The open-source vit-base-letter image classification model - Free deployment, with an accurate letter recognition rate of up to 98.81%

Vit Base Letter

Developed by pittawat

An image classification model fine-tuned on a letter recognition dataset based on Google's ViT base model, achieving 98.81% accuracy

Image Classification

Transformers

EnglishOpen Source License:Apache-2.0 #Letter Recognition #High Accuracy #ViT Fine-tuning

Downloads 93

Release Time : 3/20/2023

Model Overview

This model is a letter recognition model fine-tuned from Google's ViT base architecture, specifically designed for alphabet classification tasks in images.

Model Features

High Accuracy

Achieves 98.81% accuracy on letter recognition tasks

Based on ViT Architecture

Utilizes Vision Transformer base architecture with powerful image feature extraction capabilities

Lightweight Fine-tuning

Only 4 epochs of fine-tuning on the pre-trained model, ensuring high training efficiency

Model Capabilities

Image Classification

Letter Recognition

Character Recognition

Use Cases

Document Processing

Handwritten Letter Recognition

Recognize handwritten letters in scanned documents or photos

98.81% accuracy

Educational Technology

Alphabet Learning Applications

Used in children's educational apps to recognize and validate written letters

🚀 vit-base-letter

This model is a fine - tuned version of [google/vit - base - patch16 - 224 - in21k](https://huggingface.co/google/vit - base - patch16 - 224 - in21k) for letter recognition, achieving high accuracy on the evaluation set.

🚀 Quick Start

This model is a fine - tuned version of [google/vit - base - patch16 - 224 - in21k](https://huggingface.co/google/vit - base - patch16 - 224 - in21k) on the pittawat/letter_recognition dataset. It achieves the following results on the evaluation set:

Loss: 0.0515
Accuracy: 0.9881

📚 Documentation

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

🔧 Technical Details

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 32
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e - 08
lr_scheduler_type: linear
num_epochs: 4
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.5539	0.12	100	0.5576	0.9308
0.2688	0.25	200	0.2371	0.9665
0.1568	0.37	300	0.1829	0.9688
0.1684	0.49	400	0.1611	0.9662
0.1584	0.62	500	0.1340	0.9673
0.1569	0.74	600	0.1933	0.9531
0.0992	0.86	700	0.1031	0.9781
0.0573	0.98	800	0.1024	0.9781
0.0359	1.11	900	0.0950	0.9804
0.0961	1.23	1000	0.1200	0.9723
0.0334	1.35	1100	0.0995	0.975
0.0855	1.48	1200	0.0791	0.9815
0.0902	1.6	1300	0.0981	0.9765
0.0583	1.72	1400	0.1192	0.9712
0.0683	1.85	1500	0.0692	0.9846
0.1188	1.97	1600	0.0931	0.9785
0.0366	2.09	1700	0.0919	0.9804
0.0276	2.21	1800	0.0667	0.9846
0.0309	2.34	1900	0.0599	0.9858
0.0183	2.46	2000	0.0892	0.9769
0.0431	2.58	2100	0.0663	0.985
0.0424	2.71	2200	0.0643	0.9862
0.0453	2.83	2300	0.0646	0.9862
0.0528	2.95	2400	0.0550	0.985
0.0045	3.08	2500	0.0579	0.9846
0.007	3.2	2600	0.0517	0.9885
0.0048	3.32	2700	0.0584	0.9865
0.019	3.44	2800	0.0560	0.9873
0.0038	3.57	2900	0.0515	0.9881
0.0219	3.69	3000	0.0527	0.9881
0.0117	3.81	3100	0.0523	0.9888
0.0035	3.94	3200	0.0559	0.9865

Framework versions

Transformers 4.26.1
Pytorch 1.13.0
Datasets 2.1.0
Tokenizers 0.13.2

📄 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