Model - VGG-16 Nail part detection model for faster-RCNNs
Model - Hand and Foot Image Selector
Model - Fine Image Selector

VGG-16 Nail part detection model for faster-RCNNs
Requirements
- Linux (Ubuntu)
- NVIDIA GPU (GTX-1050 or better)
- BVLC PyCaffe
- py-faster RCNNs

Download
- VGG-16 nail part detection model : #1 FigShare (483 Mbytes)
- demo_nail.py : FigShare

How to use
1. It is difficult to compile a CPU-mode faster-rcnn on Windows operating system at present.
NVidia GPU with CUDA and cuDNN is required because it takes too much time to conduct CNNs training without GPU
We recommend to install py-faster-rcnn program (https://github.com/rbgirshick/py-faster-rcnn) which operated on Linux (http://ubuntu.com).
Installation Tutorial by Huangying : https://huangying-zhan.github.io/2016/09/22/detection-faster-rcnn.html

2. Download (VGG-16 nail part detection model) : #1 FigShare (483 Mbytes)
Model - VGG-16 nail part detection ; 2 outputs(class) : #0 background #1 nail

The VGG-16 nail part model was trained using information about the crop location on the nail part from the Asan A2 dataset as instructed by the following tutorials.
http://sgsai.blogspot.kr/2016/02/training-faster-r-cnn-on-custom-dataset.html
https://github.com/deboc/py-faster-rcnn/tree/master/help

3. We modified demo.py of py-faster-rcnn (https://github.com/rbgirshick/py-faster-rcnn/blob/master/tools/demo.py) to get the following image.

Download our demo_nail.py ( FigShare )





Model - Hand and Foot Image Selector
Requirements
- BVLC PyCaffe for Python example

Download
- Caffe Model + Python source + WIN64 EXE example : #1 FigShare (380 Mbytes)

How to use
ResNet-152; 3 outputs : #0 handfoot photos #1 others #2 index photos

We created a Windows 64bit executable using pyinstaller (http://www.pyinstaller.org).

Example)

1) Windows 64bit EXE example
c:\handfoot\demo.bat

2) Python example ; requirements - PyCaffe
handfoot.bat [source_path] [result_prefix]


Model - Fine Image Selector
Requirements
- BVLC PyCaffe

Download
- Model Fine image selector : #1 FigShare (207 Mbytes)

How to use
ResNet-152; 6 outputs
#0 nail
#1 non-focused or tilted nail
#2 skin photo, not nail
#3 skin photo, not nail
#4 index photo, or EMR screenshot
#5 non-skin photo, general internet images