mindcv3/configs/res2net

• Res2Net
• • Introduction
  • Benchmark
  • • • Notes
  • Examples
  • • Train
    • Eval

Res2Net

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Res2Net: A New Multi-scale Backbone Architecture

Introduction

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We propose a novel building block for CNNs, namely Res2Net, by constructing hierarchical residual-like connections
within one single residual block. The Res2Net represents multi-scale features at a granular level and increases the
range of receptive fields for each network layer. The proposed Res2Net block can be plugged into the state-of-the-art
backbone CNN models, e.g. , ResNet, ResNeXt, BigLittleNet, and DLA. We evaluate the Res2Net block on all these models
and demonstrate consistent performance gains over baseline models.

Benchmark

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Model	Context	Top-1 (%)	Top-5 (%)	Params (M)	Train T.	Infer T.	Download	Config	Log
Res2Net50	D910x8-G	79.35	94.64	25.76	246s/epoch	28.5ms/step	model	cfg	log
Res2Net101	D910x8-G	79.56	94.70	45.33	467s/epoch	46.0ms/step	model	cfg	log
Res2Net50	D910x8-G	80.32	95.09	25.77	250s/epoch	29.6ms/step	model	cfg	log
Res2Net101-v1b	D910x8-G	81.26	95.41	45.35	435s/epoch	42.4ms/step	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.

  mpirun -n 8 python train.py --config configs/res2net/res2net_50_gpu.yaml --data_dir /path/to/imagenet
  

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 for res2net50 to verify the accuracy of your
  training.

  python validate.py --config configs/res2net/res2net_50_gpu.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/res2net50.ckpt