R-CNN VGG nail plate detect model

Requirements- Linux (Ubuntu)<br>- NVIDIA GPU (GTX-1050 or better)<br>- BVLC PyCaffe<br>- py-faster RCNNs<br><br>Download- VGG-16 nail part detection model<br>- demo_nail.py<br><br>How to use1. It is difficult to compile a CPU-mode faster-rcnn on Windows operating system at present. <br>NVidia GPU with CUDA and cuDNN is required because it takes too much time to conduct CNNs training without GPU<br>We recommend to install py-faster-rcnn program (https://github.com/rbgirshick/py-faster-rcnn) which operated on Linux (http://ubuntu.com).<br>Installation Tutorial by Huangying : https://huangying-zhan.github.io/2016/09/22/detection-faster-rcnn.html<br><br>2. Download (VGG-16 nail part detection model) <br>Model - VGG-16 nail part detection ; 2 outputs(class) : #0 background #1 nail<br><br>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.<br>http://sgsai.blogspot.kr/2016/02/training-faster-r-cnn-on-custom-dataset.html<br>https://github.com/deboc/py-faster-rcnn/tree/master/help<br><br>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.<br><br>Download our demo_nail.py ( #1 Main Server; US East ) <br>