DeepSort integrates deep neural networks with traditional tracking methods to achieve robust and accurate tracking of objects in video streams. The algorithm leverages a combination of a deep appearance feature extractor and the Hungarian algorithm for data association.
| GPU | IXUCA SDK | Release | Branch |
|---|---|---|---|
| MR-V100 | 4.4.0 | 26.03 | release/26.03 |
| MR-V100 | 4.3.0 | 25.12 | release/25.12 |
Note: 请切换到与您的 SDK 版本对应的 Release 分支进行测试。请勿直接在 master 分支上运行测试，因为 master 分支可能包含与您的本地 SDK 版本不兼容的最新更改。
切换分支命令示例：
git checkout release/26.03
Pretrained model(ckpt.t7): https://drive.google.com/drive/folders/1xhG0kRH1EX5B9_Iz8gQJb7UNnn_riXi6
Dataset: https://zheng-lab.cecs.anu.edu.au/Project/project_reid.html to download the market1501 dataset.
pip3 install -r requirements.txtpython3 export.py --weight ckpt.t7 --output deepsort.onnx
# Use onnxsim optimize onnx model
onnxsim deepsort.onnx deepsort_opt.onnxexport DATASETS_DIR=/Path/to/market1501/# Accuracy
bash scripts/infer_deepsort_fp16_accuracy.sh
# Performance
bash scripts/infer_deepsort_fp16_performance.sh# Accuracy
bash scripts/infer_deepsort_int8_accuracy.sh
# Performance
bash scripts/infer_deepsort_int8_performance.sh| Model | BatchSize | Precision | FPS | Acc(%) |
|---|---|---|---|---|
| DeepSort | 32 | FP16 | 17164.67 | 99.32 |
| DeepSort | 32 | INT8 | 20399.12 | 99.29 |