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signatrix
128 Stars 21 Forks MIT License 24 Commits 1 Opened issues

Description

Pytorch implementation of network design paradigm described in the paper "Designing Network Design Spaces"

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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

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