regnet

RegNet

Designing Network Design Spaces

Abstract

In this work, we present a new network design paradigm. Our goal is to help advance the understanding of network design and discover design principles that generalize across settings. Instead of focusing on designing individual network instances, we design network design spaces that parametrize populations of networks. The overall process is analogous to classic manual design of networks, but elevated to the design space level. Using our methodology we explore the structure aspect of network design and arrive at a low-dimensional design space consisting of simple, regular networks that we call RegNet. The core insight of the RegNet parametrization is surprisingly simple: widths and depths of good networks can be explained by a quantized linear function. We analyze the RegNet design space and arrive at interesting findings that do not match the current practice of network design. The RegNet design space provides simple and fast networks that work well across a wide range of flop regimes. Under comparable training settings and flops, the RegNet models outperform the popular EfficientNet models while being up to 5x faster on GPUs.

How to use it?

Predict image

from mmpretrain import inference_model

predict = inference_model('regnetx-400mf_8xb128_in1k', 'demo/bird.JPEG')
print(predict['pred_class'])
print(predict['pred_score'])

Use the model

import torch
from mmpretrain import get_model

model = get_model('regnetx-400mf_8xb128_in1k', pretrained=True)
inputs = torch.rand(1, 3, 224, 224)
out = model(inputs)
print(type(out))
# To extract features.
feats = model.extract_feat(inputs)
print(type(feats))

Train/Test Command

Prepare your dataset according to the docs.

Train:

python tools/train.py configs/regnet/regnetx-400mf_8xb128_in1k.py

Test:

python tools/test.py configs/regnet/regnetx-400mf_8xb128_in1k.py https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-400mf_8xb128_in1k_20211213-89bfc226.pth

Models and results

Image Classification on ImageNet-1k

Model	Pretrain	Params (M)	Flops (G)	Top-1 (%)	Top-5 (%)	Config	Download
regnetx-400mf_8xb128_in1k	From scratch	5.16	0.41	72.56	90.78	config	model | log
regnetx-800mf_8xb128_in1k	From scratch	7.26	0.81	74.76	92.32	config	model | log
regnetx-1.6gf_8xb128_in1k	From scratch	9.19	1.63	76.84	93.31	config	model | log
regnetx-3.2gf_8xb64_in1k	From scratch	3.21	1.53	78.09	94.08	config	model | log
regnetx-4.0gf_8xb64_in1k	From scratch	22.12	4.00	78.60	94.17	config	model | log
regnetx-6.4gf_8xb64_in1k	From scratch	26.21	6.51	79.38	94.65	config	model | log
regnetx-8.0gf_8xb64_in1k	From scratch	39.57	8.03	79.12	94.51	config	model | log
regnetx-12gf_8xb64_in1k	From scratch	46.11	12.15	79.67	95.03	config	model | log

Citation

@article{radosavovic2020designing,
    title={Designing Network Design Spaces},
    author={Ilija Radosavovic and Raj Prateek Kosaraju and Ross Girshick and Kaiming He and Piotr Dollár},
    year={2020},
    eprint={2003.13678},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}