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

Model	Params(M)	FLOPs(G)	Top-1 (%)	Top-5 (%)
EPSANet-50(Small)	22.56	3.62	77.49	93.54
EPSANet-50(Large)	27.90	4.72	78.64	94.18
EPSANet-101(Small)	38.90	6.82	78.43	94.11
EPSANet-101(Large)	49.59	8.97	79.38	94.58

Object Detection on MS-COCO 2017

Faster R-CNN

model	Style	Lr schd	Params(M)	FLOPs(G)	box AP	AP_50	AP_75
EPSANet-50(small)	pytorch	1x	38.56	197.07	39.2	60.3	42.3
EPSANet-50(large)	pytorch	1x	43.85	219.64	40.9	62.1	44.6

Mask R-CNN

model	Style	Lr schd	Params(M)	FLOPs(G)	box AP	AP_50	AP_75
EPSANet-50(small)	pytorch	1x	41.20	248.53	40.0	60.9	43.3
EPSANet-50(large)	pytorch	1x	46.50	271.10	41.4	62.3	45.3

RetinaNet

model	Style	Lr schd	Params(M)	FLOPs(G)	box AP	AP_50	AP_75
EPSANet-50(small)	pytorch	1x	34.78	229.32	38.2	58.1	40.6
EPSANet-50(large)	pytorch	1x	40.07	251.89	39.6	59.4	42.3

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

model	Params(M)	FLOPs(G)	AP	AP_50	AP_75
EPSANet-50(small)	41.20	248.53	35.9	57.7	38.1
EPSANet-50(Large)	46.50	271.10	37.1	59.0	39.5

murufeng / EPSANet

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