mindcv_fixed/configs/repvgg

• RepVGG
• • Introduction
  • Benchmark
  • • • Notes
  • Examples
  • • Train
    • Eval

RepVGG

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RepVGG: Making VGG-style ConvNets Great Again

Introduction

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RepVGG, a vgg-style architecture that outperforms many complex models

Its main highlights are:

1. The model has a normal (a.k.a. feedforward) structure like vgg, without any other branches, each layer takes the
   output of its only previous layer as input, and feeds the output to its only next layer.

2. The body of the model uses only 3 × 3 conv and ReLU.

3. The specific architecture (including specific depth and layer width) is instantiated without automatic search, manual
   refinement, compound scaling, and other complicated designs.

Benchmark

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Model	Context	Top-1 (%)	Top-5 (%)	Params (M)	Train T.	Infer T.	Download	Config	Log
RepVGG_A0	GPU	71.98	90.36				model	cfg	log
RepVGG_A0	D910x8-G	71.87	90.43				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.

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.

  comming soon
  

• Here is the example for finetuning a pretrained RepVGG_A0 on CIFAR10 dataset using Adam optimizer.

  python train.py --model=RepVGG_A0 --pretrained --opt=adam --lr=0.001 ataset=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=RepVGG_A0 --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=RepVGG_A0 --dataset=imagenet --val_split=val --ckpt_path='./ckpt/RepVGG_A0-best.ckpt'