Here is our pytorch pytorch implementation of network design paradigm described in the paper "Designing Network Design Spaces"
Design space design
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... |
Top RegNetX models
Top RegNetY models
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:
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.
data ├── train │ ├── n01440764 │ └── n01443537 │ └── ... │── val │ ├── n01440764 │ └── n01443537 │ └── ...
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
- python 3.7
- pytorch 1.4
- opencv (cv2)
- pthflops
- torchsummary
Complete all networks and training script. We are training RegnetY models and will update result soon.