mindcv_fixed/configs/visformer

• Visformer
• • Introduction
  • Results
  • • • Notes
  • Quick Start
  • • Preparation
    • • Installation
      • Dataset Preparation
    • Training
    • Validation
    • Deployment (optional)

Visformer

Visformer: The Vision-friendly Transformer

Introduction

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The past few years have witnessed the rapid development of applying the Transformer module to vision problems. While some
researchers have demonstrated that Transformer based models enjoy a favorable ability of fitting data, there are still
growing number of evidences showing that these models suffer over-fitting especially when the training data is limited.
This paper offers an empirical study by performing step-bystep operations to gradually transit a Transformer-based model
to a convolution-based model. The results we obtain during the transition process deliver useful messages for improving
visual recognition. Based on these observations, we propose a new architecture named Visformer, which is abbreviated from
the ‘Vision-friendly Transformer’.

Results

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Model	Context	Top-1 (%)	Top-5 (%)	Params (M)	Train T.	Infer T.	Download	Config	Log
visformer_tiny	D910x8-G	78.61	94.33	10	353s/epoch	10.9ms/step	model	cfg	log
visformer_tiny2	D910x8-G	78.62	94.36	9	390s/epoch	11.5ms/step	model	cfg	log
visformer_small	D910x8-G	81.77	95.72	40	440s/epoch	15.3ms/step	model	cfg	log
visformer_small2	D910x8-G	82.17	95.90	23	450s/epoch	19.2ms/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.

Quick Start

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Preparation

Installation

Please refer to the installation instruction in MindCV.

Dataset Preparation

Please download the ImageNet-1K dataset for model training and validation.

Training

• Hyper-parameters. The hyper-parameter configurations for producing the reported results are stored in the yaml
  files in mindcv/configs/visformer folder. For example, to train with one of these configurations, you can run:

  # train densenet121 on 8 GPUs
  mpirun -n 8 python train.py --config configs/visformer/visformer_tiny_ascend.yaml --data_dir /path/to/imagenet
  

  Note that the number of GPUs/Ascends and batch size will influence the training results. To reproduce the training result at most, it is recommended to use the same number of GPUs/Ascends with the same batch size.

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

Validation

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

  python validate.py --config configs/visformer/visformer_tiny_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/visformer_tiny.ckpt
  

Deployment (optional)

Please refer to the deployment tutorial in MindCV.