Vit-base-patch16-224 Open Source Image Classification Model - Free Deployment for Accurate Recognition of Various Images

Vit Base Patch16 224 Album Vitvmmrdb Make Model Album Pred

Developed by venetis

A visual classification model fine-tuned on an unknown dataset based on Google's ViT model, excelling in image classification tasks

Image Classification

Transformers

Open Source License:Apache-2.0 #High-precision image classification #ViT Vision Transformer #Multi-metric optimization

Downloads 33

Release Time : 11/25/2022

Model Overview

This model is a fine-tuned version based on Google's ViT-base-patch16-224 architecture, primarily used for image classification tasks. It demonstrates high accuracy (87.81%) and F1 score (87.58%) in evaluations.

Model Features

High-precision classification

Achieves 87.81% accuracy and 87.58% F1 score on the evaluation set, demonstrating excellent performance

Based on ViT architecture

Utilizes Vision Transformer architecture with powerful image feature extraction capabilities

Efficient fine-tuning

Achieves good performance with just 15 training epochs, optimized with learning rate scheduling strategies

Model Capabilities

Image classification

Visual feature extraction

Multi-category recognition

Use Cases

Image recognition

Vehicle model recognition

Can be used to identify different brands and models of vehicles

87.81% accuracy

Product categorization

Suitable for automatic product classification on e-commerce platforms

🚀 vit-base-patch16-224_album_vitVMMRdb_make_model_album_pred

This model is a fine - tuned version of google/vit-base-patch16-224 on the None dataset. It offers high - performance image recognition capabilities, achieving excellent results on the evaluation set.

🚀 Quick Start

This fine - tuned model can be easily integrated into your projects. You can start using it by referring to the original model's usage on Hugging Face.

📚 Documentation

Model Information

Property	Details
Model Type	vit-base-patch16-224_album_vitVMMRdb_make_model_album_pred
Training Data	None
Evaluation Metrics	Loss: 0.4670, Accuracy: 0.8781, Precision: 0.8768, Recall: 0.8781, F1: 0.8758

Training Procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e - 05
train_batch_size: 64
eval_batch_size: 64
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 256
optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
3.5529	1.0	839	3.3687	0.3096	0.2809	0.3096	0.2246
1.7855	2.0	1678	1.6042	0.6378	0.6187	0.6378	0.5996
1.1054	3.0	2517	1.0105	0.7556	0.7512	0.7556	0.7385
0.8179	4.0	3356	0.7794	0.8033	0.8020	0.8033	0.7934
0.6057	5.0	4195	0.6479	0.8294	0.8274	0.8294	0.8212
0.4709	6.0	5034	0.5817	0.8478	0.8477	0.8478	0.8428
0.3962	7.0	5873	0.5333	0.8571	0.8570	0.8571	0.8527
0.346	8.0	6712	0.5073	0.8638	0.8647	0.8638	0.8615
0.2772	9.0	7551	0.4881	0.8681	0.8679	0.8681	0.8656
0.2136	10.0	8390	0.4777	0.8719	0.8718	0.8719	0.8689
0.1937	11.0	9229	0.4737	0.8734	0.8731	0.8734	0.8703
0.1754	12.0	10068	0.4604	0.8758	0.8750	0.8758	0.8733
0.1111	13.0	10907	0.4561	0.8790	0.8782	0.8790	0.8768
0.1128	14.0	11746	0.4519	0.8808	0.8799	0.8808	0.8787
0.1018	15.0	12585	0.4497	0.8813	0.8805	0.8813	0.8794

Framework versions

Transformers 4.24.0
Pytorch 1.12.1+cu113
Datasets 2.7.1
Tokenizers 0.13.2

📄 License

This model is licensed under the Apache - 2.0 license.

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