Faster R-CNN in MXNet

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. The approximate joint training scheme does not backpropagate rcnn training error to rpn training.

Experiments

Indicator	py-faster-rcnn (caffe resp.)	mx-rcnn (this reproduction)
Training speed [1]	2.5 img/s training, 5 img/s testing	3.8 img/s in training, 12.5 img/s testing
Valset performance [2]	mAP 73.2	mAP 75.97
Memory usage [3]	11G for Fast R-CNN	4.6G for Fast R-CNN
Parallelization [4]	None	3.8 img/s to 6 img/s for 2 GPUs
Extensibility [5]	Old framework and base networks	ResNet

[1] On Ubuntu 14.04.5 with device Titan X, cuDNN enabled. The experiment is VGG-16 end-to-end training.
[2] VGG network. Trained end-to-end on VOC07trainval+12trainval, tested on VOC07 test.
[3] VGG network. Fast R-CNN is the most memory expensive process.
[4] VGG network (parallelization limited by bandwidth). ResNet-101 speeds up from 2 img/s to 3.5 img/s.
[5] py-faster-rcnn does not support ResNet or recent caffe version.

Method	Network	Training Data	Testing Data	Reference	Result	Link
Fast R-CNN	VGG16	VOC07	VOC07test	66.9	66.50	Dropbox
Faster R-CNN alternate	VGG16	VOC07	VOC07test	69.9	69.62	Dropbox
Faster R-CNN end-to-end	VGG16	VOC07	VOC07test	69.9	70.23	Dropbox
Faster R-CNN end-to-end	VGG16	VOC07+12	VOC07test	73.2	75.97	Dropbox
Faster R-CNN end-to-end	ResNet-101	VOC07+12	VOC07test	76.4	79.35	Dropbox
Faster R-CNN end-to-end	VGG16	COCO train	COCO val	21.2	22.8	Dropbox
Faster R-CNN end-to-end	ResNet-101	COCO train	COCO val	27.2	26.1	Dropbox

The above experiments were conducted on this version of repository with a modified MXNet based on 0.9.1 nnvm pre-release.

Set up environment

Suppose HOME represents where this file is located. All commands, unless stated otherwise, should be started from HOME.

bash script/additional_deps.sh sets up environment. It does the following

• Install python package cython easydict matplotlib scikit-image.
• Install MXNet version v0.9.5 or higher and MXNet Python Interface. Open python type import mxnet to confirm.
• Run make in HOME.

Run prediction with existing model

• Download a example model trained on VOC07 trainval from Baidu Yun (ixiw) or Dropbox and put the model final-0000.params in HOME.
• Run predition on myimage.jpg with python demo.py --prefix final --epoch 0 --image myimage.jpg --gpu 0 --vis. Drop the --vis if you do not have a display or want to save as a new file. --prefix final --epoch 0 tells the program to load model final-0000.params.

Train new model

The following tutorial is based on VOC data, VGG network. Before proceed, look at sidenotes on preparing data and pretrained models. Command line arguments --network resnet and --dataset coco are used for other networks and datasets. Scripts like script/vgg_voc07.sh are examples to run experiments with different settings.

Scheme 1: alternate between RPN and Fast RPN

bash script/vgg_alter_voc07.sh 0 starts alternate training with gpu 0.

• Start training by running python train_alternate.py. This will train the VGG network on the VOC07 trainval. More control of training process can be found in the argparse help.
• Start testing by running python test.py --prefix model/final --epoch 0 after completing the training process. This will test the VGG network on the VOC07 test with the model in HOME/model/final-0000.params. Adding a --vis will turn on visualization and -h will show help as in the training process.

Scheme 2: end-to-end but no backpropagation from Fast R-CNN to RPN

bash script/vgg_voc07.sh 0 starts end-to-end training with gpu 0.

• Start training by running python train_end2end.py. This will train the VGG network on VOC07 trainval.
• Start testing by running python test.py. This will test the VGG network on the VOC07 test.

Scheme 3: Fast R-CNN with existing object proposals

bash script/get_selective.sh downloads precomputed object proposals. bash script/vgg_fast_rcnn.sh 0 starts training Fast R-CNN with aformentioned proposals with gpu 0.

• To reproduce Fast R-CNN, scipy is used to load selective search proposals, thus an additional dependency.
• Download precomputed selective search data and place them to data folder.
• Start training by running python -m rcnn.tools.train_rcnn --proposal selective_search to use the selective search proposal.
• Start testing by running python -m rcnn.tools.test_rcnn --proposal selective_search.
• script/vgg_fast_rcnn.sh will train Fast R-CNN on VOC07 and test on VOC07test.

Sidenotes

Download data and label

bash script/get_voc.sh and bash script/get_coco.sh downloads data from official repos and organizes them.

• Make a folder data in HOME. data folder will be used to place the training data folder VOCdevkit and coco.
• Download and extract Pascal VOC data, place the VOCdevkit folder in HOME/data.
• Download and extract coco dataset, place all images to coco/images and annotation jsons to data/annotations.

Download ImageNet models

bash script/get_pretrained_model.sh downloads pretrained ImageNet models and organizes them.

• Make a folder model in HOME. model folder will be used to place model checkpoints along the training process. It is recommended to set model as a symbolic link to somewhere else in hard disk.
• Download VGG16 pretrained model vgg16-0000.params from MXNet model gallery to model folder.
• Download ResNet pretrained model resnet-101-0000.params from ResNet to model folder.

Dataset attributes

All dataset have three attributes, image_set, root_path and dataset_path.

• image_set could be 2007_trainval or something like 2007trainval+2012trainval.
• root_path is usually data, where cache, selective_search_data, rpn_data will be stored.
• dataset_path could be something like data/VOCdevkit, where images, annotations and results can be put so that many copies of datasets can be linked to the same actual place.

Format of command line arguments

Command line arguments follow the format of mxnet/example/image-classification.

• prefix refers to the first part of a saved model file name and epoch refers to a number in this file name. In model/vgg-0000.params, prefix is "model/vgg" and epoch is 0.
• begin_epoch means the start of your training process, which will apply to all saved checkpoints.
• Remember to turn off cudnn auto tune. export MXNET_CUDNN_AUTOTUNE_DEFAULT=0.

Code Structure

This repository provides Faster R-CNN as a package named rcnn.

• rcnn.core: core routines in Faster R-CNN training and testing.
• rcnn.cython: cython speedup from py-faster-rcnn.
• rcnn.dataset: dataset library. Base class is rcnn.dataset.imdb.IMDB.
• rcnn.io: prepare training data.
• rcnn.processing: data and label processing library.
• rcnn.pycocotools: python api from coco dataset.
• rcnn.symbol: symbol and operator.
• rcnn.tools: training and testing wrapper.
• rcnn.utils: utilities in training and testing, usually overloads mxnet functions.

History

• Fast R-CNN (v1)
• Faster R-CNN (v2)
• Faster R-CNN with module training (v3)
• Faster R-CNN with end-to-end training (v3.5, tornadomeet/mx-rcnn)
• Faster R-CNN with end-to-end training and module testing (v4)
• Faster R-CNN with accelerated training and resnet (v5, current)

Disclaimer

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

References

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 IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016.
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).
8. Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. "Deep Residual Learning for Image Recognition". In Computer Vision and Pattern Recognition, IEEE Conference on, 2016.
9. Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick. "Microsoft COCO: Common Objects in Context" In European Conference on Computer Vision, pp. 740-755. Springer International Publishing, 2014.