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Special Features for This Caffe Repository

  • approximate joint train, test and evaluate models of Faster rcnn/R-FCN, .etc
  • support multi-GPU training
  • support R-FCN with OHEM
  • support Light-head R-CNN / R-FCN++
  • support Cascade R-CNN
  • support FPN (Feature Pyramid Network)
  • support Deformable Conv and Deformable PSROIPooling
  • support SSD layers
  • support YOLOv3 inference
  • Action recognition (Two Stream CNN)
  • CTPN layers for scene text detection, port from tianzhi0549/CTPN
  • script for merging Conv + BatchNorm + Scale layers to 1 layer when those layer are freezed to reduce memory: 'examples/FRCNN/res50/gen_merged_model.py'. script for merge ResNet: 'examples/FRCNN/merge_resnet.sh'.
  • support snapshot after got -SIGTERM (kill command's default signal)
  • logger tools by VisualDL which can visualize loss scalars and feature images .etc
  • support NMS and IOU calc on GPU, Soft-NMS on CPU
  • support box-voting & multi-scale testing
  • support solver learning rate warm-up strategy & cosine decay lr & Cyclical lr (see sgd_solver.cpp)
  • support model file encrypt/decrypt, see 'encrypt_model.cpp' & 'frcnn_api.cpp'

Special layers

Data Preprocess

data enhancement:

  • support Histogram equalization of color image
  • haze-free algorithm

data augmentation:

  • random flip horizontal
  • random jitter
  • hue, saturation, exposure
  • rotate(multiple of 90 degree)

TODO list

  • support batch image greater than 1 (on branch batch)
  • support Rotated R-CNN for rotated bounding box (on branch r-frcnn)
  • support OHEM (see r-fcn)

Installation

This repository uses C++11 features, so make sure to use compiler that is compatible of C++11.

Tested on CUDA 8.0/9.2, CuDNN 7.0, NCCLv1#286916a.

GCC v5.4.0/7.3.1, note that versions lower than v5 are not supported. Python 2.7 for python scripts.

cd $CAFFE_ROOT
cp Makefile.config.example Makefile.config
# modify the content in Makefile.config to adapt your system
# if you like to use VisualDL to log losses, set USE_VISUALDL to 1,
# and cd src/logger && make
make -j7
# extra: 'py' for python interface of Caffe.
# extra: 'pyfrcnn' python wrapper of C++ api. You can use this for demo.
make pyfrcnn py

All following steps, you should do these in the $CAFFE_ROOT path.

Faster R-CNN

Disclaimer

The official Faster R-CNN code of NIPS 2015 paper (written in MATLAB) is available here. It is worth noticing that:

  • This repository contains a C++ reimplementation of the Python code(py-faster-rcnn), which is built on caffe.
  • This repository used code from caffe-faster-rcnn commit 8ba1d26 as base framework.

Demo

Using sh example/FRCNN/demo_frcnn.sh, the will process five pictures in the examples/FRCNN/images, and put results into examples/FRCNN/results.

Note: You should prepare the trained caffemodel into models/FRCNN, such as ZF_faster_rcnn_final.caffemodel for ZF model.

Prepare for training and evaluation

  • The list of training data is examples/FRCNN/dataset/voc2007.trainval.
  • The list of testing data is examples/FRCNN/dataset/voc2007.trainval.
  • Create symlinks for the PASCAL VOC dataset ln -s $YOUR_VOCdevkit_Path $CAFFE_ROOT/VOCdevkit.

As shown in VGG example models/FRCNN/vgg16/train_val.proto, the original pictures should appear at $CAFFE_ROOT/VOCdevkit/VOC2007/JPEGImages/. (Check window_data_param in FrcnnRoiData)

If you want to train Faster R-CNN on your own dataset, you may prepare custom dataset list. The format is as below

# image-id
image-name
number of boxes
label x1 y1 x2 y2 difficulty
...

Training

sh examples/FRCNN/zf/train_frcnn.sh will start training process of voc2007 data using ZF model.

The ImageNet pre-trained models can be found in this link

If you use the provided training script, please make sure:

  • VOCdevkit is within $CAFFE_ROOT and VOC2007 in within VOCdevkit
  • ZF pretrain model should be put into models/FRCNN/ as ZF.v2.caffemodel

examples/FRCNN/convert_model.py transform the parameters of bbox_pred layer by mean and stds values, because the regression value is normalized during training and we should recover it to obtain the final model.

Evaluation

sh examples/FRCNN/zf/test_frcnn.sh the will evaluate the performance of voc2007 test data using the trained ZF model.

  • First Step of This Shell : Test all voc-2007-test images and output results in a text file.
  • Second Step of This Shell : Compare the results with the ground truth file and calculate the mAP.

