Faster RCNN pytroch代码

# 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 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 **This project is no longer maintained.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](https://github.com/ruotianluo/pytorch-faster-rcnn) if you want to train faster rcnn with your own data; This is a [PyTorch](https://github.com/pytorch/pytorch) implementation of Faster RCNN. This project is mainly based on [py-faster-rcnn](https://github.com/rbgirshick/py-faster-rcnn) and [TFFRCNN](https://github.com/CharlesShang/TFFRCNN). For details about R-CNN please refer to the [paper](https://arxiv.org/abs/1506.01497) Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks by Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun. ### Progress - [x] Forward for detecting - [x] RoI Pooling layer with C extensions on CPU (only forward) - [x] RoI Pooling layer on GPU (forward and backward) - [x] Training on VOC2007 - [x] TensroBoard support - [x] Evaluation ### Installation and demo 0. Install the requirements (you can use pip or [Anaconda](https://www.continuum.io/downloads)): ``` conda install pip pyyaml sympy h5py cython numpy scipy conda install -c menpo opencv3 pip install easydict ``` 1. Clone the Faster R-CNN repository ```bash git clone git@github.com:longcw/faster_rcnn_pytorch.git ``` 2. Build the Cython modules for nms and the roi_pooling layer ```bash cd faster_rcnn_pytorch/faster_rcnn ./make.sh ``` 3. Download the trained model [VGGnet_fast_rcnn_iter_70000.h5](https://drive.google.com/open?id=0B4pXCfnYmG1WOXdpYVFybWxiZFE) and set the model path in `demo.py` 3. Run demo `python demo.py` ### Training on Pascal VOC 2007 Follow [this project (TFFRCNN)](https://github.com/CharlesShang/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. ```bash 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. ### Training with TensorBoard With the aid of [Crayon](https://github.com/torrvision/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`. ### Evaluation Set the path of the trained model in `test.py`. ```bash cd faster_rcnn_pytorch mkdir output python test.py ``` License: MIT license (MIT)