Comments (2)
AlfredXiangWu
commented on May 13, 2024
The accuracy you obtained is abnormal.
Do you convert the value of pixels from [0, 255] to [0, 1]?
from face_verification_experiment.
zlinker
commented on May 13, 2024
okay…I did miss this preprocess step, it can reach about 93% on verification experiment now, thank you.
from face_verification_experiment.
Related Issues (20)
- There may be some errors in the 'labels' HOT 3
- Where do you get the 5 facial points for MSCELEB dataset. Do you use some other model to find the facial points ? HOT 1
- Xavier vs Gaussian HOT 1
- I feel the size of the trainning data doubt HOT 1
- no an issue but a doubt HOT 2
- about lfw labels in model C-LightenedCNN_C_lfw.mat HOT 3
- Error occurred when computing accuracy
- Query
- no such file in your clean list
- Eltwise层名字拼写错误有影响吗？
- MegaFace Evaluation: it generated 'OpenCV Error' when running experiment.py with own features HOT 1
- hello，your model A and B is small,why your model C is bigger than B by using the same cnn architecture HOT 1
- can't get the same result HOT 3
- How to use fine tuned model to do classification for an image in light CNN?
- Any architecture patent information HOT 1
- the issue about architecture
- LightenedCNN_A_deploy.prototxt
- The evaluation of CASIA-NIR2.0
- Request Pretrained lightenedCNN model in .mat format HOT 1
- Computation of rank 1
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