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jhb86253817 / PIPNet

Efficient facial landmark detector
MIT License
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Only 19 of 21 landmarks in AFLW use? Why? Can not found settings in CPM. #17

Closed DefTruth closed 2 years ago

DefTruth commented 2 years ago

hi~ I have found that you only use 19 of 21 landmarks in AFLW, and point out that it's follow CPW. But, I can't found any information about AFLW setting in CPM's paper. Could you please release the detail indices of 19 landmarks you have used in you paper?

I see the code in train.py:

points_flip = [6, 5, 4, 3, 2, 1, 12, 11, 10, 9, 8, 7, 15, 14, 13, 18, 17, 16, 19]  # no 0 and 20
points_flip = (np.array(points_flip)-1).tolist()  # then from 0~18, total 19 points

so, it means that you have exclude 0 and 20 in AFLW ?

jhb86253817 commented 2 years ago

Hi, in the paper, we did not refer to CPM, but this work: Zhu S, Li C, Loy CC, Tang X (2016) Unconstrained face alignment via cascaded compositional learning. In: CVPR. Please check again.

DefTruth commented 2 years ago

Thanks~ PIPNet seems can get good performance in my tests 👍👍👍

jhb86253817 commented 2 years ago

Thank you for your interests~