Comments (3)
When we don't use the feature normalization, it is still necessary to use annealing strategy to set the m like the lambda in SphereFace. So I need to change the code to add the annealing strategy. I will give an example tomorrow.
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The example is uploaded. You also need to update the label_specific_add_layer to get the annealing codes.
https://github.com/happynear/AMSoftmax/blob/master/prototxt/face_train_test_wo_fn.prototxt
Note that I am still running the experiment to reproduce the result, so the prototxt may be changed in the next a few hours...
By the way, I can get similar results on LFW BLUFR with normalization by finetuning the network using scale 60 and margin 0.4 after iteration 16000. Maybe you can also try this way.
from amsoftmax.
Thanks a lot! I will have a try.
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Related Issues (20)
- The way of using AMSoftmax HOT 4
- iter_size问题 HOT 1
- CASIA-Webface dataset download link HOT 31
- Compare AMSoftmax with ArcFace HOT 1
- Have you ever tried batch normalization? HOT 4
- lfw准确率为99.73是如何得到的?使用的什么数据集! HOT 12
- What is gnap prototxt for? HOT 1
- How to debug margin and scale? HOT 3
- auto margin HOT 5
- how does the loss converge HOT 1
- Where is fc6 in face_train_test.prototxt? HOT 2
- how to set the m and s when there are only 1000 identities? HOT 5
- Test Problem HOT 2
- when set a mini-batch to model input the time of calculation won't decreade
- flip in face_deploy_mirror_normalize.prototxt doesn't work properly
- Training without alignment
- high similarity between abnormal face images HOT 2
- I put inner_product_layer and LabelSpecificAdd together,but it gets worse result.
- Alignment of the image in realtime
- what is inner-product layer's output depended , How to calculate。
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