added FacesetEnhancer
4.2.other) data_src util faceset enhance best GPU.bat
4.2.other) data_src util faceset enhance multi GPU.bat
FacesetEnhancer greatly increases details in your source face set,
same as Gigapixel enhancer, but in fully automatic mode.
In OpenCL build it works on CPU only.
Please consider a donation.
If you want, you can manually remove unnecessary angles from src faceset after sort by yaw.
Optimized sample generators (CPU workers). Now they consume less amount of RAM and work faster.
added
4.2.other) data_src/dst util faceset pack.bat
Packs /aligned/ samples into one /aligned/samples.pak file.
After that, all faces will be deleted.
4.2.other) data_src/dst util faceset unpack.bat
unpacks faces from /aligned/samples.pak to /aligned/ dir.
After that, samples.pak will be deleted.
Packed faceset load and work faster.
4.2.other) data_src util faceset metadata save.bat
saves metadata of data_src\aligned\ faces into data_src\aligned\meta.dat
4.2.other) data_src util faceset metadata restore.bat
restore metadata from 'meta.dat' to images
if image size different from original, then it will be automatically resized
You can greatly enhance face details of src faceset by using Topaz Gigapixel software.
example https://i.imgur.com/Gwee99L.jpg
Example of workflow:
1) run 'data_src util faceset metadata save.bat'
2) launch Topaz Gigapixel
3) open 'data_src\aligned\' and select all images
4) set output folder to 'data_src\aligned_topaz' (create folder in save dialog)
5) set settings as on screenshot https://i.imgur.com/kAVWMQG.jpg
you can choose 2x, 4x, or 6x upscale rate
6) start process images and wait full process
7) rename folders:
data_src\aligned -> data_src\aligned_original
data_src\aligned_topaz -> data_src\aligned
8) copy 'data_src\aligned_original\meta.dat' to 'data_src\aligned\'
9) run 'data_src util faceset metadata restore.bat'
images will be downscaled back to original size (256x256) preserving details
metadata will be restored
10) now your new enhanced faceset is ready to use !
More stable and precise version of the face transformation matrix.
Now full_faces are aligned with the upper and lateral boundaries of the frame,
result: fix of cutted mouth, increase area of the cheeks of side faces
before/after https://i.imgur.com/t9IyGZv.jpg
therefore, additional training is required for existing models.
Optionally, you can re-extract dst faces of your project, if they have problems with cutted mouth or cheeks.
improved model generalization, overall accuracy and sharpness
by using new 'Learning rate dropout' technique from paper https://arxiv.org/abs/1912.00144
An example of a loss histogram where this function is enabled after the red arrow:
https://i.imgur.com/3olskOd.jpg
This is sort method by absolute per pixel difference between all faces.
options:
Sort by similar? ( y/n ?:help skip:y ) :
if you choose 'n', then most dissimilar faces will be placed first.
Colombo
