Comments (3)
Hi,
- The 2 corresponds to the pooling layer of the first 12 net, since the pooling layer scales the input image down by a factor of 2.
Yes, narrowing the image means finding larger faces, since if you want to find smaller faces, you can just decrease the min_face_size argument of the detect_faces_net function.
The number of detection windows generated can be calculated as follows:
We wish to find 40 × 40 faces, so we first downscale the original image by a factor of 12/40, which results in an image of size 240 x 180, generating ((240 - 40) / 4 + 1) * ((180 - 40) / 4 + 1) = 1836 windows, and depending on the resizing factor for creating the pyramid, the number of detection windows may vary.
In 12 net it says 12 × 12 detection windows,is it because the net input is 12*12 so the window is 12?
Yes
is 4-pixel spacing corresponding to the train_val.prototxt and how the 4 is been calculated?
The spacing can be any value, because according to the description of the original paper, crops are taken out of the image and fed into the 12-net one at a time.
However, I have modified the 12-net to be a fully convolutional neural network, such that much redundant work can be saved.
You can take a look at this link if you're interested.
No welcome, I'm from Taiwan!
from cnn_face_detection.
Thank you and why the factor is calculated by 12/40,what is the meaning of the factor?
I put a 466 x 699 image to the network after resize_image it is 139 x209 and the (out = net_12c_full_conv.blobs['prob'].data[0][1, :, :]) out.shape is 64*99, is each confidence point in 64 x99 means the possibility of a face and if so why a point can represent a rectangle?
In the paper it says the image pyramid is resized by 12/F ,is it means w x12/F,h x12/F?
I use 1W face image and 1W background image without face to train the 12net, is it enough?
from cnn_face_detection.
The factor resizes the image such that after resizing, each 12 x 12 block corresponds to min_face_size x min_face_size block in original image.
why a point can represent a rectangle?
A point in the last output feature map represents a 12 x 12 block in resized image, which in turn corresponds to min_face_size x min_face_size block in original image.
In the paper it says the image pyramid is resized by 12/F ,is it means w x12/F,h x12/F?
Yes.
I use 1W face image and 1W background image without face to train the 12net, is it enough?
Yes, I think this is enough.
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
- 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 training step HOT 1
- 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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