mindcv3/configs/rexnet

• ReXNet
• • Introduction
  • Benchmark
  • Examples
  • • Train
    • Eval

ReXNet

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ReXNet: Rethinking Channel Dimensions for Efficient Model Design

Introduction

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Rank Expansion Networks (ReXNets) follow a set of new design principles for designing bottlenecks in image classification models. Authors refine each layer by 1) expanding the input channel size of the convolution layer and 2) replacing the ReLU6s.

Benchmark

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				Pynative	Pynative	Graph	Graph
	Model	Top-1 (%)	Top-5 (%)	train (s/epoch)	Infer (ms)	train(s/epoch)	Infer (ms)	Download	Config
GPU	rexnet_x09							model	config
Ascend	rexnet_x09
GPU	rexnet_x10							model	config
Ascend	rexnet_x10
GPU	rexnet_x13							model	config
Ascend	rexnet_x13
GPU	rexnet_x15							model	config
Ascend	rexnet_x15
GPU	rexnet_x20							model	config
Ascend	rexnet_x20

Examples

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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'