Config

The program use config file named like config.json to set params. Special params need to be cared about:

  • data_jitter: data augmentation, if set <0 then no jitter,hue,saturation,exposure
  • im_size_align: set to stride of last conv layer of FPN to avoid Deconv shape problem, such as 64, set to 0 to disable
  • bbox_normalize_targets: do bbox norm in training, and do unnorm at testing(do not need convert model weight before testing)
  • test_rpn_score_thresh: you can set >0 to speed up NMS at testing

Detail

Scripts and prototxts for different models are listed in the examples/FRCNN

More details about the code in include and src directory:

  • api/FRCNN for demo and test api
  • caffe/FRCNN contains codes related to Faster R-CNN
  • caffe/RFCN for R-FCN
  • caffe/DeformConv for Deformable Conv
  • caffe/SSD for SSD
  • examples/YOLO for YOLOv3 inference, includes converter script and demo. pay attention to the Upsample layer usage.
  • logger dir relates to logger tools
  • modules and yaml-cpp relate to Caffe module layers, which include FPN layers .etc
  • python/frcnn relates to pybind11 interface for demo
  • caffe/ACTION_REC Two-Stream Convolutional Networks for Action Recognition in Video
  • caffe/CTPN relates to CTPN special layers for scene text detection
  • caffe/PR for some layers from caffe PR

Commands, Rebase From Caffe Master

For synchronous with official caffe

Rebase the dev branch

  • git checkout dev
  • git rebase master
  • git push -f origin dev

QA

  • CUB not found, when compile for GPU version, frcnn_proposal_layer.cu requires a head file <cub/cub.cuh>. CUB is library contained in the official Cuda Toolkit, usually can be found in /usr/local/cuda/include/thrust/system/cuda/detail/. You should add this path in your Makefile.config (try locate cub.cuh to find cub on your system)
  • When Get error: RPC failed; result=22, HTTP code = 0, use git config http.postBuffer 524288000, increases git buffer to 500mb
  • Cannot load module layer dynamic library, the program search the modules first in enviroment variable CAFFE_LAYER_PATH then in predefined DEFAULT_LAYER_PATH in Makefile. So try to set CAFFE_LAYER_PATH in shell script. And this could be happen when using pycaffe.
  • about R-FCN: currently not support class-agnostic (although it is easy to modify), and OHEM method has very little improvement in joint train. also remember to set bg_thresh_lo to 0 when use OHEM.

License and Citation

Caffe is released under the BSD 2-Clause license. The BAIR/BVLC reference models are released for unrestricted use.

Please cite the following papers in your publications if it helps your research:

@article{jia2014caffe,
  Author = {Jia, Yangqing and Shelhamer, Evan and Donahue, Jeff and Karayev, Sergey and Long, Jonathan and Girshick, Ross and Guadarrama, Sergio and Darrell, Trevor},
  Journal = {arXiv preprint arXiv:1408.5093},
  Title = {Caffe: Convolutional Architecture for Fast Feature Embedding},
  Year = {2014}
}
@inproceedings{girshick2015fast,
  title={Fast R-CNN},
  author={Girshick, Ross},
  booktitle={International Conference on Computer Vision},
  pages={1440--1448},
  year={2015}
}
@inproceedings{ren2015faster,
  title={Faster {R-CNN}: Towards real-time object detection with region proposal networks},
  author={Ren, Shaoqing and He, Kaiming and Girshick, Ross and Sun, Jian},
  booktitle={Neural Information Processing Systems},
  pages={91--99},
  year={2015}
}
@article{ren2017faster,
  title={Faster {R-CNN}: Towards real-time object detection with region proposal networks},
  author={Ren, Shaoqing and He, Kaiming and Girshick, Ross and Sun, Jian},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  volume={39},
  number={6},
  pages={1137--1149},
  year={2017},
  publisher={IEEE}
}
@article{dai16rfcn,
    Author = {Jifeng Dai, Yi Li, Kaiming He, Jian Sun},
    Title = {{R-FCN}: Object Detection via Region-based Fully Convolutional Networks},
    Journal = {arXiv preprint arXiv:1605.06409},
    Year = {2016}
}
@article{dai17dcn,
    Author = {Jifeng Dai, Haozhi Qi, Yuwen Xiong, Yi Li, Guodong Zhang, Han Hu, Yichen Wei},
    Title = {Deformable Convolutional Networks},
    Journal = {arXiv preprint arXiv:1703.06211},
    Year = {2017}
}
@article{
    Author = {Navaneeth Bodla and Bharat Singh and Rama Chellappa and Larry S. Davis},
    Title = {Soft-NMS -- Improving Object Detection With One Line of Code},
    Booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
    Year = {2017}
}
@article{li2017light,
  title={Light-Head R-CNN: In Defense of Two-Stage Object Detector},
  author={Li, Zeming and Peng, Chao and Yu, Gang and Zhang, Xiangyu and Deng, Yangdong and Sun, Jian},
  journal={arXiv preprint arXiv:1711.07264},
  year={2017}
}
@inproceedings{cai18cascadercnn,
  author = {Zhaowei Cai and Nuno Vasconcelos},
  Title = {Cascade R-CNN: Delving into High Quality Object Detection},
  booktitle = {CVPR},
  Year  = {2018}
}