Google-ViT-Base-Patch16-224-Face Open-Source Image Classification Model

Google Vit Base Patch16 224 Face

Developed by jayanta

A Vision Transformer model fine-tuned on an image folder dataset based on google/vit-base-patch16-224 for image classification tasks.

Image Classification

Transformers

Open Source License:Apache-2.0 #ViT Image Classification #Facial Feature Extraction #High-Precision Classification

Downloads 18

Release Time : 1/12/2023

Model Overview

This model is an image classification model based on the Vision Transformer (ViT) architecture, fine-tuned for specific domain image recognition tasks.

Model Features

Based on ViT Architecture

Utilizes the Vision Transformer architecture with self-attention mechanisms for image data processing

Fine-Tuned Version

Fine-tuned on the base model to adapt to specific image classification tasks

Medium-Scale Model

Employs a base-scale ViT model, balancing performance and computational resource requirements

Model Capabilities

Image Classification

Feature Extraction

Visual Pattern Recognition

Use Cases

Computer Vision

Facial Image Classification

Classifies and recognizes images containing human faces

Achieves 72.49% accuracy on the evaluation set

General Image Classification

Classifies and recognizes various types of images

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.8514	1.0	290	0.8464	0.7048	0.7035	0.7048	0.6909
0.7202	2.0	580	0.7791	0.7283	0.7297	0.7283	0.7111
0.5455	3.0	870	0.7950	0.7285	0.7174	0.7285	0.7171
0.334	4.0	1160	0.8948	0.7155	0.7152	0.7155	0.7145
0.1644	5.0	1450	1.0820	0.7239	0.7189	0.7239	0.7194
0.0482	6.0	1740	1.2792	0.7204	0.7144	0.7204	0.7160
0.0236	7.0	2030	1.4162	0.7279	0.7195	0.7279	0.7209
0.0049	8.0	2320	1.4531	0.7249	0.7172	0.7249	0.7196

Property	Details
Model Type	Fine - tuned version of google/vit - base - patch16 - 224 on imagefolder dataset
Training Data	imagefolder dataset
Metrics	Accuracy, Precision, Recall, F1

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Google Vit Base Patch16 224 Face

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🚀 google-vit-base-patch16-224-face

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