https://github.com/ataraxialab/faster_rcnn_pytorch

Faster RCNN with PyTorch

https://github.com/ataraxialab/faster_rcnn_pytorch

Repository

Faster RCNN with PyTorch

https://github.com/ataraxialab/faster_rcnn_pytorch/blob/dev/

# Faster RCNN with PyTorch
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
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 on Imagenet 2015

Since the image sizes and bbox sizes in the Imagenet DB have conflicts with Pascal VOC DB, 
before training, we have to clean those images with conflicts first.
```bash
python image_preprocess.py
```

Then we can start training with the remaining images. 

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 $imagenetdevkit imagenetdevkit2015
```

Then you can set some hyper-parameters in `train.py` and training parameters in the `.yml` file.

### 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
```

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