vit-base-patch16-224-in21k Open-source Weather Image Classification Model - Accurately Identify Various Weather Conditions

Vit Base Patch16 224 In21k Weather Images Classification

Developed by DunnBC22

A weather image classification model based on Vision Transformer architecture, fine-tuned on the Kaggle weather dataset with an accuracy of 93.4%

Image Classification

Transformers

EnglishOpen Source License:Apache-2.0 #High-precision Weather Classification #ViT Vision Transformer #Multi-metric Optimization

Downloads 236

Release Time : 2/11/2023

Model Overview

This model is fine-tuned from Google's pre-trained ViT model, specifically designed for weather image classification tasks, capable of identifying various types of weather conditions.

Model Features

High Accuracy

Achieves 93.4% accuracy on the test set with balanced evaluation metrics

Transfer Learning

Fine-tuned from a large-scale pre-trained ViT model, effectively leveraging pre-trained knowledge

Multi-metric Evaluation

Provides comprehensive evaluation metrics including accuracy, F1 score, recall, and precision

Model Capabilities

Weather Image Classification

Image Feature Extraction

Multi-class Recognition

Use Cases

Meteorological Monitoring

Automatic Weather Recognition

Automatically identifies weather conditions from surveillance cameras or user-uploaded images

93.4% accuracy

Agricultural Applications

Farm Weather Monitoring

Analyzes field weather conditions through images captured by drones

🚀 vit-base-patch16-224-in21k-weather-images-classification

This model is a fine - tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It's designed for image classification, specifically for classifying weather images, and achieves high accuracy in relevant tasks.

🚀 Quick Start

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

Loss: 0.2255
Accuracy: 0.9340
Weighted f1: 0.9341
Micro f1: 0.9340
Macro f1: 0.9372
Weighted recall: 0.9340
Micro recall: 0.9340
Macro recall: 0.9354
Weighted precision: 0.9347
Micro precision: 0.9340
Macro precision: 0.9398

✨ Features

This is a classification model of weather images.

For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Computer%20Vision/Image%20Classification/Multiclass%20Classification/Weather%20Images/Weather_Images_ViT.ipynb

📚 Documentation

Intended uses & limitations

This model is intended to demonstrate my ability to solve a complex problem using technology.

Training and evaluation data

Dataset Source: https://www.kaggle.com/datasets/jehanbhathena/weather-dataset

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 16
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e - 08
lr_scheduler_type: linear
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Weighted f1	Micro f1	Macro f1	Weighted recall	Micro recall	Macro recall	Weighted precision	Micro precision	Macro precision
2.4333	1.0	337	0.3374	0.9036	0.9028	0.9036	0.9080	0.9036	0.9036	0.9002	0.9088	0.9036	0.9234
0.4422	2.0	674	0.2504	0.9228	0.9226	0.9228	0.9285	0.9228	0.9228	0.9273	0.9248	0.9228	0.9318
0.1051	3.0	1011	0.2255	0.9340	0.9341	0.9340	0.9372	0.9340	0.9340	0.9354	0.9347	0.9340	0.9398

Framework versions

Transformers 4.25.1
Pytorch 1.12.1
Datasets 2.8.0
Tokenizers 0.12.1

📄 License

This model is licensed under the Apache - 2.0 license.

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