This repo contains the official Pytorch implementaion code.

Installation

Requirements

• Python 3.6+
• PyTorch 1.0+

Our environments

• OS: Ubuntu 18.04
• CUDA: 10.0
• Toolkit: PyTorch 1.0
• GPU: Titan RTX

Data preparation

Download and extract ImageNet train and val images from http://image-net.org/. The directory structure is the standard layout for the torchvision datasets.ImageFolder, and the training and validation data is expected to be in the train/ folder and val folder respectively:

/path/to/imagenet/
  train/
    class1/
      img1.jpeg
    class2/
      img2.jpeg
  val/
    class1/
      img3.jpeg
    class/2
      img4.jpeg

Usage

First, clone the repository locally:

git clone https://github.com/murufeng/EPSANet.git
cd EPSANet

• Create a conda virtual environment and activate it:

conda create -n epsanet python=3.6 
conda activate epsanet

• Install CUDA==10.0 with cudnn7 following the official installation instructions
• Install PyTorch==1.0.1 and torchvision==0.2.0 with CUDA==10.0:

conda install -c pytorch pytorch torchvision

Training

To train models on ImageNet with 8 gpus run:

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python main.py -a epsanet50 --data /path/to/imagenet 

Model Zoo

Models are trained with 8 GPUs on both ImageNet and MS-COCO 2017 dataset.

Image Classification on ImageNet

Object Detection on MS-COCO 2017

Faster R-CNN

Mask R-CNN

RetinaNet

Instance segmentation with Mask R-CNN on MS-COCO 2017