Vinicius de Paulo Souza Ribeiro
Department of Computer Engineering and Industrial Automation
School of Electrical and Computer Engineering
University of Campinas
Contact me:
E-mail: vinicius.ribeiro1@gmail.com
LinkedIn: https://www.linkedin.com/in/vribeiro1/
Requirements:
- Docker
- Python 3.5
How to run?
- Create a docker container with image
nvidia/cuda:9.1-devel-ubuntu16.04
nvidia-docker run --userns=host \
--shm-size 8G -ti \
-e OUTSIDE_USER=$USER \
-e OUTSIDE_UID=$UID \
-e OUTSIDE_GROUP=`/usr/bin/id -ng $USER` \
-e OUTSIDE_GID=`/usr/bin/id -g $USER` \
-v /path/to/repo/skin:/workspace/skin \
-v /path/to/data/isic2017:/datasets/isic2017 \
--name container_name \
nvidia/cuda:9.1-devel-ubuntu16.04 /bin/bash
- Update
apt-get
and install Ubuntu dependencies
apt-get update
apt-get install -y curl git wget python3 python3-pip python3-dev nano
- Install Python dependencies
cd workspace/skin
pip3 install -r requirements.txt
- Run the code
CUDA_VISIBLE_DEVICES=<gpu-id> python3 train.py with /path/to/config/file
Acknowledges:
The code available in the models package is based in the following repos:
- DeepLab V3+: https://github.com/jfzhang95/pytorch-deeplab-xception
- AutoDeepLab: https://github.com/MenghaoGuo/AutoDeeplab
- U-Net: https://github.com/learningtitans/isic2018-seg
- LinkNet: https://github.com/e-lab/pytorch-linknet
- RefineNet: https://github.com/thomasjpfan/pytorch_refinenet