This is an official implementation of our CVPR 2020 paper "HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation" (https://arxiv.org/abs/1908.10357)
MIT License
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Use valid to reason about the data, and the result looks wrong. #110
When using valid inference, I always get a lot of noise points, so I set the threshold DETECTION_THRESHOLD and changed it from 0.5 to 0.9, and the same graph did not get the result as expected.
When the threshold is raised, the interference is reduced, but the real results are filtered out.
When using valid inference, I always get a lot of noise points, so I set the threshold DETECTION_THRESHOLD and changed it from 0.5 to 0.9, and the same graph did not get the result as expected.
When the threshold is raised, the interference is reduced, but the real results are filtered out.
DETECTION_THRESHOLD=0.5
DETECTION_THRESHOLD=0.9