Faster_R-CNN_pytorch

• Modifed based on longcw/faster_rcnn_pytorch .
• Implementation of Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.

Statement

• Based on longcw/faster_rcnn_pytorch (python2.7), I modified the code so that it can run on python3.6 and numpy 1.13.3. So the numpy version do not need to change into 1.11.
• Follow longcw's code, I draw some picture to understand the process of Faster R-CNN.

Thank longcw, his code helps me a lot to understand Faster R-CNN. Also the readme files of longcw and ruotianluo tell me how to build cython models. You can follow their code and readme file.

If there is something I can not write in my repertory, please contact me.

Environment and Compile

• python 3.6
• pytorch 0.3.0
• numpy 1.13.3
• opencv 3.3.0

Follow longcw/faster_rcnn_pytorch and ruotianluo/pytorch-faster-rcnn.

1. Install requirements (directly copy from longcw/faster_rcnn_pytorch/README.md).

   conda install pip pyyaml sympy h5py cython numpy scipy
   conda install -c menpo opencv3
   pip install easydict
   

2. Choose arch of GPU type. (directly copy from ruotianluo/pytorch-faster-rcnn/README.md)

GPU model	Architecture
TitanX (Maxwell/Pascal)	sm_52
GTX 960M	sm_50
GTX 1080 (Ti)	sm_61
Grid K520 (AWS g2.2xlarge)	sm_30
Tesla K80 (AWS p2.xlarge)	sm_37

modify the parameter arch in faster_rcnn_pytorch/faster_rcnn/make.sh

3. Build Cython models (directly copy from longcw/faster_rcnn_pytorch/README.md).

   cd faster_rcnn_pytorch/faster_rcnn
   ./make.sh

Run

You can follow longcw/faster_rcnn_pytorch .

• demo.py You need to download trained_weights(VGGnet_fast_rcnn_iter_70000.h5) and load it's weight.
• train.py You need to download VOC dataset and VGG_imagenet_pretrained_weight(VGG_imagenet.npy).
• test.py You need to download VOC dataset.

Process of Code