mindcv_fixed/configs/rexnet

• ReXNet
• • Introduction
  • Results
  • • • Notes
  • Quick Start
  • • Train
    • Eval
    • Deployment (optional)

ReXNet

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ReXNet: Rethinking Channel Dimensions for Efficient Model Design

Introduction

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This is a new paradigm for network architecture design. ReXNet proposes a set of design principles to solve the Representational Bottleneck problem in the existing network. Rexnet combines these design principles with the existing network units to obtain a new network, RexNet, which achieves a great performance improvement.

Results

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Model	Context	Top-1 (%)	Top-5 (%)	Params (M)	Train T.	Infer T.	Download	Config	Log
rexnet_x09	D910x8-G	77.07	93.41				model	cfg	log
rexnet_x10	D910x8-G	77.38	93.60				model	cfg	log
rexnet_x13	D910x8-G	79.06	94.28				model	cfg	log
rexnet_x15	D910x8-G	79.94	94.74				model	cfg	log
rexnet_x20	D910x8-G	80.6	94.99				model	cfg	log

Notes

• All models are trained on ImageNet-1K training set and the top-1 accuracy is reported on the validatoin set.
• Context: GPU_TYPE x pieces - G/F, G - graph mode, F - pynative mode with ms function.

Quick Start

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Train

• The yaml config files that yield competitive results on ImageNet for different models are listed in the configs folder. To trigger training using preset yaml config.

  python train.py --config ./config/rexnet/rexnet_x10.yaml
  

• Here is the example for finetuning a pretrained rexnet x1.0 on CIFAR10 dataset using Adam optimizer.

  python train.py --model=rexnet_x10 --pretrained --opt=momentum --lr=0.001 dataset=cifar10 --num_classes=10 --dataset_download
  

Detailed adjustable parameters and their default value can be seen in config.py.

Eval

• To validate the model, you can use validate.py. Here is an example to verify the accuracy of pretrained weights.

  python validate.py --model=rexnet_x10 --dataset=imagenet --val_split=val --pretrained
  

• To validate the model, you can use validate.py. Here is an example to verify the accuracy of your training.

  python validate.py --model=rexnet_x10 --dataset=imagenet --val_split=val --ckpt_path='./rexnetx10_ckpt/rexnet-best.ckpt'
  

Deployment (optional)

Please refer to the deployment tutorial in MindCV.