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https://github.com/CharlesShang/TFFRCNN
FastER RCNN built on tensorflow
https://github.com/CharlesShang/TFFRCNN
detection faster-rcnn pedestrian pvanet rcnn tensorflow
Last synced: 5 days ago
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FastER RCNN built on tensorflow
- Host: GitHub
- URL: https://github.com/CharlesShang/TFFRCNN
- Owner: CharlesShang
- License: mit
- Created: 2016-10-19T07:38:26.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2018-06-07T07:45:18.000Z (over 6 years ago)
- Last Synced: 2024-08-01T15:32:02.223Z (3 months ago)
- Topics: detection, faster-rcnn, pedestrian, pvanet, rcnn, tensorflow
- Language: Python
- Homepage:
- Size: 3.77 MB
- Stars: 874
- Watchers: 52
- Forks: 420
- Open Issues: 102
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# TFFRCNN
This is an experimental **T**ensor **F**low implementation of **F**aster **RCNN** (**TFFRCNN**), mainly based on the work of [smallcorgi](https://github.com/smallcorgi/Faster-RCNN_TF) and [rbgirshick](https://github.com/rbgirshick/py-faster-rcnn). 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](http://arxiv.org/pdf/1506.01497v3.pdf) by Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun.
### What's New
- [x] Resnet networks support
- [x] KITTI object detection dataset support
- [x] **P**osition **S**ensitive **ROI Pooling** (psroi_pooling), not testing yet
- [x] Hard Example Mining
- [x] Data Augment
- [x] PVANet
- [x] Tensorflow 1.0
- [ ] R-FCN
- [ ] Multi-layer Architecture (HyperNet)
- [ ] more hacks...
### Acknowledgments:
1. [py-faster-rcnn](https://github.com/rbgirshick/py-faster-rcnn)
2. [Faster-RCNN_TF](https://github.com/smallcorgi/Faster-RCNN_TF)
3. [ROI pooling](https://github.com/zplizzi/tensorflow-fast-rcnn)
### Requirements: software
1. Requirements for Tensorflow (see: [Tensorflow](https://www.tensorflow.org/))
2. Python packages you might not have: `cython`, `python-opencv`, `easydict` (recommend to install: [Anaconda](https://www.continuum.io/downloads))
### 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
```Shell
git clone https://github.com/CharlesShang/TFFRCNN.git
```
2. Build the Cython modules
```Shell
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](#installation-sufficient-for-the-demo)*, you'll be ready to run the demo.
To run the demo
```Shell
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](https://drive.google.com/open?id=0ByuDEGFYmWsbNVF5eExySUtMZmM)
2. [VGG16 - TFFRCNN (0.689 mAP on VOC07)](https://drive.google.com/file/d/0B_xFdh9onPagX0JWRlR0cTZ5OGc/view?usp=sharing).
3. [VGG16 - TFFRCNN (0.748 mAP on VOC07)](https://drive.google.com/file/d/0B_xFdh9onPagVmt5VHlCU25vUEE/view?usp=sharing)
4. [Resnet50 trained on ImageNet](https://drive.google.com/file/d/0B_xFdh9onPagSWU1ZTAxUTZkZTQ/view?usp=sharing)
5. [Resnet50 - TFFRCNN (0.712 mAP on VOC07)](https://drive.google.com/file/d/0B_xFdh9onPagbXk1b0FIeDRJaU0/view?usp=sharing)
6. [PVANet trained on ImageNet, converted from caffemodel](https://drive.google.com/open?id=0B_xFdh9onPagQnJBdWl3VGQxam8)
### Training on Pascal VOC 2007
1. Download the training, validation, test data and VOCdevkit
```Shell
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`
```Shell
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
```Shell
$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
```Shell
cd $TFFRCNN/data
ln -s $VOCdevkit VOCdevkit2007
```
5. Download pre-trained model [VGG16](https://drive.google.com/open?id=0ByuDEGFYmWsbNVF5eExySUtMZmM) and put it in the path `./data/pretrain_model/VGG_imagenet.npy`
6. Run training scripts
```Shell
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
```Shell
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
```Shell
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
```Shell
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
```