zouyunzhe / loco Goto Github PK

A local context layer is implemented based on Faster R-CNN(see: py-faster-rcnn code) for detecting small objects more Effectively

1. Requirements: software
2. Requirements: hardware
3. Basic installation
4. Data preparation
5. Training and Testing
6. Usage

1. Requirements for Caffe and pycaffe (see: Caffe installation instructions)

Note: Caffe must be built with support for Python layers!

# In your Makefile.config, make sure to have this line uncommented
WITH_PYTHON_LAYER := 1
# Unrelatedly, it's also recommended that you use CUDNN
USE_CUDNN := 1

You can download Makefile.config for reference. 2. Python packages you might not have: cython, python-opencv, easydict

1. For training smaller networks (ZF, VGG_CNN_M_1024) a good GPU (e.g., Titan, K20, K40, ...) with at least 3G of memory suffices
2. For training Fast R-CNN with VGG16, you'll need a K40 (~11G of memory)
3. For training the end-to-end version of Faster R-CNN or LOCO with VGG16, 3G of GPU memory is sufficient (using CUDNN)

1. Clone the LOCO repository

git clone https://github.com/CPFLAME/LOCO.git

2. We'll call the directory that you cloned LOCO into LOCO

   you'll need to manually clone the caffe-fast-rcnn submodule:

   cd LOCO
   git clone https://github.com/rbgirshick/caffe-fast-rcnn.git

3. Build the Cython modules

   cd $LOCO/lib
   make

4. Build Caffe and pycaffe

   cd $LOCO/caffe-fast-rcnn
   # Now follow the Caffe installation instructions here:
   #   http://caffe.berkeleyvision.org/installation.html
   
   # If you're experienced with Caffe and have all of the requirements installed
   # and your Makefile.config in place, then simply do:
   make -j8 && make pycaffe

1. Download the traffic dataset

   the dataset is modified from Tsinghua-Tencent 100K a traffic sign dataset. We treat all traffic signs as one category and transfer it into VOC's annotation format. For convenience, we call it VOCdevkit2007.

   Download these four files: http://pan.baidu.com/s/1o8n2bVG

2. Extract all of these tars into one directory named VOCdevkit2007

   cat VOCdevkit2007.tar0* | tar -xzv

3. It should have this basic structure

   $VOCdevkit2007/                           # development kit
   $VOCdevkit2007/VOC2007                    # image sets, annotations, etc.
   # ... and several other directories ...

4. Create symlinks for the PASCAL VOC dataset

   cd $LOCO/data
   ln -s $VOCdevkit2007 VOCdevkit2007

   Using symlinks is a good idea because you will likely want to share the same PASCAL dataset installation between multiple projects.

1. For training

   you should download pre-trained ImageNet models

   cd $LOCO
   ./data/scripts/fetch_imagenet_models.sh

   VGG16 comes from the Caffe Model Zoo, but is provided here for your convenience. ZF was trained at MSRA.

   start training

   cd $LOCO
   ./experiments/scripts/faster_rcnn_context.sh [GPU_ID] VGG16 pascal_voc

2. For testing We released our pretrained model at model, you can download it for testing.

   start testing

   cd $LOCO
   ./tools/test_net.py --gpu [GPU_ID] --def models/pascal_voc/VGG16/faster_rcnn_end2end/context_test.prototxt --net $your_model_path --imdb voc_2007_test --cfg experiments/cfgs/faster_rcnn_end2end.yml

Trained networks are saved under:

output/<experiment directory>/<dataset name>/

Test outputs are saved under:

output/<experiment directory>/<dataset name>/<network snapshot name>/