Comments (4)
Do you generate the training data and labels by yourself?
If so, what's the learning rate of your code?
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I check and modify the code generating training data.
Then i re-generate the training data and labels and train again. Although the loss of landmark seems to converge, the detected landmarks of faces are not correct.
from mtcnn.
i prepare my data and label like this:
image_path face face_box_cords landmarks
img1.jpg 1 0.12 0.21 0.34 0.27 [0.1,0.2,0.3,0.4,0.5,0.1,0.2,0.3,0.4,0.5]; //positive face
img2.jpg 1 0.02 0.12 0.24 0.17 [0.1,0.2,0.3,0.4,0.5,0.1,0.2,0.3,0.4,0.5]; // partial face
img3.jpg 0 0 0 0 0 [ 0, 0 , 0, 0 , 0 , 0 , 0 , 0 , 0 , 0 ]; // no face
......
......
i want to konw whether the image data (eg:img1.jpg ) is just a face image or face part with background part?
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@gobigrassland Hi,I met the same question . Have you solved the problem ? Although my landmarks loss converge at last it descend so slowly and it does badly on my landmark validation set.
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Related Issues (20)
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