
| 2026-07-24 | 3013 |
| 2026-07-25 | 3013 |
| 2026-07-28 | 3013 |
| 2026-07-30 | 3013 |
| 2026-08-06 | 3013 |
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# Deep Sort with PyTorch  ## Update(1-1-2020) Changes - fix bugs - refactor code - accerate detection by adding nms on gpu ## Update(07-22) Changes - bug fix (Thanks @JieChen91 and @yingsen1 for bug reporting). - using batch for feature extracting for each frame, which lead to a small speed up. - code improvement. Futher improvement direction - Train detector on specific dataset rather than the official one. - Retrain REID model on pedestrain dataset for better performance. - Replace YOLOv3 detector with advanced ones. ## Update(23-05-2024) ### tracking - Added resnet network to the appearance feature extraction network in the deep folder - Fixed the NMS bug in the `preprocessing.py` and also fixed covariance calculation bug in the `kalmen_filter.py` in the sort folder ### detecting - Added YOLOv5 detector, aligned interface, and added YOLOv5 related yaml configuration files. Codes references this repo: [YOLOv5-v6.1](https://github.com/ultralytics/yolov5/tree/v6.1). - The `train.py`, `val.py` and `detect.py` in the original YOLOv5 were deleted. This repo only need **yolov5x.pt**. ### deepsort - Added tracking target category, which can display both category and tracking ID simultaneously. ## Update(28-05-2024) ### segmentation * Added Mask RCNN instance segmentation model. Codes references this repo: [mask_rcnn](https://github.com/WZMIAOMIAO/deep-learning-for-image-processing/tree/master/pytorch_object_detection/mask_rcnn). Visual result saved in `demo/demo2.gif`. * Similar to YOLOv5, `train.py`, `validation.py` and `predict.py` were deleted. This repo only need **maskrcnn_resnet50_fpn_coco.pth**. ### deepsort - Added tracking target mask, which can display both category, tracking ID and target mask simultaneously. ## latest Update(09-06-2024) ### feature extraction network * Using `nn.parallel.DistributedDataParallel` in PyTorch to support multiple GPUs training. * Added [GETTING_STARTED.md](deep_sort/deep/GETTING_STARTED.md) for better using `train.py` and `train_multiGPU.py`. Updated `README.md` for previously updated content(#Update(23-05-2024) and #Update(28-05-2024)). **Any contributions to this repository is welcome!** ## Introduction This is an implement of MOT tracking algorithm deep sort. Deep sort is basicly the same with sort but added a CNN model to extract features in image of human part bounded by a detector. This CNN model is indeed a RE-ID model and the detector used in [PAPER](https://arxiv.org/abs/1703.07402) is FasterRCNN , and the original source code is [HERE](https://github.com/nwojke/deep_sort). However in original code, the CNN model is implemented with tensorflow, which I'm not familier with. SO I re-implemented the CNN feature extraction model with PyTorch, and changed the CNN model a little bit. Also, I use **YOLOv3** to generate bboxes instead of FasterRCNN. ## Dependencies - python 3 **(python2 not sure)** - numpy - scipy - opencv-python - sklearn - torch >= 1.9 - torchvision >= 0.13 - pillow - vizer - edict - matplotlib - pycocotools - tqdm ## Quick Start 0. Check all dependencies installed ```bash pip install -r requirements.txt ``` for user in china, you can specify pypi source to accelerate install like: ```bash pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple ``` 1. Clone this repository ```bash git clone [email protected]:ZQPei/deep_sort_pytorch.git ``` 2. Download detector parameters ```bash # if you use YOLOv3 as detector in this repo cd detector/YOLOv3/weight/ wget https://pjreddie.com/media/files/yolov3.weights wget https://pjreddie.com/media/files/yolov3-tiny.weights cd ../../../ # if you use YOLOv5 as detector in this repo cd detector/YOLOv5 wget https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5s.pt or wget https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5m.pt cd ../../ # if you use Mask RCNN as detector in this repo cd detector/Mask_RCNN/save_weights wget https://down
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:41f71fd588494665, topic:pytorch