Jennyli-xin/Faster_RCNN_pytorch

Faster_RCNN_pytorch

A Python3.5/Pytroch implementation of Faster RCNN:Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. And the official implementations are available here. Besides, special thanks for those two repositories：

• tf-Faster-RCNN
• faster-rcnn.pytorch

Prerequisites

• python 3.5.x
• pytorch 0.4.1
• tensorboardX
• pillow
• scipy
• numpy
• opencv3
• matplotlib
• easydict

Results

mAP

• Train on voc07trainval+voc12trainval
• Test on voc07test

Paper/ResNet101	This/ResNet101	This/ResNet50
76.4	77.1	75.3

Acc and Loss

The training accuracy of rpn and faster_rcnn:

The training loss curves:

Detection Results

Repo Organization

• config: define configuration information of Faster RCNN.
• dataset: scripts for creating, downloading, organizing datasets.
• faster_rcnn: neural networks and components that form parts of Faster RCNN.
• utils: tools package, containing some necessary functions.

Installation

1. Clone this repository (Faster_RCNN_pytorch):

    git clone --recursive https://github.com/Jacqueline121/Faster_RCNN_pytorch.git
   

2. Install dependencies:

    cd Faster_RCNN_pytorch
    pip install -r requirements.txt
   

3. Compile roi_pooling and nms:

    cd Faster_RCNN_pytorch/faster_rcnn
    sh make.sh
   

Train

Download PASCAL VOC data

1. Download the training, validation, test data:

    # download 2007 data
    wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar
    wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar
    wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCdevkit_08-Jun-2007.tar
   
    # download 2012 data
    wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
   

2. Extract data into one directory named VOCdevkit

    # 2007 data
    tar xvf VOCtrainval_06-Nov-2007.tar
    tar xvf VOCtest_06-Nov-2007.tar
    tar xvf VOCdevkit_08-Jun-2007.tar
   
    # 2012 data
    tar xvf VOCtrainval_11-May-2012.tar
   

3. It should have this basic structure:

    $VOCdevkit/                           # development kit
    $VOCdevkit/VOCcode/                   # VOC utility code
    $VOCdevkit/VOC2007                    # image sets, annotations, etc.
    # ... and several other directories ...
   

4. Create symlinks for the PASCAL VOC dataset:

    cd Faster_RCNN_pytorch/dataset
    mkdir data
    cd data
    # 2007 data
    mkdir VOCdevkit2007
    cd VOCdevkit2007
    ln -s $VOCdevit/VOC2007 VOC2007
   
    # 2012 data
    mkdir VOCdevkit2012
    cd VOCdevkit2012
    ln -s $VOCdevit/VOC2012 VOC2012
   

Download pretrained ImageNet model

cd Faster_RCNN_pytorch/faster_rcnn/backbone
mkdir pretrained
cd pretrained
# resnet50-caffe
wget https://drive.google.com/open?id=0B7fNdx_jAqhtbllXbWxMVEdZclE

# resnet101-caffe
wget https://drive.google.com/open?id=0B7fNdx_jAqhtaXZ4aWppWV96czg

Train

python train.py

Test

python test.py

If you want to visualize the detection result, you can use:

python test.py --vis