- Modifed based on longcw/faster_rcnn_pytorch .
- Implementation of Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.
- 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.
- 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.
-
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 -
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
-
Build Cython models (directly copy from longcw/faster_rcnn_pytorch/README.md).
cd faster_rcnn_pytorch/faster_rcnn ./make.sh
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.
