Model - VGG-16 Nail part detection model for faster-RCNNs
Model - Hand and Foot Image Selector
Model - Fine Image Selector
- NVIDIA GPU (GTX-1050 or better)
- BVLC PyCaffe
- py-faster RCNNs
- demo_nail.py : FigShare
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 )

We created a Windows 64bit executable using pyinstaller (http://www.pyinstaller.org).
c:\handfoot\demo.bat
2) Python example ; requirements - PyCaffe
handfoot.bat [source_path] [result_prefix]
#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
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 exampleDownload
- 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 photosWe created a Windows 64bit executable using pyinstaller (http://www.pyinstaller.org).
Example)
1) Windows 64bit EXE examplec:\handfoot\demo.bat
2) Python example ; requirements - PyCaffe
handfoot.bat [source_path] [result_prefix]
Model - Fine Image Selector
Requirements
- BVLC PyCaffeDownload
- 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