Region Proposal Network solves object detection as a regression problem from the objectness perspective. Bounding boxes are predicted by applying learned bounding box deltas to base boxes, namely anchor boxes across different positions in feature maps. Training process directly learns a mapping from raw image intensities to bounding box transformation targets.

Fast R-CNN treats general object detection as a classification problem and bounding box prediction as a regression problem. Classifying cropped region feature maps and predicting bounding box displacements together yields detection results. Cropping feature maps instead of image input accelerates computation utilizing shared convolution maps. Bounding box displacements are simultaneously learned in the training process.

Faster R-CNN utilize an alternate optimization training process between RPN and Fast R-CNN. Fast R-CNN weights are used to initiate RPN for training.

• Install python package easydict, cv2, matplotlib. MXNet require numpy.
• Install MXNet with version no later than Commit 8a3424e, preferably the latest master. Follow the instructions at http://mxnet.readthedocs.io/en/latest/how_to/build.html. Install the python interface.
• Try out detection result by running python demo.py --prefix final --epoch 0 --image myimage.jpg --gpu 0. Suppose you have downloaded pretrained network and place the extracted file final-0000.params in this folder and there is an image named myimage.jpg.

• Install additional python package scipy.
• Download Pascal VOC data and place them to data folder according to Data Folder Structure. You might want to create a symbolic link to VOCdevkit folder by ln -s /path/to/your/VOCdevkit data/VOCdevkit.
• Download VGG16 pretrained model, use mxnet/tools/caffe_converter to convert it, rename to vgg16-symbol.json and vgg16-0001.params and place it in model folder. model folder will be used to place model checkpoints along the training process.

• Start training by running python train_alternate.py after VOCdevkit is ready. A typical command would be python train_alternate.py --gpus 0. This will train the network on the VOC07 trainval. More control of training process can be found in the argparse help accessed by python train_alternate.py -h.
• Start testing by running python test.py after completing the training process. A typical command would be python test.py --has_rpn --prefix model/final --epoch 8. This will test the network on the VOC07 test. Adding a --vis will turn on visualization and -h will show help as in the training process.
• An experiment yields 69.54 mAP, close to 69.9 as reported.

• End-to-end training is the same as py-faster-rcnn and is first made possible by tornadomeet/mx-rcnn, which also has resnet-50 based detection.
• Start training by running python train_end2end.py. A typical command would be python train_end2end.py. This will train the network on VOC07 trainval. As usual control of training process can be found in argparse help.
• Start testing by running python test.py --has_rpn --prefix model/e2e --epoch 10. This will test the network on the VOC07 test.
• An experiment yields 69.65 mAP.

• Download Pascal VOC data and place them to data folder according to Data Folder Structure. You might want to create a symbolic link to VOCdevkit folder by ln -s /path/to/your/VOCdevkit data/VOCdevkit.
• Download precomputed selective search data and place them to data folder according to Data Folder Structure.
• Download VGG16 pretrained model, use mxnet/tools/caffe_converter to convert it, rename to vgg16-symbol.json and vgg16-0001.params and place it in model folder. model folder will be used to place model checkpoints along the training process.
• Start training by running python -m rcnn.tools.train_rcnn --proposal ss to use the selective search proposal.
• Start testing by running python -m rcnn.tools.test_rcnn --proposal ss.
• An experiment yields 66.5 mAP, close to 66.9 as reported.

• This repository provides Faster R-CNN as a package named rcnn.
  • rcnn.core: core routines in Faster R-CNN training and testing.
  • rcnn.dataset: dataset library. Base class is rcnn.dataset.imdb.IMDB.
  • rcnn.processing: data and label processing library.
  • rcnn.tools: training and testing wrapper.
  • rcnn.utils: utilities in training and testing, usually overloads mxnet functions.
• data Refer to Data Folder Structure for dataset reference. Usually dataset contains images and imglists.
• model It is recommended to make a model symbolic link to hard disk to place your model.

• Download link to trained model Baidu Yun: http://pan.baidu.com/s/1boRhGvH (ixiw) or Dropbox: https://www.dropbox.com/s/jrr83q0ai2ckltq/final-0000.params.tar.gz?dl=0

• Download link to Pascal VOC and precomputed selective search proposals

  Pascal VOCdevkit
  http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar
  http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar
  http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCdevkit_08-Jun-2007.tar
  selective_search_data (by Ross Girshick)
  Download link accessible at https://github.com/rbgirshick/fast-rcnn/blob/master/data/scripts/fetch_selective_search_data.sh
  

• Data Folder Structure (create a data folder if there is none)

  VOCdevkit
  -- VOC + year (JPEG images and annotations)
  -- results (will be created by evaluation)
  ---- VOC + year
  ------ main
  -------- comp4_det_val_aeroplane.txt
  selective_search_data
  rpn_data (will be created by rpn)
  cache (will be created by imdb)
  

This repository used code from MXNet, Fast R-CNN, Faster R-CNN, caffe. Training data are from Pascal VOC, ImageNet. Model comes from VGG16. Thanks to tornadomeet for end-to-end experiments and MXNet contributers for helpful discussions.

1. Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, and Zheng Zhang. MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems. In Neural Information Processing Systems, Workshop on Machine Learning Systems, 2015
2. Ross Girshick. "Fast R-CNN." In Proceedings of the IEEE International Conference on Computer Vision, 2015.
3. Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. "Faster R-CNN: Towards real-time object detection with region proposal networks." In Advances in Neural Information Processing Systems, 2015.
4. Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell. "Caffe: Convolutional architecture for fast feature embedding." In Proceedings of the ACM International Conference on Multimedia, 2014.
5. Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. "The pascal visual object classes (voc) challenge." International journal of computer vision 88, no. 2 (2010): 303-338.
6. Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. "ImageNet: A large-scale hierarchical image database." In Computer Vision and Pattern Recognition, IEEE Conference on, 2009.
7. Karen Simonyan, and Andrew Zisserman. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014).