regnet

Designing Network Design Spaces

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.

Introduction

We implement RegNetX models in 3D detection systems and provide their first results with PointPillars on nuScenes and Lyft dataset.

The pre-trained modles are converted from model zoo of pycls and maintained in mmcv.

Usage

To use a regnet model, there are two steps to do:

1. Convert the model to ResNet-style supported by MMDetection
2. Modify backbone and neck in config accordingly

Convert model

We already prepare models of FLOPs from 800M to 12G in our model zoo.

For more general usage, we also provide script regnet2mmdet.py in the tools directory to convert the key of models pretrained by pycls to ResNet-style checkpoints used in MMDetection.

python -u tools/model_converters/regnet2mmdet.py ${PRETRAIN_PATH} ${STORE_PATH}

This script convert model from PRETRAIN_PATH and store the converted model in STORE_PATH.

Modify config

The users can modify the config's depth of backbone and corresponding keys in arch according to the configs in the pycls model zoo. The parameter in_channels in FPN can be found in the Figure 15 & 16 of the paper (wi in the legend). This directory already provides some configs with their performance, using RegNetX from 800MF to 12GF level. For other pre-trained models or self-implemented regnet models, the users are responsible to check these parameters by themselves.

Note: Although Fig. 15 & 16 also provide w0, wa, wm, group_w, and bot_mul for arch, they are quantized thus inaccurate, using them sometimes produces different backbone that does not match the key in the pre-trained model.

Results and models

nuScenes

Backbone	Lr schd	Mem (GB)	Inf time (fps)	mAP	NDS	Download
SECFPN	2x	16.4		35.17	49.7	model | log
RegNetX-400MF-SECFPN	2x	16.4		41.2	55.2	model | log
FPN	2x	17.1		40.0	53.3	model | log
RegNetX-400MF-FPN	2x	17.3		44.8	56.4	model | log
RegNetX-1.6gF-FPN	2x	24.0		48.2	59.3	model | log

Lyft

Backbone	Lr schd	Mem (GB)	Inf time (fps)	Private Score	Public Score	Download
SECFPN	2x	12.2		13.9	14.1	model | log
RegNetX-400MF-SECFPN	2x	15.9		14.9	15.1	model | log
FPN	2x	9.2		14.9	15.1	model | log
RegNetX-400MF-FPN	2x	13.0		16.0	16.1	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}
}