mindcv_latest/configs/vit

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

ViT

[2010.11929] An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (arxiv.org)

Introduction

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While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited. In vision, attention is either applied in conjunction with convolutional networks, or used to replace certain components of convolutional networks while keeping their overall structure in place. We show that this reliance on CNNs is not necessary and a pure transformer applied directly to sequences of image patches can perform very well on image classification tasks. When pre-trained on large amounts of data and transferred to multiple mid-sized or small image recognition benchmarks (ImageNet, CIFAR-100, VTAB, etc.), Vision Transformer (ViT) attains excellent results compared to state-of-the-art convolutional networks while requiring substantially fewer computational resources to train.

Results

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Model	Context	Top-1 (%)	Top-5 (%)	Params(M)	Train T.	Infer T.	Download	Config	Log
vit_b_32_224	D910x8-G	75.86	92.08	86	619ms/step	11.6ms/step	model	cfg	log
vit_l_16_224	D910x8-G	76.34	92.79	307	632ms/step	5.37ms/step	model	cfg	log
vit_l_32_224	D910x8-G	73.71	90.92	307	534ms/step	6.22ms/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/vit folder. For example, to train with one of these configurations, you can run:

  # train vit on 8 NPUs
  mpirun -n 8 python train.py -c configs/vit/vit_b32_224_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/Ascneds with the same batch size.

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

Validation

• To validate the trained model, you can use validate.py. Here is an example for vit_b_32 to verify the accuracy of pretrained weights.

  python validate.py -c configs/vit/vit_b32_224_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt
  

Deployment (optional)

Please refer to the deployment tutorial in MindCV.