Vitforimageclassification
This model is a fine-tuned image classification model based on google/vit-base-patch16-224-in21k on the CIFAR10 dataset, achieving an accuracy of 96.78%.
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Release Time : 11/28/2023
Model Overview
Vision Transformer (ViT) image classification model, suitable for general image classification tasks.
Model Features
High accuracy
Achieves 96.78% classification accuracy on the CIFAR10 dataset
Transformer-based architecture
Utilizes the Vision Transformer architecture, processing images with self-attention mechanisms
Pre-trained fine-tuning
Fine-tuned from a large-scale pre-trained model to adapt to specific classification tasks
Model Capabilities
Image classification
Feature extraction
Use Cases
Computer vision
General image classification
Classify and recognize common object images
Achieves 96.78% accuracy on CIFAR10
Image understanding
Extract image features for downstream tasks
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