Deep Variational Instance Segmentation

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A simple, fully convolutional model for real-time instance segmentation. This is the code for our papers:

Deep Variational Instance Segmentation

The implementation of backbone network is based on repository: Yolact-github

Installation

Clone this repository and enter it:

git clone https://github.com/jia2lin3yuan1/2020-instanceSeg.git $PRJ_NAME
cd  $PRJ_NAME

Set up the environment using one of the following methods:
- Using Anaconda
  - Run conda env create -f environment.yml
- Manually with pip
  - Set up a Python3 environment (e.g., using virtenv).
  - Install Pytorch 1.0.1 (or higher) and TorchVision.
  - Install some other packages:
```
# Cython needs to be installed before pycocotools
pip install cython
pip install opencv-python pillow pycocotools matplotlib 
pip install scikit-image
pip install scipy==1.2.0
```
If you'd like to train DVIS, download the COCO dataset and the 2014/2017 annotations. Note that this script will take a while and dump 21gb of files into ./data/coco.
```
sh data/scripts/COCO.sh
```
If you'd like to evaluate DVIS on test-dev, download test-dev with this script.
```
sh data/scripts/COCO_test.sh
```
Install pymeanshift following instruction on Install

Train on your own dataset:

You could edit the config_xx.py in data/ to customize the network setting and dataset setting.

You could run with specific the arguments on shell command:

 python train.py --config=plus_resnet50_config_550 --resume=PATH/TO/YOUR/FILE --start_iter=0 --exp_name=dummy

Or, you could customize the json script in exp_scripts/, and run with:
```
 python train.py --scripts=exp_scripts/xxx.json
```