vit-base-patch16-384-wi5 Open-source Image Classification Model - Free Deployment and Accurate Identification of Image Categories

Vit Base Patch16 384 Wi5

Developed by Imene

This model is a fine-tuned Vision Transformer based on google/vit-base-patch16-384, primarily used for image classification tasks.

Image Classification

Transformers

Open Source License:Apache-2.0 #High-precision Image Classification #Vision Transformer #Few-shot Learning

Downloads 21

Release Time : 9/6/2022

Model Overview

This is an image classification model based on the Vision Transformer architecture, using the patch16-384 configuration and fine-tuned on a specific dataset.

Model Features

High-resolution Processing

Supports input resolution of 384x384 pixels

Efficient Fine-tuning

Targeted fine-tuning on the base model to adapt to specific tasks

Mixed Precision Training

Uses mixed_float16 precision for training, balancing accuracy and efficiency

Model Capabilities

Image Classification

Visual Feature Extraction

Use Cases

Computer Vision

General Image Classification

Classifies and identifies input images

Validation accuracy 49.12%, Top-3 accuracy 73.02%

🚀 Imene/vit-base-patch16-384-wi5

This model is a fine - tuned version of google/vit-base-patch16-384, aiming to achieve better performance on specific tasks.

🚀 Quick Start

This model is a fine - tuned version of google/vit-base-patch16-384 on an unknown dataset. It achieves the following results on the evaluation set:

Train Loss: 0.4102
Train Accuracy: 0.9755
Train Top - 3 - accuracy: 0.9960
Validation Loss: 1.9021
Validation Accuracy: 0.4912
Validation Top - 3 - accuracy: 0.7302
Epoch: 8

📚 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': 3e - 05, 'decay_steps': 3180, '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
4.2945	0.0568	0.1328	3.6233	0.1387	0.2916	0
3.1234	0.2437	0.4585	2.8657	0.3041	0.5330	1
2.4383	0.4182	0.6638	2.5499	0.3534	0.6048	2
1.9258	0.5698	0.7913	2.3046	0.4202	0.6583	3
1.4919	0.6963	0.8758	2.1349	0.4553	0.6784	4
1.1127	0.7992	0.9395	2.0878	0.4595	0.6809	5
0.8092	0.8889	0.9720	1.9460	0.4962	0.7210	6
0.5794	0.9419	0.9883	1.9478	0.4979	0.7201	7
0.4102	0.9755	0.9960	1.9021	0.4912	0.7302	8

Framework versions

Transformers 4.21.3
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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