CSC

Category-Aware Spatial Constraint for Weakly Supervised Detection

Citing CSC

If you find CSC useful in your research, please consider citing:

@article{Shen_2019_TIP,
author = {Shen, Yunhang and Ji, Rongrong and Yang, Kuiyuan and Deng, Cheng and Wang, Changhu},
journal = {IEEE TRANSACTIONS ON IMAGE PROCESSING},
title = {{Category-Aware Spatial Constraint for Weakly Supervised Detection}},
year = {2019}
}

Contents

1. Requirements: software
2. Requirements: hardware
3. Installation
4. Usage

Requirements: software

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

2. Python packages you might not have: cython, python-opencv, easydict
3. [Optional] MATLAB is required for official PASCAL VOC evaluation only. The code now includes unofficial Python evaluation code.

Requirements: hardware

1. For training smaller networks (VGG_CNN_F, VGG_CNN_M_1024), a GPU with about 6G of memory suffices.
2. For training lager networks (VGG16), you'll need a GPU with about 8G of memory.

Installation

1. Clone the CSC repository

# Make sure to clone with --recursive
git clone --recursive https://github.com/shenyunhang/CSC.git

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

   Ignore notes 1 and 2 if you followed step 1 above.

   Note 1: If you didn't clone CSC with the --recursive flag, then you'll need to manually clone the caffe-wsl submodule:

   git submodule update --init --recursive

   Note 2: The caffe-wsl submodule needs to be on the wsl branch (or equivalent detached state). This will happen automatically if you followed step 1 instructions.

3. Build the Cython modules

   cd $CSC_ROOT/lib
   make

4. Build Caffe and pycaffe

   cd $CSC_ROOT/caffe-wsl
   # 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

5. Download the training, validation, test data and VOCdevkit

   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

6. Extract all of these tars into one directory named VOCdevkit

   tar xvf VOCtrainval_06-Nov-2007.tar
   tar xvf VOCtest_06-Nov-2007.tar
   tar xvf VOCdevkit_08-Jun-2007.tar

7. It should have this basic structure

   $VOCdevkit/                           # development kit
   $VOCdevkit/VOCcode/                   # VOC utility code
   $VOCdevkit/VOC2007                    # image sets, annotations, etc.
   # ... and several other directories ...

8. Create symlinks for the PASCAL VOC dataset

   cd $CSC_ROOT/data
   ln -s $VOCdevkit VOCdevkit2007

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

9. [Optional] follow similar steps to get PASCAL VOC 2010 and 2012

10. [Optional] If you want to use COCO, please see some notes under data/README.md

11. Follow the next sections to download pre-trained ImageNet models

Download object proposals

1. Selective Search: original matlab code, python wrapper
2. EdgeBoxes: matlab code
3. MCG: matlab code

Download pre-trained ImageNet models

Pre-trained ImageNet models can be downloaded for the three networks described in the paper: ZF and VGG16.

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

Usage

To train and test a CSC detector, use experiments/scripts/csc.sh. Output is written underneath $CSC_ROOT/output.

cd $CSC_ROOT
./experiments/scripts/csc.sh [GPU_ID] [NET] [--set ...]
# GPU_ID is the GPU you want to train on
# NET in {VGG_CNN_F, VGG_CNN_M_1024, VGG16} is the network arch to use
# --set ... allows you to specify configure options, e.g.
#   --set EXP_DIR seed_rng1701 RNG_SEED 1701

Example:

./experiments/scripts/csc.sh 0 VGG16 pascal_voc --set EXP_DIR csc

This will reproduction the VGG16 result in paper.

Trained CSC networks are saved under:

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

Test outputs are saved under:

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

Other method

WSDDN:

./experiments/scripts/wsddn.sh 0 VGG16 pascal_voc --set EXP_DIR wsddn

or

./experiments/scripts/wsddn_x.sh 0 VGG16 pascal_voc --set EXP_DIR wsddn_x

ContextLocNet:

./experiments/scripts/contextlocnet.sh 0 VGG16 pascal_voc --set EXP_DIR contextlocnet

or

./experiments/scripts/contextlocnet_x.sh 0 VGG16 pascal_voc --set EXP_DIR contextlocnet_x