By Yao Xiao
Virginia Tech
This is a practical Faster RCNN's implementation based on Keras. For details you can read this paper: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- Install Keras
- Python packages:
- cython
- opencv
- numpy
- matplotlib
- scipy
Git clone the repository:
git clone https://github.com/PatrickXYS/Reproduce_frcnn.git
Use pre-trained model to predict images. You can simply use command line under ./Reproduce_frcnn directory:
python test_frcnn.py -p ./img
If you want to use your own images, you can import your images into ./img directory. Then use the above command sentences.
You need to first download Pascal_VOC dataset or COCO dataset from: ``
-
1 Pascal_VOC 2012:
http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
-
2 COCO 2014: http://images.cocodataset.org/zips/train2014.zip
You need to put dataset under ./Reprouduce_frcnn directory
To train your own model, you can use vgg16, resnet50 or resnet101.
-
1 Vgg16 pre-trained weights:
-
2 Resnet50 pre-trained weights:
-
3 Resnet101 pre-trained weights:
https://drive.google.com/file/d/0Byy2AcGyEVxfdUV1MHJhelpnSG8/view?usp=sharing
You need to put .h5 file under ./Reprouduce_frcnn directory
-
1 Pascal_VOC dataset training:
python train_frcnn.py -p ./VOCdevkit/
-
2 COCO dataset training:
python train_frcnn.py -p ./coco/
After training you can check your results by running following commands:
python test_frcnn.py -p ./img
@article{He2015, author = {Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun}, title = {Deep Residual Learning for Image Recognition}, journal = {arXiv preprint arXiv:1512.03385}, year = {2015} }
@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} } }