hollylxj / TFFRCNN

FastER RCNN built on tensorflow

TFFRCNN

This is an experimental Tensor Flow implementation of Faster RCNN (TFFRCNN), mainly based on the work of smallcorgi and rbgirshick. I have re-organized the libraries under lib path, making each of python modules independent to each other, so you can understand, re-write the code easily.

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.

What's New

• Resnet networks support
• KITTI object detection dataset support
• Position Sensitive ROI Pooling (psroi_pooling), not testing yet
• Hard Example Mining
• Data Augment
• PVANet
• Tensorflow 1.0
• R-FCN
• Multi-layer Architecture (HyperNet)
• more hacks...

Acknowledgments:

1. py-faster-rcnn

2. Faster-RCNN_TF

3. ROI pooling

Requirements: software

1. Requirements for Tensorflow (see: Tensorflow)

2. Python packages you might not have: cython, python-opencv, easydict (recommend to install: Anaconda)

Requirements: hardware

1. For training the end-to-end version of Faster R-CNN with VGG16, 3G of GPU memory is sufficient (using CUDNN)

Installation (sufficient for the demo)

1. Clone the Faster R-CNN repository

git clone https://github.com/CharlesShang/TFFRCNN.git

2. Build the Cython modules

   cd TFFRCNN/lib
   make # compile cython and roi_pooling_op, you may need to modify make.sh for your platform

Demo

After successfully completing basic installation, you'll be ready to run the demo.

To run the demo

cd $TFFRCNN
python ./faster_rcnn/demo.py --model model_path

The demo performs detection using a VGG16 network trained for detection on PASCAL VOC 2007.

Download list

1. VGG16 trained on ImageNet

2. VGG16 - TFFRCNN (0.689 mAP on VOC07).

3. VGG16 - TFFRCNN (0.748 mAP on VOC07)

4. Resnet50 trained on ImageNet

5. Resnet50 - TFFRCNN (0.712 mAP on VOC07)

6. PVANet trained on ImageNet, converted from caffemodel

Training on Pascal VOC 2007

1. Download the training, validation, test data and VOCdevkit

   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

2. Extract all of these tars into one directory named VOCdevkit

   tar xvf VOCtrainval_06-Nov-2007.tar
   tar xvf VOCtest_06-Nov-2007.tar
   tar xvf VOCdevkit_08-Jun-2007.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 $TFFRCNN/data
   ln -s $VOCdevkit VOCdevkit2007

5. Download pre-trained model VGG16 and put it in the path ./data/pretrain_model/VGG_imagenet.npy

6. Run training scripts

   cd $TFFRCNN
   python ./faster_rcnn/train_net.py --gpu 0 --weights ./data/pretrain_model/VGG_imagenet.npy --imdb voc_2007_trainval --iters 70000 --cfg  ./experiments/cfgs/faster_rcnn_end2end.yml --network VGGnet_train --set EXP_DIR exp_dir

7. Run a profiling

   cd $TFFRCNN
   # install a visualization tool
   sudo apt-get install graphviz  
   ./experiments/profiling/run_profiling.sh 
   # generate an image ./experiments/profiling/profile.png

Training on KITTI detection dataset

1. Download the KITTI detection dataset

   http://www.cvlibs.net/datasets/kitti/eval_object.php
   

2. Extract all of these tar into ./TFFRCNN/data/ and the directory structure looks like this:

   KITTI
       |-- training
               |-- image_2
                   |-- [000000-007480].png
               |-- label_2
                   |-- [000000-007480].txt
       |-- testing
               |-- image_2
                   |-- [000000-007517].png
               |-- label_2
                   |-- [000000-007517].txt
   

3. Convert KITTI into Pascal VOC format

   cd $TFFRCNN
   ./experiments/scripts/kitti2pascalvoc.py \
   --kitti $TFFRCNN/data/KITTI --out $TFFRCNN/data/KITTIVOC

4. The output directory looks like this:

   KITTIVOC
       |-- Annotations
               |-- [000000-007480].xml
       |-- ImageSets
               |-- Main
                   |-- [train|val|trainval].txt
       |-- JPEGImages
               |-- [000000-007480].jpg
   

5. Training on KITTIVOC is just like on Pascal VOC 2007

   python ./faster_rcnn/train_net.py \
   --gpu 0 \
   --weights ./data/pretrain_model/VGG_imagenet.npy \
   --imdb kittivoc_train \
   --iters 160000 \
   --cfg ./experiments/cfgs/faster_rcnn_kitti.yml \
   --network VGGnet_train