Finetuned Affecthq
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Finetuned Affecthq
Developed by Piro17
An image classification model fine-tuned based on google/vit-base-patch16-224-in21k, trained on an image folder dataset with an evaluation accuracy of 71.79%.
Downloads 18
Release Time : 2/16/2023
Model Overview
This model is an image classification model based on the Vision Transformer (ViT) architecture, fine-tuned for specific image classification tasks.
Model Features
High-precision classification
Achieves 71.79% accuracy and 71.67% F1 score on the evaluation set.
ViT-based architecture
Utilizes the Vision Transformer architecture with powerful image feature extraction capabilities.
Fine-tuning
Fine-tuned on google/vit-base-patch16-224-in21k to adapt to specific classification tasks.
Model Capabilities
Image classification
Visual feature extraction
Multi-class recognition
Use Cases
Image analysis
General image classification
Classifies and identifies input images.
71.79% accuracy
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