R-CNN VGG nail plate detect model
datasetposted on 18.10.2017 by Seung Seog Han
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
Requirements- Linux (Ubuntu)
- NVIDIA GPU (GTX-1050 or better)
- BVLC PyCaffe
- py-faster RCNNs
Download- VGG-16 nail part detection model
How to use1. 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)
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.
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 ( #1 Main Server; US East )