vision-transformer-from-scratch and ViT_PyTorch

ViT_PyTorch
25
Experimental
Maintenance 0/25
Adoption 10/25
Maturity 16/25
Community 20/25
Maintenance 0/25
Adoption 7/25
Maturity 8/25
Community 10/25
Stars: 241
Forks: 41
Downloads:
Commits (30d): 0
Language: Jupyter Notebook
License: MIT
Stars: 25
Forks: 3
Downloads:
Commits (30d): 0
Language: Python
License:
Stale 6m No Package No Dependents
No License Stale 6m No Package No Dependents

About vision-transformer-from-scratch

tintn/vision-transformer-from-scratch

A Simplified PyTorch Implementation of Vision Transformer (ViT)

This project provides a clear and straightforward example of how a Vision Transformer (ViT) model is constructed and trained for image classification. It takes image datasets as input and outputs a trained model capable of classifying images into predefined categories. This is ideal for machine learning researchers or students who want to understand the inner workings of ViT models.

deep-learning-education computer-vision-research image-classification-learning transformer-models academic-code-examples

About ViT_PyTorch

godofpdog/ViT_PyTorch

This is a simple PyTorch implementation of Vision Transformer (ViT) described in the paper "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale"

This project helps machine learning engineers and researchers quickly set up and train a Vision Transformer (ViT) model for image classification tasks. You input a dataset of images, and it outputs a trained model capable of categorizing new images. This is for professionals building advanced computer vision systems.

image-classification deep-learning computer-vision model-training vision-transformers

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