https://github.com/imvision12/vit-tf
A Tensorflow Implementation of "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (Vision Transformer)"
https://github.com/imvision12/vit-tf
cnn keras tensorflow transformer
Last synced: 2 months ago
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A Tensorflow Implementation of "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (Vision Transformer)"
- Host: GitHub
- URL: https://github.com/imvision12/vit-tf
- Owner: IMvision12
- Created: 2023-02-23T00:24:37.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-02-25T21:25:37.000Z (over 3 years ago)
- Last Synced: 2026-03-27T18:50:49.686Z (3 months ago)
- Topics: cnn, keras, tensorflow, transformer
- Language: Python
- Homepage:
- Size: 567 KB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Vision Transformer
This repository is about an implementation of the research paper "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
" using Tensorflow.
The Vision Transformer, or ViT, is a model for image classification that employs a Transformer-like architecture over patches of the image. An image is split into fixed-size patches, each of them are then linearly embedded, position embeddings are added, and the resulting sequence of vectors is fed to a standard Transformer encoder. In order to perform classification, the standard approach of adding an extra learnable “classification token” to the sequence is used.
# Patches of an Image
# Model Architecture
# Details of Variants of vision transformer(Huge, Base, Large)
# References
[1] ViT paper: https://arxiv.org/abs/2010.11929
[2] Official ViT Repo: https://github.com/google-research/vision_transformer