Vit Base Railspace
V
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
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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