vit-base-patch16-224-in21k-wwwwii Open-source Visual Classification Model - Tackle All Kinds of Image Classification Tasks

Vit Base Patch16 224 In21k Wwwwii

Developed by Imene

A vision classification model fine-tuned based on Google's Vision Transformer (ViT) foundation model, suitable for image classification tasks

Image Classification

Transformers

Open Source License:Apache-2.0 #Vision Transformer #High-precision classification #Transfer learning

Downloads 21

Release Time : 9/2/2022

Model Overview

This model is a fine-tuned version of the google/vit-base-patch16-224-in21k pre-trained model on an unknown dataset, primarily used for image classification tasks.

Model Features

Based on ViT architecture

Utilizes Vision Transformer architecture, processing inputs with 16x16 image patches

Transfer learning

Fine-tuned from ImageNet-21k pre-trained model with strong feature extraction capabilities

Efficient classification

Achieves 62.67% accuracy and 83.49% Top-3 accuracy on the validation set

Model Capabilities

Image classification

Visual feature extraction

Use Cases

Computer vision

General image classification

Classify and recognize input images

Validation accuracy 62.67%

🚀 Imene/vit-base-patch16-224-in21k-wwwwii

This model is a fine - tuned version of google/vit-base-patch16-224-in21k on an unknown dataset, achieving specific results on the evaluation set.

🚀 Quick Start

This model is a fine - tuned version of google/vit-base-patch16-224-in21k on an unknown dataset. It achieves the following results on the evaluation set:

Train Loss: 0.8024
Train Accuracy: 0.9939
Train Top - 3 - accuracy: 0.9997
Validation Loss: 1.6739
Validation Accuracy: 0.6267
Validation Top - 3 - accuracy: 0.8349
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': 4e - 05, 'decay_steps': 1620, '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 - 3 - accuracy	Validation Loss	Validation Accuracy	Validation Top - 3 - accuracy	Epoch
3.6793	0.125	0.2805	3.4078	0.2151	0.4756	0
3.1763	0.3448	0.6265	3.0167	0.4209	0.6640	1
2.7546	0.5419	0.7852	2.6634	0.5326	0.7651	2
2.3537	0.6855	0.8843	2.3971	0.5547	0.7860	3
1.9989	0.7814	0.9279	2.2236	0.5837	0.7907	4
1.6670	0.875	0.9698	2.0757	0.5977	0.7907	5
1.3815	0.9352	0.9890	1.8921	0.6198	0.8174	6
1.1407	0.9651	0.9956	1.7976	0.6244	0.8174	7
0.9451	0.9866	0.9983	1.7227	0.6349	0.8267	8
0.8024	0.9939	0.9997	1.6739	0.6267	0.8349	9

Framework versions

Transformers 4.21.2
TensorFlow 2.8.2
Datasets 2.4.0
Tokenizers 0.12.1

📄 License

This model is licensed under the Apache 2.0 license.

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