The project that leverages the power of YOLOv8 model to classify weather conditions from images. The application is built with Flask and takes an image as input to classify the weather in the image into five categories(Cloudy, Foggy, Rainy, Shine, and Sunrise).

1. clone the repo

git clone https://github.com/Abhi527370/Weather_Classification.git

2. install virtualenv package

install virtualenv package

3. create virtual environment for project to run

python -m venv <venv_name>

4. activate the virtual environment

<venv_name>\Scripts\activate

5. install flask and ultralytics package

pip install flask
pip install ultralytics

6. run the app

python app.py

Technologies used in the project:

• Python3
• HTML
• CSS
• Flask

1. Weather Forecasting: This model could be used to improve weather forecasting by analyzing images from weather satellites or ground-based cameras. It could help in identifying weather patterns and predicting future weather conditions.
2. Outdoor Event Planning: Event planners could use this model to check the weather conditions at their event location. This could help them for contingencies in case of adverse weather.
3. Smart Home Automation: Your weather classification model could be integrated into smart home systems. By analyzing images from outdoor cameras, the system could automatically adjust settings such as temperature, lighting, or irrigation based on the detected weather conditions.
4. Environmental Monitoring: Your model could be applied to monitor and analyze weather conditions in remote or inaccessible areas. By analyzing images captured by cameras or drones, it could provide valuable data for environmental research and monitoring.
5. Social Media Analysis: Your weather classification model could be used to analyze images shared on social media platforms. This could help in understanding the weather conditions in different locations based on user-generated content.

Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

• If you have suggestions for adding or removing projects, feel free to open an issue to discuss it, or directly create a pull request after you edit the README.md file with necessary changes.
• Please make sure you check your spelling and grammar.
• Create individual PR for each suggestion.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

• Abhishek Yadav - Mtech in SWE, DTU - Linkdin