Comments (2)
Thank you for the recommendation, and the addition in the README. I'm new to tracking and still finding it hard to use well. One more question for tracking: In the README sample, you call update on the current detections :
for image in images:
dets = detector(image)
online_targets = tracker.update(dets, info_imgs, img_size)
If you don't have a detection in the frame, do we skip tracking for that frame or call update with an empty list?
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We have just added the tutorials of how to combine BYTE with other detectors. Hope that can help you :)
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Related Issues (20)
- AttributeError: module 'numpy' has no attribute 'float' HOT 8
- tool/train.py 训练的是yolox模型是吗?bytetrack是不是不需要训练? HOT 1
- Minor possibility of boundary value problem
- I need to fine tune the pre trained (byte track default) model
- Evaluation of the modified algorithm
- Update class_id of tracked object HOT 1
- Support for DeepStream 6.4 HOT 1
- np.float errors in the trackers HOT 5
- yolox person detection result can not reproduce HOT 1
- How to use interpolation on MOT17 validation sets
- MOT17-13-FRCNN 0.0% NaN 44 0.0% 0.0% 100.0% 0.0% 100.0% 0.0% 0.0% 0.0% NaN 3156 HOT 9
- Using my own YOLOX model trained with official YOLOX repo, but demo_track.py outputs nothing HOT 4
- Tracking Outputs vs Detection Outputs HOT 1
- Id switches when object is not detected for a frame then returns
- 状态为TrackState.Lost的Tracker不参与第二次低置信度bbox的匹配? HOT 1
- validation set selection
- Publicly disclose the size and high-resolution detection threshold of the test images for each sequence of MOT17 and MOT20🚀
- CityPerson dataset unavailable
- GPU issues in training HOT 2
- runing ByteTrack ncnn cpp throws error
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