Vit-base-patch16-224-in21k-wwwwwi Open-source Visual Model - Free for Accurate Image Classification

Vit Base Patch16 224 In21k Wwwwwi

Developed by Imene

This model is a fine-tuned Vision Transformer based on google/vit-base-patch16-224-in21k on an unknown dataset, primarily used for image classification tasks.

Image Classification

Transformers

Open Source License:Apache-2.0 #ViT fine-tuned model #Medium accuracy #Multi-class classification task

Downloads 21

Release Time : 9/1/2022

Model Overview

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

Model Features

Vision Transformer architecture

Utilizes advanced Transformer architecture for image data processing with powerful feature extraction capabilities

Pre-trained model fine-tuning

Fine-tuned based on the google/vit-base-patch16-224-in21k pre-trained model

Mixed precision training

Uses mixed_float16 precision for training to balance computational efficiency and model accuracy

Model Capabilities

Image classification

Feature extraction

Transfer learning

Use Cases

Computer vision

General image classification

Can be used for classifying common objects and scenes

Achieved 25.4% accuracy on the validation set

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

This model is a fine - tuned version of google/vit-base-patch16-224-in21k on an unknown dataset. It provides evaluation results on the dataset, which can be used for reference in relevant tasks.

🚀 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: 3.2187
Train Accuracy: 0.5652
Train Top - 3 - accuracy: 0.7611
Validation Loss: 3.8221
Validation Accuracy: 0.2540
Validation Top - 3 - accuracy: 0.4409
Epoch: 9

📚 Documentation

Model Information

Property	Details
Model Name	Imene/vit-base-patch16-224-in21k-wwwwwi
Model Type	Fine - tuned version of google/vit-base-patch16-224-in21k
Training Data	Unknown

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': 3e - 05, 'decay_steps': 4920, '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': 0.01}}, '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
5.3476	0.0283	0.0716	5.1306	0.0483	0.1240	0
4.9357	0.0914	0.2057	4.7998	0.1158	0.2385	1
4.6155	0.1641	0.3230	4.5616	0.1430	0.2891	2
4.3325	0.2269	0.4188	4.3480	0.1722	0.3391	3
4.0702	0.2915	0.4984	4.1662	0.2042	0.3886	4
3.8262	0.3638	0.5758	4.0416	0.2296	0.4067	5
3.6117	0.4258	0.6415	3.9451	0.2329	0.4234	6
3.4324	0.4855	0.6956	3.8690	0.2499	0.4397	7
3.2991	0.5320	0.7376	3.8351	0.2553	0.4359	8
3.2187	0.5652	0.7611	3.8221	0.2540	0.4409	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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