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 mIOU as the original caffe code.

This project is no longer maintained. 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 if you want to train faster rcnn with your own data;

This is a PyTorch implementation of Faster RCNN. This project is mainly based on py-faster-rcnn and TFFRCNN.

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
• Evaluation

0. 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
   

1. Clone the Faster R-CNN repository

   git clone [email protected]:longcw/faster_rcnn_pytorch.git

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

   cd faster_rcnn_pytorch/faster_rcnn
   ./make.sh

3. Download the trained model VGGnet_fast_rcnn_iter_70000.h5 and set the model path in demo.py

4. Run demo python demo.py

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 .yml file.

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.

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) and set use_tensorboard = True in faster_rcnn/train.py.

Set the path of the trained model in test.py.

cd faster_rcnn_pytorch
mkdir output
python test.py

License: MIT license (MIT)