Comments (1)
So all the nets should be trained alone, but input data for training higher resolution nets are dependent on lower resolution ones.
Calibration nets are not dependent on each other, so you can train those first.
Negative images to feed into 24-net are the negative images classified as positive by 12-net, and negative images to feed into 48-net are the negative images classified as positive by the cascade of 12-net and 24-net.
Steps to train the nets can be found in the README.
You can ignore the files with quantize in them, those are just used for my implementation on hardware.
Training 12-net can fail several times without a good initialization, you can just try retraining a few times.
The accuracy should be around 0.9 - 0.95.
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Related Issues (20)
- approximate Threshold T1 and T2 HOT 4
- About the result after running HOT 4
- Number of face detected in 2002/07/19/big/img_352.jpg HOT 1
- About the training step HOT 4
- A question about the cascade cnn HOT 2
- About the face size in create_face_12c.sh HOT 6
- How to train calibration nets?
- 3000 images without any faces (negative images) HOT 2
- How to implement the Multi-resolution net structure HOT 5
- Speed Problem HOT 1
- AFLW new website can't find AFLW_Faces.txt HOT 1
- create negative_py HOT 9
- train_val.prototxt about FCN HOT 3
- many false face HOT 1
- calibration_AFLW.py new code HOT 5
- Resize images when creatiing LMDB file HOT 2
- About the test result HOT 1
- How to get the file face12c_full_conv.caffemodel HOT 2
- How to python caffe to test the model
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from cnn_face_detection.