weather-classification

Multiclass image classification using Convolutional Neural Network

The central point of ongoing research regarding the usefulness of the large-scale atmospheric variability in the Mediterranean for forecasting extreme precipitation in the domain.

A real-time traffic monitoring system integrating YOLOv5 for vehicle counting and ResNet50 for weather classification. Features dynamic flow threshold adjustments based on environmental conditions (Sunny/Rainy/Snowy) with a GUI visualization.

Second homework for the Machine Learning course at La Sapienza University of Rome

2026 睿抗机器人开发者大赛（RAICOM）CAIP 强脑赛道｜智能算法及应用·智海算法调优

Streamlit app using KNN to classify weather as Sunny or Rainy based on temperature and humidity. Features interactive sliders to adjust K-values, real-time Matplotlib visualizations, and confidence metrics. Built with Scikit-Learn for ML logic.

Benchmarking MobileNetV3 and MobileNetV4 on wildlife camera images for metereological image classification

Weather image classification project using YOLOv8 Classification with GPU training on Google Colab

Machine Learning & Parallel Computing projects, including NVIDIA DGX A100 supercomputer analysis and an ML model for extreme weather classification (Decision Tree & Random Forest).

TensorFlow implementation of CNN architectures for multi-class weather image classification

A CNN model built with PyTorch to classify weather conditions into 4 classes: sunrise, cloud, rain, and shine

Deep learning weather classifier using a TensorFlow/Keras neural network on Seattle weather data (2012-2015). Predicts rain, drizzle, fog, sun, or snow from precipitation, temperature, and wind with ~82% accuracy.

Predict weather precipitation (Rain vs. Snow) using Machine Learning classifiers: Random Forest, KNN, Naive Bayes, and Logistic Regression. Includes comprehensive evaluation using ROC-AUC and Learning Curves.

🌦️ A Flask-based ML web app for classifying weather types (Sunny, Rainy, Cloudy, Snowy) using multiple models. ⛅⚡❄️

Weather image classification using machine learning techniques.

A systematic study of training recipes, data augmentation, distribution shift, and ensemble strategies for weather image classification.

Inference-only demo of an adaptive object detection system for adverse weather conditions.

In this project, we aim to classify different types of weather using several machine learning algorithms. The dataset consists of various weather features like temperature, humidity, wind speed, precipitation, and more, which are used to predict the weather type.

🌦️ A Flask-based ML web app for classifying weather types (Sunny, Rainy, Cloudy, Snowy) using multiple models. ⛅⚡❄️