Releases: mikel-brostrom/boxmot
Releases · mikel-brostrom/boxmot
Release v10.0.14
- All prints substituted by LOGGER
- Always print environment info, followed by args
- Fix passing multiple classes to
val.py
andevolve.py
for multi-class evaluation
Release v10.0.13
- 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
Release v10.0.12
- Fix google colab example
- Less verbose evaluation
- The evaluation process stops if a single subprocess fails.
Release v10.0.11
- 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:
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
Release v10.0.10
- configure non-yolov8 models for inference, based on input arguments
- add @torch.no_grad() to all ReID related inference methods
Release v10.0.9
- 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
Release v10.0.8
- Simultaneous pypi + release publishing in CI
v10.0.6
v.10.0.4
- Complete repo refactor
- PyPI package published
- Yolo-NAS example added
v10.0.0
- Tracking now available for all main computer vision tasks:
- detection
- segmentation
- pose estimation - Added DeepOCSORT to trackers
- Added LightMBN to ReID models
- Added ReID export compatibility for
--half
with--dynamic
, thanks to @Rm1n90 in #798 - ReID TFlite model export now available using
onnx2tf
instead of usingopenvino2tensorflow
- Deleted Yolov8 submodule in favour of a pure Yolov8 package implementation
- Complete
track.py
refactor - Simplified
val.py
- Log val and evolve results to tensorboard. Usage
tensorboard --logdir=./runs/evolve/
tensorboard --logdir=./runs/val/