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Vit Base Railspace

Developed by Kaspar
A Vision Transformer model fine-tuned from google/vit-base-patch16-224-in21k, achieving 99.26% accuracy on the evaluation set
Image Classification
Transformers
Open Source License:Apache-2.0#High-precision image classification#Few-shot fine-tuning#Transfer learning
Downloads 18
Release Time : 3/13/2023

Model Overview

This model is a Vision Transformer optimized for image classification tasks, excelling on specific datasets, particularly in high-precision classification tasks.

Model Features

High accuracy
Achieves 99.26% accuracy on the evaluation set, demonstrating excellent performance
Based on ViT architecture
Utilizes the Vision Transformer base architecture with powerful image feature extraction capabilities
Efficient fine-tuning
Requires only 4 training epochs to achieve high performance

Model Capabilities

Image classification
High-precision recognition
Multi-category differentiation

Use Cases

Image analysis
Map image recognition
Can be used to identify and analyze specific elements in map images
From example images, the model can accurately recognize map image tiles
Industrial quality inspection
Suitable for product quality inspection on production lines
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