🌦️ Weather Image Recognition 🧠📷

Welcome to Weather Image Recognition! This project utilizes machine learning and advanced neural network architectures to classify weather conditions from images. 🌤️🌧️❄️

🚀 Features

• 📂 Data Handling: Load, preprocess, and augment weather image datasets.
• 🖼️ Image Classification: Classify weather types (e.g., sunny, rainy, snowy) with high accuracy.
• 🧠 Architectures Used:
  • Custom CNN: A lightweight convolutional neural network designed specifically for this task.
  • Torchvision Models: Integrates pre-trained models for transfer learning (e.g., ResNet, VGG).
• 📈 Evaluation Metrics: Analyze model performance with accuracy, confusion matrices, and visualizations.
• 🔧 Customizable Pipelines: Easily adapt the notebook for different datasets or tasks.

📋 Prerequisites

• 🐍 Python 3.7+
• 📦 Required libraries (install with pip):
  • torch
  • torchvision
  • numpy
  • pandas
  • matplotlib

🛠️ How to Use

1. Clone the repository:

   git clone https://github.com/your-username/weather-image-recognition.git

2. Navigate to the project folder:

   cd weather-image-recognition

3. Install dependencies:

   pip install -r requirements.txt

4. Open the notebook:

   jupyter notebook Weather_Image_Recognition.ipynb

5. Run the cells: Follow the step-by-step guidance in the notebook.

📈 Sample Workflow

1. Load and preprocess labeled weather images. 🌅🌩️🌨️
2. Train a custom CNN or fine-tune a pre-trained model (like ResNet).
3. Evaluate model performance using test datasets.
4. Visualize predictions and analyze errors.

📚 Architectures Used

1. Custom CNN

• Designed for lightweight yet effective classification.
• Includes convolutional layers, max-pooling, and fully connected layers optimized for weather data.

2. Pre-trained Models

• Uses torchvision.models for transfer learning.
• Examples: ResNet, VGG, or other deep CNNs, fine-tuned on weather datasets.

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