yhsmiley / regnet

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

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

• facebookresearch/pycls
• pytorch/examples