ViT

Introduction

Vision Transformer (ViT) achieves remarkable results compared to convolutional neural networks (CNN) while obtaining fewer computational resources for pre-training. In comparison to convolutional neural networks (CNN), Vision Transformer (ViT) shows a generally weaker inductive bias resulting in increased reliance on model regularization or data augmentation (AugReg) when training on smaller datasets.

The ViT is a visual model based on the architecture of a transformer originally designed for text-based tasks, as shown in the below figure. The ViT model represents an input image as a series of image patches, like the series of word embeddings used when using transformers to text, and directly predicts class labels for the image. ViT exhibits an extraordinary performance when trained on enough data, breaking the performance of a similar state-of-art CNN with 4x fewer computational resources. [2]

Figure 1. Architecture of ViT [1]

Results

Our reproduced model performance on ImageNet-1K is reported as follows.

Model

Context

Top-1 (%)

Top-5 (%)

Params (M)

Recipe

Download

vit_b_32_224

D910x8-G

75.86

92.08

87.46

yaml

weights

vit_l_16_224

D910x8-G

76.34

92.79

303.31

yaml

weights

vit_l_32_224

D910x8-G

73.71

90.92

305.52

yaml

weights

Notes

  • Context: Training context denoted as {device}x{pieces}-{MS mode}, where mindspore mode can be G - graph mode or F - pynative mode with ms function. For example, D910x8-G is for training on 8 pieces of Ascend 910 NPU using graph mode.

  • Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K.

Quick Start

Preparation

Installation

Please refer to the installation instruction in MindCV.

Dataset Preparation

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

Training

  • Distributed Training

It is easy to reproduce the reported results with the pre-defined training recipe. For distributed training on multiple Ascend 910 devices, please run

# distrubted training on multiple GPU/Ascend devices
mpirun -n 8 python train.py --config configs/vit/vit_b32_224_ascend.yaml --data_dir /path/to/imagenet

If the script is executed by the root user, the --allow-run-as-root parameter must be added to mpirun.

Similarly, you can train the model on multiple GPU devices with the above mpirun command.

For detailed illustration of all hyper-parameters, please refer to config.py.

Note: As the global batch size (batch_size x num_devices) is an important hyper-parameter, it is recommended to keep the global batch size unchanged for reproduction or adjust the learning rate linearly to a new global batch size.

  • Standalone Training

If you want to train or finetune the model on a smaller dataset without distributed training, please run:

# standalone training on a CPU/GPU/Ascend device
python train.py --config configs/vit/vit_b32_224_ascend.yaml --data_dir /path/to/dataset --distribute False

Validation

To validate the accuracy of the trained model, you can use validate.py and parse the checkpoint path with --ckpt_path.

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

Deployment

To deploy online inference services with the trained model efficiently, please refer to the deployment tutorial.

References

[1] Dosovitskiy A, Beyer L, Kolesnikov A, et al. An image is worth 16x16 words: Transformers for image recognition at scale[J]. arXiv preprint arXiv:2010.11929, 2020.

[2] “Vision Transformers (ViT) in Image Recognition – 2022 Guide”, https://viso.ai/deep-learning/vision-transformer-vit/