WeatherNet-05-18039

Overview

WeatherNet-05 is a weather image classification dataset consisting of 18,039 images labeled into 5 distinct weather-related classes. The dataset is suitable for training and evaluating computer vision models on the task of classifying weather conditions based on image data.

Dataset Structure

• Split: train
• Number of rows: 18,039
• Label Type: Categorical (5 classes)
• Image Resolution: Varies (from 90px to 4.86k px width)
• File Format: Auto-converted to Parquet for efficient processing

Label Classes

The dataset contains the following classes (not fully visible in the image but inferred from partial data):

• cloudy or overcast
• [4 other class names not displayed in the screenshot]

Usage

You can use the dataset directly with Hugging Face's datasets library:

from datasets import load_dataset

dataset = load_dataset("prithivMLmods/WeatherNet-05-18039")

Applications

This dataset is ideal for:

• Weather image classification
• Transfer learning with visual transformers
• Fine-tuning pre-trained computer vision models

Related Models

This dataset has been used to train or fine-tune models such as:

• prithivMLmods/Weather-Image-Classification (Image Classification)

Collections

This dataset is part of the collection:

• Content Filters SigLIP2/ViT (Moderation, Balance, Contextual Understanding)