文件列表:

LICENSE (1070, 2017-03-07)
data (0, 2017-03-07)
data\demo (0, 2017-03-07)
data\demo\000022.png (857360, 2017-03-07)
data\demo\000188.png (886495, 2017-03-07)
data\demo\000456.jpg (105302, 2017-03-07)
data\demo\000542.jpg (115536, 2017-03-07)
data\demo\001150.jpg (88635, 2017-03-07)
data\demo\001763.jpg (73424, 2017-03-07)
data\demo\004545.jpg (123072, 2017-03-07)
experiments (0, 2017-03-07)
experiments\cfgs (0, 2017-03-07)
experiments\cfgs\faster_rcnn_end2end.yml (693, 2017-03-07)
experiments\cfgs\faster_rcnn_end2end_pva.yml (767, 2017-03-07)
experiments\cfgs\faster_rcnn_end2end_resnet.yml (754, 2017-03-07)
experiments\cfgs\faster_rcnn_kitti.yml (645, 2017-03-07)
experiments\eval (0, 2017-03-07)
experiments\eval\voc2007_test.sh (946, 2017-03-07)
experiments\profiling (0, 2017-03-07)
experiments\profiling\gprof2dot.py (105119, 2017-03-07)
experiments\profiling\profile.png (178663, 2017-03-07)
experiments\profiling\run_profiling.sh (619, 2017-03-07)
experiments\scripts (0, 2017-03-07)
experiments\scripts\faster_rcnn_end2end.sh (1851, 2017-03-07)
experiments\scripts\faster_rcnn_kitti.sh (236, 2017-03-07)
experiments\scripts\faster_rcnn_voc.sh (486, 2017-03-07)
experiments\scripts\faster_rcnn_voc_pva.sh (274, 2017-03-07)
experiments\scripts\faster_rcnn_voc_resnet.sh (254, 2017-03-07)
experiments\scripts\kitti2pascalvoc.py (11561, 2017-03-07)
faster_rcnn (0, 2017-03-07)
faster_rcnn\__init__.py (35, 2017-03-07)
faster_rcnn\demo.py (4544, 2017-03-07)
faster_rcnn\test_net.py (3106, 2017-03-07)
faster_rcnn\train_net.py (3858, 2017-03-07)
lib (0, 2017-03-07)
lib\Makefile (68, 2017-03-07)
lib\__init__.py (16, 2017-03-07)
... ...

