boxmot_rebuild
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Basic Info
- Host: GitHub
- Owner: HoangDuong-Devs
- License: agpl-3.0
- Language: Jupyter Notebook
- Default Branch: main
- Size: 53.5 MB
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Created 8 months ago
· Last pushed 8 months ago
Metadata Files
Readme
Contributing
License
Citation
README.md
BoxMOT: Pluggable SOTA multi-object tracking modules for segmentation, object detection and pose estimation models
[](https://github.com/mikel-brostrom/yolov8_tracking/actions/workflows/ci.yml) [](https://badge.fury.io/py/boxmot) [](https://pepy.tech/project/boxmot) [](https://github.com/mikel-brostrom/boxmot/blob/master/LICENSE) [](https://badge.fury.io/py/boxmot) [](https://colab.research.google.com/drive/18nIqkBr68TkK8dHdarxTco6svHUJGggY?usp=sharing) [](https://doi.org/10.5281/zenodo.8132989) [](https://hub.docker.com/r/boxmot/boxmot) [](https://discord.gg/3w4aYGbU)
🚀 Key Features
Pluggable Architecture
Easily swap in/out SOTA multi-object trackers.Universal Model Support
Integrate with any segmentation, object-detection and pose-estimation models that outputs bounding boxesBenchmark-Ready
Local evaluation pipelines for MOT17, MOT20, and DanceTrack ablation datasets with "official" ablation detectorsPerformance Modes
Reusable Detections & Embeddings
Save once, run evaluations with no redundant preprocessing lightning fast.
📊 Benchmark Results (MOT17 ablation split)
| Tracker | Status | HOTA↑ | MOTA↑ | IDF1↑ | FPS |
| :-----: | :-----: | :---: | :---: | :---: | :---: |
| [boosttrack](https://arxiv.org/abs/2408.13003) | ✅ | 69.253 | 75.914 | 83.206 | 25 |
| [botsort](https://arxiv.org/abs/2206.14651) | ✅ | 68.885 | 78.222 | 81.344 | 46 |
| [strongsort](https://arxiv.org/abs/2202.13514) | ✅ | 68.05 | 76.185 | 80.763 | 17 |
| [deepocsort](https://arxiv.org/abs/2302.11813) | ✅ | 67.796 | 75.868 | 80.514 | 12 |
| [bytetrack](https://arxiv.org/abs/2110.06864) | ✅ | 67.68 | 78.039 | 79.157 | 1265 |
| [ocsort](https://arxiv.org/abs/2203.14360) | ✅ | 66.441 | 74.548 | 77.899 | 1483 |
NOTES: Evaluation was conducted on the second half of the MOT17 training set, as the validation set is not publicly available and the ablation detector was trained on the first half. We employed [pre-generated detections and embeddings](https://github.com/mikel-brostrom/boxmot/releases/download/v11.0.9/runs2.zip). Each tracker was configured using the default parameters from their official repositories.
🔧 Installation
Install the boxmot package, including all requirements, in a Python>=3.9 environment:
bash
pip install boxmot
BoxMOT provides a unified CLI boxmot with the following subcommands:
```bash Usage: boxmot COMMAND [ARGS]...
Commands: track Run tracking only generate Generate detections and embeddings eval Evaluate tracking performance using the official trackeval repository tune Tune tracker hyperparameters based on selected detections and embeddings ```
If you want to contribute to this package check how to contribute here
📝 Code Examples & Tutorials
Tracking
```bash $ boxmot track --yolo-model rf-detr-base.pt # bboxes only boxmot track --yolo-model yolox_s.pt # bboxes only boxmot track --yolo-model yolo12n.pt # bboxes only boxmot track --yolo-model yolo11n.pt # bboxes only boxmot track --yolo-model yolov10n.pt # bboxes only boxmot track --yolo-model yolov9c.pt # bboxes only boxmot track --yolo-model yolov8n.pt # bboxes only yolov8n-seg.pt # bboxes + segmentation masks yolov8n-pose.pt # bboxes + pose estimation ```Tracking methods
```bash $ boxmot track --tracking-method deepocsort strongsort ocsort bytetrack botsort boosttrack ```Tracking sources
Tracking can be run on most video formats ```bash $ boxmot track --source 0 # webcam img.jpg # image vid.mp4 # video path/ # directory path/*.jpg # glob 'https://youtu.be/Zgi9g1ksQHc' # YouTube 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream ```Select ReID model
Some tracking methods combine appearance description and motion in the process of tracking. For those which use appearance, you can choose a ReID model based on your needs from this [ReID model zoo](https://kaiyangzhou.github.io/deep-person-reid/MODEL_ZOO). These model can be further optimized for you needs by the [reid_export.py](https://github.com/mikel-brostrom/yolo_tracking/blob/master/boxmot/appearance/reid_export.py) script ```bash $ boxmot track --source 0 --reid-model lmbn_n_cuhk03_d.pt # lightweight osnet_x0_25_market1501.pt mobilenetv2_x1_4_msmt17.engine resnet50_msmt17.onnx osnet_x1_0_msmt17.pt clip_market1501.pt # heavy clip_vehicleid.pt ... ```Filter tracked classes
