Comments (1)
if you can only reach recall = 0.6, I.e., not detecting the other 40% objects, you still have MOTA values at all 40 recall values, which are just 0s at the other 16 recall values. So the overall AMOTA score will be low because it is averaged with many 0s, which means that the metric is penalizing systems with a low recall.
Instead, if you divide by 24 recall values (values that are larger than 0, ignore the other 16 0s), you are not penalizing an MOT system with a lower recall.
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Related Issues (20)
- HOTA
- C++ implement HOT 1
- Converting nuScenes detections to KITTI format missing correspondence files HOT 3
- Can you share the new change lists of the code this year?
- Question about the KITTI detections. HOT 2
- Project dependencies may have API risk issues
- Question about visualization HOT 2
- carefully followed provided installation instructions, got FileNotFoundError HOT 7
- real-time data HOT 4
- 无法评估的问题
- Detector/Training procedure to get the provided detections HOT 1
- 请问是否修改过工程 HOT 1
- A question about detection results which in data/KITTI/detection/
- Speed optimization and parameter choice for custom detector
- Do you have ab3dmot c++ version code? HOT 1
- a question about function"get_ego_traj" HOT 1
- 请问如何修改别的检测结果作为输入进行实验?
- 'Box3D' object has no attribute 'o'
- sAMOTA, AMOTA, AMOTP, MOTA
- How to use AB3DMOT with custom dataset?
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