Boxmot

- Inputs (det, im0) to all trackers are now `numpy.ndarray`
- Fix README custom model example
- Clean up classifiers in `setup.py`
- Add trackers instantiation pytest tests
- Lower CI time by ~2 minutes by using a more CPU friendly architecture

- Fix google colab example
- Less verbose evaluation
- The evaluation process stops if a single subprocess fails.

- Minimal requirements
- Avoid ultralytics installation by default, only if Yolov8 model is used for tracking
- Avoid SG installation by default, only if YOLO-NAS is used for tracking
- Update OpenVINO export
- 33% CI speedup by lower `imgsz` resolution
- Boxmot paths imports. From:

python
FILE = Path(__file__).resolve()
ROOT = FILE.parents[0].parents[0] repo root absolute path
EXAMPLES = FILE.parents[0] examples absolute path
WEIGHTS = EXAMPLES / 'weights'


in each example file, to:

`from boxmot.utils import ROOT, EXAMPLES, WEIGHTS`

- configure non-yolov8 models for inference, based on input arguments
- add torch.no_grad() to all ReID related inference methods

- Multi Yolo backend added (Ultralytics + SuperGradients)
- Unified yolo-nas, yolox and yolov8 tracking file
- - `python examples/track.py --yolo-model yolo_nas_s`- - `python examples/track.py --yolo-model yolo_nas_s`
- All YOLO models tested in CI

- Simultaneous pypi + release publishing in CI

v.10.0.6
- Fix conf not getting updated in DeepOCSORT: https://github.com/mikel-brostrom/yolo_tracking/issues/877
- Add comments to custom object detector example
- Fixed `example/track.py` Yolov8 bug. Filter out detections, sementation masks and pose estimations based on tracking results

v.10.1
- Complete repo refactor
- PyPI package published
- Yolo-NAS example added