By default the tracker tracks all MS COCO classes. If you want to track a subset of the classes that you model predicts, add their corresponding index after the classes flag, ```bash boxmot track --source 0 --yolo-model yolov8s.pt --classes 16 17 # COCO yolov8 model. Track cats and dogs, only ``` [Here](https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/) is a list of all the possible objects that a Yolov8 model trained on MS COCO can detect. Notice that the indexing for the classes in this repo starts at zeroEvaluation
Evaluate a combination of detector, tracking method and ReID model on standard MOT dataset or you custom one by ```bash # reproduce MOT17 README results $ boxmot eval --yolo-model yolox_x_MOT17_ablation.pt --reid-model lmbn_n_duke.pt --tracking-method boosttrack --source MOT17-ablation --verbose # MOT20 results $ boxmot eval --yolo-model yolox_x_MOT20_ablation.pt --reid-model lmbn_n_duke.pt --tracking-method boosttrack --source MOT20-ablation --verbose # Dancetrack results $ boxmot eval --yolo-model yolox_x_dancetrack_ablation.pt --reid-model lmbn_n_duke.pt --tracking-method boosttrack --source dancetrack-ablation --verbose # metrics on custom dataset $ boxmot eval --yolo-model yolov8n.pt --reid-model osnet_x0_25_msmt17.pt --tracking-method deepocsort --source ./assets/MOT17-mini/train --verbose ``` add `--gsi` to your command for postprocessing the MOT results by gaussian smoothed interpolation. Detections and embeddings are stored for the selected YOLO and ReID model respectively. They can then be loaded into any tracking algorithm. Avoiding the overhead of repeatedly generating this data.Evolution
We use a fast and elitist multiobjective genetic algorithm for tracker hyperparameter tuning. By default the objectives are: HOTA, MOTA, IDF1. Run it by ```bash # saves dets and embs under ./runs/dets_n_embs separately for each selected yolo and reid model $ boxmot generate --source ./assets/MOT17-mini/train --yolo-model yolov8n.pt yolov8s.pt --reid-model weights/osnet_x0_25_msmt17.pt # evolve parameters for specified tracking method using the selected detections and embeddings generated in the previous step $ boxmot tune --dets yolov8n --embs osnet_x0_25_msmt17 --n-trials 9 --tracking-method botsort --source ./assets/MOT17-mini/train ``` The set of hyperparameters leading to the best HOTA result are written to the tracker's config file.Export
We support ReID model export to ONNX, OpenVINO, TorchScript and TensorRT ```bash # export to ONNX $ python3 boxmot/appearance/reid_export.py --include onnx --device cpu # export to OpenVINO $ python3 boxmot/appearance/reid_export.py --include openvino --device cpu # export to TensorRT with dynamic input $ python3 boxmot/appearance/reid_export.py --include engine --device 0 --dynamic ```
| Example Description | Notebook |
|---------------------|----------|
| Torchvision bounding box tracking with BoxMOT | [](examples/det/torchvision_boxmot.ipynb) |
| Torchvision pose tracking with BoxMOT | [](examples/pose/torchvision_boxmot.ipynb) |
| Torchvision segmentation tracking with BoxMOT | [](examples/seg/torchvision_boxmot.ipynb) |
Contributors
Contact
For BoxMOT bugs and feature requests please visit GitHub Issues. For business inquiries or professional support requests please send an email to: box-mot@outlook.com
Owner
- Login: HoangDuong-Devs
- Kind: user
- Repositories: 1
- Profile: https://github.com/HoangDuong-Devs
Citation (CITATION.cff)
cff-version: 13.0.17
preferred-citation:
type: software
message: "If you use Yolo Tracking, please cite it as below."
authors:
- family-names: Broström
given-names: Mikel
title: "BoxMOT: pluggable SOTA tracking modules for object detection, segmentation and pose estimation models"
version: 13.0.17
doi: https://zenodo.org/record/7629840
date-released: 2024-6
license: AGPL-3.0
url: "https://github.com/mikel-brostrom/boxmot"
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Dependencies
Dockerfile
docker
- pytorch/pytorch 2.3.1-cuda11.8-cudnn8-runtime build
yolov7/utils/google_app_engine/Dockerfile
docker
- gcr.io/google-appengine/python latest build
pyproject.toml
pypi
uv.lock
pypi
- 191 dependencies
yolov7/requirements.txt
pypi
- Pillow >=7.1.2
- PyYAML >=5.3.1
- ipython *
- matplotlib >=3.2.2
- numpy >=1.18.5,<1.24.0
- opencv-python >=4.1.1
- pandas >=1.1.4
- protobuf <4.21.3
- psutil *
- requests >=2.23.0
- scipy >=1.4.1
- seaborn >=0.11.0
- tensorboard >=2.4.1
- thop *
- torch >=1.7.0,
- torchvision >=0.8.1,
- tqdm >=4.41.0
yolov7/utils/google_app_engine/additional_requirements.txt
pypi
- Flask ==1.0.2
- gunicorn ==19.9.0
- pip ==18.1