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This is aPyTorchimplementation of R-FCN for object detection.This project is mainly based onfaster_rcnn_pytorch

For details about R-FCN please refer to thepaperR-FCN: Object Detection via Region-based Fully Convolutional Networksby Jifeng Dai, Yi Li, Kaiming He, Jian Sun.

1. Clone the this repository

   git clone https://github.com/PureDiors/pytorch_RFCN.git

2. Build the Cython modules for nms and the psroi_pooling layer

   cd faster_rcnn_pytorch/faster_rcnn./make.sh

The psroi pooling layer is defined in the folder faster_rcnn/psroi_pooling,and the whole detection network is implemented in faster_rcnn/rfcn.py.

Followthis project (TFFRCNN)to download and prepare the training, validation, test dataand the VGG16 model pre-trained on ImageNet.

Since the program loading the data infaster_rcnn_pytorch/data by default,you can set the data path as following.

cd faster_rcnn_pytorchmkdir datacd dataln -s$VOCdevkit VOCdevkit2007

The speed for training the rfcn with VGG16 on a Nvidia Titan X(Pascal) is 4.9 fps, and 12 fps for testing.

You can set some hyper-parameters intrain.py and training parameters in the.yml file.

With the aid ofCrayon,we can access the visualisation power of TensorBoard for anydeep learning framework.

To use the TensorBoard, install Crayon (https://github.com/torrvision/crayon)and setuse_tensorboard = True infaster_rcnn/train.py.

Set the path of the trained model intest.py.

cd faster_rcnn_pytorchmkdir outputpython test.py