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Vit Base Patch16 224 In21k
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Vit Base Patch16 224 In21k

Developed by Xenova
Vision model based on Transformer architecture, processing 224x224 resolution input through 16x16 image patches, pre-trained on ImageNet-21k dataset
Image Classification
Transformers
#Web-based Image Classification#ONNX Format Compatibility#High-precision Vision Model
Downloads 132
Release Time : 5/3/2023

Model Overview

This model employs a pure Transformer architecture for image classification tasks, breaking the limitations of traditional CNNs by dividing images into fixed-size patches and modeling global relationships through self-attention mechanisms

Model Features

Pure Transformer Architecture
Processes images entirely based on self-attention mechanisms without convolutional operations
Global Context Modeling
Captures global dependencies in images through Transformer's self-attention mechanism
Efficient Image Patch Processing
Divides images into 16x16 pixel patches as input sequences

Model Capabilities

Image Feature Extraction
Image Classification
Transfer Learning Foundation Model

Use Cases

Computer Vision
General Image Classification
Classifies natural images into 1000 categories
Achieves approximately 80% top-1 accuracy on ImageNet validation set (estimated)
Transfer Learning Foundation
Adapts to domain-specific image recognition tasks through fine-tuning
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