# TFFRCNN This is an experimental **T**ensor **F**low implementation of **F**aster **RCNN** (**TFFRCNN**), mainly based on the work of [smallcorgi](https://github.com/smallcorgi/Faster-RCNN_TF) and [rbgirshick](https://github.com/rbgirshick/py-faster-rcnn). I have re-organized the libraries under `lib` path, making each of python modules independent to each other, so you can understand, re-write the code easily. For details about R-CNN please refer to the paper [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](http://arxiv.org/pdf/1506.01497v3.pdf) by Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun. ### What's New - [x] Resnet networks support - [x] KITTI object detection dataset support - [x] **P**osition **S**ensitive **ROI Pooling** (psroi_pooling), not testing yet - [x] Hard Example Mining - [x] Data Augment - [x] PVANet - [x] Tensorflow 1.0 - [ ] R-FCN - [ ] Multi-layer Architecture (HyperNet) - [ ] more hacks... ### Acknowledgments: 1. [py-faster-rcnn](https://github.com/rbgirshick/py-faster-rcnn) 2. [Faster-RCNN_TF](https://github.com/smallcorgi/Faster-RCNN_TF) 3. [ROI pooling](https://github.com/zplizzi/tensorflow-fast-rcnn) ### Requirements: software 1. Requirements for Tensorflow (see: [Tensorflow](https://www.tensorflow.org/)) 2. Python packages you might not have: `cython`, `python-opencv`, `easydict` (recommend to install: [Anaconda](https://www.continuum.io/downloads)) ### Requirements: hardware 1. For training the end-to-end version of Faster R-CNN with VGG16, 3G of GPU memory is sufficient (using CUDNN) ### Installation (sufficient for the demo) 1. Clone the Faster R-CNN repository ```Shell git clone https://github.com/CharlesShang/TFFRCNN.git ``` 2. Build the Cython modules ```Shell cd TFFRCNN/lib make # compile cython and roi_pooling_op, you may need to modify make.sh for your platform ``` ### Demo *After successfully completing [basic installation](#installation-sufficient-for-the-demo)*, you'll be ready to run the demo. To run the demo ```Shell cd $TFFRCNN python ./faster_rcnn/demo.py --model model_path ``` The demo performs detection using a VGG16 network trained for detection on PASCAL VOC 2007. ### Download list 1. [VGG16 trained on ImageNet](https://drive.google.com/open?id=0ByuDEGFYmWsbNVF5eExySUtMZmM) 2. [VGG16 - TFFRCNN (0.689 mAP on VOC07)](https://drive.google.com/file/d/0B_xFdh9onPagX0JWRlR0cTZ5OGc/view?usp=sharing). 3. [VGG16 - TFFRCNN (0.748 mAP on VOC07)](https://drive.google.com/file/d/0B_xFdh9onPagVmt5VHlCU25vUEE/view?usp=sharing) 4. [Resnet50 trained on ImageNet](https://drive.google.com/file/d/0B_xFdh9onPagSWU1ZTAxUTZkZTQ/view?usp=sharing) 5. [Resnet50 - TFFRCNN (0.712 mAP on VOC07)](https://drive.google.com/file/d/0B_xFdh9onPagbXk1b0FIeDRJaU0/view?usp=sharing) 6. [PVANet trained on ImageNet, converted from caffemodel](https://drive.google.com/open?id=0B_xFdh9onPagQnJBdWl3VGQxam8) ### Training on Pascal VOC 2007 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 $TFFRCNN/data ln -s $VOCdevkit VOCdevkit2007 ``` 5. Download pre-trained model [VGG16](https://drive.google.com/open?id=0ByuDEGFYmWsbNVF5eExySUtMZmM) and put it in the path `./data/pretrain_model/VGG_imagenet.npy` 6. Run training scripts ```Shell cd $TFFRCNN python ./faster_rcnn/train_net.py --gpu 0 --weights ./data/pretrain_model/VGG_imagenet.npy --imdb voc_2007_trainval --iters 70000 --cfg ./experiments/cfgs/faster_rcnn_end2end.yml --network VGGnet_train --set EXP_DIR exp_dir ``` 7. Run a profiling ```Shell cd $TFFRCNN # install a visualization tool sudo apt-get install graphviz ./experiments/profiling/run_profiling.sh # generate an image ./experiments/profiling/profile.png ``` ### Training on KITTI detection dataset 1. Download the KITTI detection dataset ``` http://www.cvlibs.net/datasets/kitti/eval_object.php ``` 2. Extract all of these tar into `./TFFRCNN/data/` and the directory structure looks like this: ``` KITTI |-- training |-- image_2 |-- [000000-007480].png |-- label_2 |-- [000000-007480].txt |-- testing |-- image_2 |-- [000000-007517].png |-- label_2 |-- [000000-007517].txt ``` 3. Convert KITTI into Pascal VOC format ```Shell cd $TFFRCNN ./experiments/scripts/kitti2pascalvoc.py \ --kitti $TFFRCNN/data/KITTI --out $TFFRCNN/data/KITTIVOC ``` 4. The output directory looks like this: ``` KITTIVOC |-- Annotations |-- [000000-007480].xml |-- ImageSets |-- Main |-- [train|val|trainval].txt |-- JPEGImages |-- [000000-007480].jpg ``` 5. Training on `KITTIVOC` is just like on Pascal VOC 2007 ```Shell python ./faster_rcnn/train_net.py \ --gpu 0 \ --weights ./data/pretrain_model/VGG_imagenet.npy \ --imdb kittivoc_train \ --iters 160000 \ --cfg ./experiments/cfgs/faster_rcnn_kitti.yml \ --network VGGnet_train ```

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