MONet

Using Multi-label Classification to Improve Object Detection

MONet is modified based on py-R-FCN-priv, thanks for soeaver's job.

Disclaimer

The official R-FCN code (written in MATLAB) is available here.

The official R-FCN code (written in PYTHON) is available here.

Installation

Clone the MONet repository
```
git clone https://github.com/GT9505/MONet
```
We'll call the directory that you cloned MONet into MONET_ROOT
Build the Cython modules
```
cd $MONET_ROOT/lib
make
```

Build Caffe and pycaffe

cd $MONET_ROOT/caffe
# Now follow the Caffe installation instructions here:
#   http://caffe.berkeleyvision.org/installation.html

# cp Makefile.config.example Makefile.config
# If you're experienced with Caffe and have all of the requirements installed
# and your Makefile.config in place, then simply do:
make all -j && make pycaffe -j

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
# NCCL (https://github.com/NVIDIA/nccl) is necessary for multi-GPU training with python layer
USE_NCCL := 1

Preparation for Training & Testing

Please follow the official py-R-FCN code to preparation training set and testing set

Please download backbone network ResNet-101 in here

Start training

The usage is same as py-R-FCN-priv

cd $MONet_ROOT
./experments/scripts/monet_end2end_ohem_multi_gpu.sh 0 pascal_voc

Results on PASCAL VOC 2007

Using the default hyperparameters and iterations, you can achieve a mAP around 83.0% (no multi-scale training). The model with 83.0% mAP. Using multi-scale training can further improve the mAP to 83.6%.

License

MONet is released under the MIT License (refer to the LICENSE file for details).

Citing

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

@article{gong18monet,
    Author = {Tao Gong, Bin Liu, Qi Chu, Nenghai Yu},
    Title = {Using Multi-label Classification to Improve Object Detection},
    Journal = {Submitted to Neurocomputing},
    Year = {2018.09}
}

GT9505/MONet