https://github.com/deeplearnphysics/faster-rcnn
https://github.com/deeplearnphysics/faster-rcnn
Last synced: 12 months ago
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- Host: GitHub
- URL: https://github.com/deeplearnphysics/faster-rcnn
- Owner: DeepLearnPhysics
- Created: 2017-10-09T15:37:08.000Z (over 8 years ago)
- Default Branch: develop
- Last Pushed: 2018-01-19T20:22:32.000Z (about 8 years ago)
- Last Synced: 2025-04-13T00:35:16.447Z (12 months ago)
- Language: Python
- Size: 1.44 MB
- Stars: 8
- Watchers: 4
- Forks: 3
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# faster-rcnn
A Tensorflow implementation of faster RCNN detection framework toward 3D object detection. This repository is based on the original python Caffe implementation of faster RCNN by Ross Girshick available [here](https://github.com/rbgirshick/py-faster-rcnn) and implementation to Tensorflow by Xinlei Chen available [here](https://github.com/endernewton/tf-faster-rcnn). While much of the code follows Xinlei's implementation, the reason why we did not fork is because we plan to extend further to 3D. This involves much reorganization of code/directory structures and departs from the goal of replicating py-caffe implementation. Note changes made by Xinlei (summarized in their paper [An Implementation of Faster RCNN with Study for Region Sampling](https://arxiv.org/pdf/1702.02138.pdf)) is inherited as is.
**Note**:
This implementation followed Xinlei's method step by step, and may still lack some functionalities and/or performance.
The current goal is to reproduce the results in Xinlei's paper. The network does get trained with vgg implementation (resnet not yet tried).
### Prerequisites
- Tensorflow: currently tested with v1.3. If concerned, Xinlei's Tensorflow [fork](https://github.com/endernewton/tensorflow) implements the original ROI pooling in py-caffe.
- Python packages: `cython`, `opencv-python`, `easydict`
### Installation
1. Clone the repository
```Shell
git clone https://github.com/DeepLearnPhysics/faster-rcnn.git
```
2. Source configure script (you have to do this each time you login to a new shell).
```Shell
cd faster-rcnn
source configure.sh
```
3. Update your -arch in setup script to match your GPU
```Shell
# Change the GPU architecture (-arch) if necessary
vim setup.py
```
| GPU model | Architecture |
| ------------- | ------------- |
| TitanX (Maxwell/Pascal) | sm_52 |
| GTX 960M | sm_50 |
| GTX 1080 (Ti) | sm_61 |
| Grid K520 (AWS g2.2xlarge) | sm_30 |
| Tesla K80 (AWS p2.xlarge) | sm_37 |
**Note**: You are welcome to contribute the settings on your end if you have made the code work properly on other GPUs.
4. Build the Cython modules
```Shell
make clean
make
```
### Setup data for training (py-faster-rcnn has more detailed instructions [here](https://github.com/rbgirshick/py-faster-rcnn#beyond-the-demo-installation-for-training-and-testing-models))
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 $FRCN_ROOT/data
ln -s $VOCdevkit VOCdevkit2007
```
Using symlinks is a good idea because you will likely want to share the same PASCAL dataset installation between multiple projects.
5. [Optional] follow similar steps to get PASCAL VOC 2010 and 2012
6. [Optional] for coco data set see py-faster-rcnn instruction [here](https://github.com/rbgirshick/py-faster-rcnn/tree/master/data)
### Train your own model
1. Download pre-trained models and weights. The current code support VGG16. Pre-trained models are provided by slim, you can get the pre-trained models [here](https://github.com/tensorflow/models/tree/master/research/slim#Pretrained) and set them in the ``data`` folder. For example for VGG16 model, you can set up like:
```Shell
cd data
wget -v http://download.tensorflow.org/models/vgg_16_2016_08_28.tar.gz
tar -xzvf vgg_16_2016_08_28.tar.gz
mv vgg_16.ckpt vgg16.ckpt
cd ..
```
2. Train (and test, evaluation)
```Shell
python example/train_coco.py
```
3. Visualization with Tensorboard
```Shell
tensorboard --logdir=tensorboard/log &
```
By default, trained networks are saved under:
```
output/[NET]
```
Tensorboard information for train and validation is saved under:
```
tensorboard/[NET]/
```
### Citation
For citation, please consider citing Xinlei's work
@article{chen17implementation,
Author = {Xinlei Chen and Abhinav Gupta},
Title = {An Implementation of Faster RCNN with Study for Region Sampling},
Journal = {arXiv preprint arXiv:1702.02138},
Year = {2017}
}
as well as faster RCNN citation:
@inproceedings{renNIPS15fasterrcnn,
Author = {Shaoqing Ren and Kaiming He and Ross Girshick and Jian Sun},
Title = {Faster {R-CNN}: Towards Real-Time Object Detection
with Region Proposal Networks},
Booktitle = {Advances in Neural Information Processing Systems ({NIPS})},
Year = {2015}
}