Skip to content
master
Switch branches/tags
Go to file
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
lib
 
 
 
 
 
 
 
 
 
 
 
 

SIN

Structure Inference Net: Object Detection Using Scene-level Context and Instance-level Relationships. In CVPR 2018.(http://vipl.ict.ac.cn/uploadfile/upload/2018041318013480.pdf)

Requirements: software

  1. Requirements for Tensorflow 1.3.0 (see: Tensorflow)

  2. Python packages you might not have: cython, python-opencv, easydict

Installation (sufficient for the demo)

  1. Clone the SIN repository
# Make sure to clone with --recursive
git clone --recursive https://github.com/choasUp/SIN.git
  1. Build the Cython modules
cd $SIN_ROOT/lib
make

Demo

After successfully completing basic installation, you'll be ready to run the demo.

Wait ...

Training Model

  1. 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
  2. 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
  3. It should have this basic structure

    $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

    cd $SIN_ROOT/data
    ln -s $VOCdevkit VOCdevkit
  5. Download the pre-trained ImageNet models [Google Drive] [Dropbox]

     mv VGG_imagenet.npy $SIN_ROOT/data/pretrain_model/VGG_imagenet.npy
  6. [optional] Set learning rate and max iter

    vim experiments/scripts/faster_rcnn_end2end.sh 		# ITERS
    vim lib/fast/config.py 					# LR
    cd lib	 						# if you edit the code, make best
    make
  7. Set your GPU id, then run script to train and test model

    cd $SIN_ROOT
    export CUDA_VISIBLE_DEVICSE=0
    ./train.sh
  8. Test your dataset

    ./test_all.sh

The result of testing on PASCAL VOC 2007 (VGG net)

AP for aeroplane = 0.7853
AP for bicycle = 0.8045
AP for bird = 0.7456
AP for boat = 0.6657
AP for bottle = 0.6144
AP for bus = 0.8424
AP for car = 0.8663
AP for cat = 0.8894
AP for chair = 0.5803
AP for cow = 0.8466
AP for diningtable = 0.7171
AP for dog = 0.8578
AP for horse = 0.8626
AP for motorbike = 0.7802
AP for person = 0.7857
AP for pottedplant = 0.4869
AP for sheep = 0.7599
AP for sofa = 0.7351
AP for train = 0.8199
AP for tvmonitor = 0.7683
Mean AP = 0.7607

References

Faster R-CNN caffe version

Faster R-CNN tf version

Citation

Yong Liu, Ruiping Wang, Shiguang Shan, and Xilin Chen. Structure Inference Net: Object Detection Using Scene-level Context and Instance-level Relationships. In CVPR 2018.

About

CVPR 2018: Structure Inference Net for Object Detection

Topics

Resources

Releases

No releases published

Packages

No packages published