Vit-base-patch16-224-wi2 Open-source Vision Model - Efficiently Complete Image Classification Tasks with Free Deployment

Home

Vit Base Patch16 224 Wi2

Developed by Imene

Vision Transformer model fine-tuned from google/vit-base-patch16-224, suitable for image classification tasks

Image Classification

Transformers

Open Source License:Apache-2.0 #ViT fine-tuned model #Image classification #Mixed precision training

Downloads 21

Release Time : 9/10/2022

Model Overview

This model is an image classification model based on the Vision Transformer (ViT) architecture, with improved performance on specific datasets through fine-tuning.

Model Features

Based on ViT architecture

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

Mixed precision training

Trained with mixed_float16 precision to optimize computational efficiency

AdamW optimizer

Employs AdamWeightDecay optimizer with polynomial learning rate decay strategy

Model Capabilities

Image classification

Feature extraction

Use Cases

Computer vision

General image classification

Classifies input images

Achieves 24.91% accuracy on the validation set

🚀 Imene/vit-base-patch16-224-wi2

This model is a fine - tuned version of google/vit-base-patch16-224, aiming to provide better performance on specific tasks.

🚀 Quick Start

This model is a fine - tuned version of google/vit-base-patch16-224 on an unknown dataset. It achieves the following results on the evaluation set:

Train Loss: 0.3098
Train Accuracy: 0.9821
Train Top - 5 - accuracy: 0.9971
Validation Loss: 3.0737
Validation Accuracy: 0.2491
Validation Top - 5 - accuracy: 0.4476
Epoch: 9

📚 Documentation

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0003, 'decay_steps': 1750, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e - 08, 'amsgrad': False, 'weight_decay_rate': 0.001}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
training_precision: mixed_float16

Training results

Train Loss	Train Accuracy	Train Top - 5 - accuracy	Validation Loss	Validation Accuracy	Validation Top - 5 - accuracy	Epoch
4.4859	0.0195	0.0579	4.2995	0.0368	0.0865	0
4.1729	0.0355	0.0987	4.0916	0.0472	0.1266	1
3.9541	0.0666	0.1641	3.8050	0.0781	0.2035	2
3.5823	0.1247	0.2615	3.4015	0.1429	0.2950	3
3.0156	0.1913	0.3987	3.0598	0.1880	0.3916	4
2.4618	0.3077	0.5572	2.9869	0.2056	0.4129	5
1.8979	0.4541	0.7165	2.9507	0.2298	0.4425	6
1.2075	0.6914	0.8886	3.0106	0.2394	0.4425	7
0.6026	0.9097	0.9810	3.0739	0.2428	0.4413	8
0.3098	0.9821	0.9971	3.0737	0.2491	0.4476	9

Framework versions

Transformers 4.21.3
TensorFlow 2.8.2
Datasets 2.4.0
Tokenizers 0.12.1

📄 License

This project is licensed under the Apache - 2.0 license.

Featured Recommended AI Models

Empowering the Future, Your AI Solution Knowledge Base

English 简体中文繁體中文にほんご