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Regnet

Introduction

Here is our pytorch pytorch implementation of network design paradigm described in the paper "Designing Network Design Spaces"


Design space design

Comparison

P: Paper's. O: Our

Model [P/O] gflops [P/O] params [P/O] top-1 error
RerNetY-200MF 0.2/0.22 3.2/3.27 29.6/updating...
RerNetY-400MF 0.4/0.42 4.3/4.45 25.9/updating...
RerNetY-600MF 0.6/0.60 6.1/5.66 24.5/updating...
RerNetY-800MF 0.8/0.82 6.3/6.26 23.7/updating...

Best models


Top RegNetX models


Top RegNetY models

Datasets

We use Imagenet (ILSVRC2012) for all experiments, as stated in the paper.

Create a data folder under this repository,

cd {repo_root}
mkdir data
  • ImageNet: Download the ImageNet dataset and put the files as the following structure: 
    data
├── train
│   ├── n01440764
│   └── n01443537
│   └── ...
│── val
│   ├── n01440764
│   └── n01443537
│   └── ...
    Of course you could change this path to whatever you want based on your own preference, or mount it to a folder when using docker.

How to use our code

With our code, you can:

  • Train your model with default arguments by running python train.py -d path/to/image/root/folder
  • We also provide shell scripts which could be used to run training for first RegnetY models at ./scripts/. For example, if you want to train RegNetY 800MF, you could simply run ./scripts/RegnetY_800MF.sh

Requirements

  • python 3.7
  • pytorch 1.4
  • opencv (cv2)
  • pthflops
  • torchsummary

Updating (21/04/2020)

Complete all networks and training script. We are training RegnetY models and will update result soon.

References