Comments (4)
Oh, I understand.
Thank you very much. You help me a lot.
from cnn_face_detection.
Hi,
For the training set, you can use the AFLW dataset for positive samples, and any images without faces for negative samples.
The reason why different sizes of crops are taken from images for training is to create as many negative samples as possible from a single image, and caffe will automatically convert them to 12*12 pixel images if set properly in the prototxt file.
from cnn_face_detection.
Oh, I see. Thank you very much.
And I have another question. Why do you save negative samples into different directory? Why don't just save all negative samples in the same folder?
from cnn_face_detection.
Oh, that's just for a silly reason, Nautilus on Ubuntu crashes when opening folders containing too many files, haha
from cnn_face_detection.
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
- 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 training step HOT 1
- About the test result HOT 1
- How to get the file face12c_full_conv.caffemodel HOT 2
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from cnn_face_detection.