Faster RCNN with PyTorch
Note: I re-implemented faster rcnn in this project when I started learning PyTorch. Then I use PyTorch in all of my projects. I still remember it costed one week for me to figure out how to build cuda code as a pytorch layer :). But actually this is not a good implementation and I didn't achieve the same mAP as the original caffe code.
This project is no longer maintained and may not compatible with the newest pytorch (after 0.4.0). So I suggest:
- You can still read and study this code if you want to re-implement faster rcnn by yourself;
- You can use the better PyTorch implementation by ruotianluo or Detectron.pytorch if you want to train faster rcnn with your own data;
For details about R-CNN please refer to the paper Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks by Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun.
- Forward for detecting
- RoI Pooling layer with C extensions on CPU (only forward)
- RoI Pooling layer on GPU (forward and backward)
- Training on VOC2007
- TensroBoard support
Installation and demo
Install the requirements (you can use pip or Anaconda):
conda install pip pyyaml sympy h5py cython numpy scipy conda install -c menpo opencv3 pip install easydict
Clone the Faster R-CNN repository
git clone email@example.com:longcw/faster_rcnn_pytorch.git
Build the Cython modules for nms and the roi_pooling layer
cd faster_rcnn_pytorch/faster_rcnn ./make.sh
Download the trained model VGGnet_fast_rcnn_iter_70000.h5 and set the model path in
Training on Pascal VOC 2007
Follow this project (TFFRCNN) to download and prepare the training, validation, test data and the VGG16 model pre-trained on ImageNet.
Since the program loading the data in
faster_rcnn_pytorch/data by default,
you can set the data path as following.
cd faster_rcnn_pytorch mkdir data cd data ln -s $VOCdevkit VOCdevkit2007
Then you can set some hyper-parameters in
train.py and training parameters in the
Now I got a 0.661 mAP on VOC07 while the origin paper got a 0.699 mAP.
You may need to tune the loss function defined in
faster_rcnn/faster_rcnn.py by yourself.
Training with TensorBoard
With the aid of Crayon, we can access the visualisation power of TensorBoard for any deep learning framework.
To use the TensorBoard, install Crayon (https://github.com/torrvision/crayon)
use_tensorboard = True in
Set the path of the trained model in
cd faster_rcnn_pytorch mkdir output python test.py
License: MIT license (MIT)