constantinpape / SuperHuman

Superhuman accuracy on the SNEMI3D connectomics challenge

Superhuman accuracy on the SNEMI3D connectomics challenge

Introduction

This repository is the reproduced implementation of the paper, "Superhuman accuracy on the SNEMI3D connectomics challenge".

Installation

This code was tested with Pytorch 1.0.1 (later versions may work), CUDA 9.0, Python 3.7.4 and Ubuntu 16.04. It is worth mentioning that, besides some commonly used image processing packages, you also need to install some special post-processing packages for neuron segmentation, such as waterz and elf.

If you have a Docker environment, we strongly recommend you to pull our image as follows,

docker pull registry.cn-hangzhou.aliyuncs.com/renwu527/auto-emseg:v5.4

or

docker pull renwu527/auto-emseg:v5.4

Dataset

Datasets	Training set	Validation set	Test set	Download (Processed)
AC3/AC4	1024x1024x80 (AC4)	1024x1024x20 (AC4)	1024x1024x100 (AC3)	BaiduYun (Access code: weih) or GoogleDrive

Download and unzip them in corresponding folders in './data'.

Model Zoo

Datasets	Models	Download
AC3/AC4	ac3ac4-test.ckpt	BaiduYun (Access code: weih) or GoogleDrive

Training and Inference

cd ./scripts

1. Training

python main.py -c=seg_3d

2. Inference

python inference.py -c=seg_3d -mn=ac3ac4 -id=ac3ac4-test -m=ac3

Output:

waterz: voi_split=1.095144, voi_merge=0.342404, voi_sum=1.437549, arand=0.168990

LMC: voi_split=1.144543, voi_merge=0.262998, voi_sum=1.407541, arand=0.122037

Contact

If you have any problem with the released code, please do not hesitate to contact me by email ([e-mail]).