FastMask
FastMask: Segment Multi-scale Object Candidates in One Shot
(Published as a spotlight paper at CVPR 2017 in Honolulu, Hawaii)
Hexiang Hu*, Shiyi Lan*, Yuning Jiang, Zhimin Cao, Fei Sha (*Equal contribution. Work was done during their internships at Megvii Inc.)
If you are using code or other related resources from this repository, please cite the following paper:
@article{hu2016fastmask,
title={FastMask: Segment Multi-scale Object Candidates in One Shot},
author={Hu, Hexiang and Lan, Shiyi and Jiang, Yuning and Cao, Zhimin and Sha, Fei},
journal={arXiv preprint arXiv:1612.08843},
year={2016}
}
Requirements and Dependencies
- MAC OS X or Linux
- NVIDIA GPU with compute capability 3.5+
- Python 2.7+
- COCOApi, redis, python-cjson, opencv2, numpy
- Alchemy, caffe-fm
Quick Start
Step in common
We highly recommend you to use anaconda2 on ubuntu 14.04, which is our main experimental environment.
For ubuntu 14.04
sudo apt-get update
sudo apt-get install python-opencv python-pip
and then install cocoapi see COCOApi
git clone --recursive https://github.com/voidrank/FastMask
cd FastMask
mkdir params results
pip install -r requirements.txt
cd caffe-fm
make pycaffe -j 4
cd ..
Demonstrate
Download parameters of pretrained models
Download final model and save it in ./params
Image Demo
python image_demo.py [gpu_id] [model] [input_image_path] [--init_weights=weights] [--threshold=0.9]
This instruction will segment the image at [input_image_path] with models/[model].test.prototxt and params/[weights].caffemodel
There is an example: (suppose that the input image named input.jpg is at ./)
python image_demo.py 0 fm-res39 input.jpg --init_weights=fm-res39_final_params.caffemodel
Video Demo
python video_demo.py [gpu_id] [model] [input_video_path] [output_video_path] [--init_weights=weights] [--threshold=0.9]
Training
Set Up Redis Server for Multiprocess Communication
nohup redis-server redis.conf
Download COCO
cd data
wget http://msvocds.blob.core.windows.net/coco2014/train2014.zip http://msvocds.blob.core.windows.net/annotations-1-0-3/instances_train-val2014.zip http://msvocds.blob.core.windows.net/coco2014/val2014.zip
unzip train.zip instances_train_val2014.zip val2014.zip
cd ..
Download pretrained model on imagenet
Download ResNet-50-model.caffemodel and save it in ./params
Training
python train.py [gpu_id] [model] [--init_weights=ResNet-50-model.caffemodel] [--process=4]
For examples,
python train.py 0 fm-res39 --init_weights=ResNet-50-model.caffemodel
Evaluation
python test.py [gpu_id] [model] [--init_weights=xxx.caffemodel]
python evalCOCO.py [model]