Weather-Image-Classifier is an advanced image classification project designed to distinguish between eight weather conditions: 'dew', 'frost', 'glaze', 'lightning', 'rain', 'rainbow', 'sandstorm', and 'snow'. This model stands out for its ability to accurately identify various weather phenomena, addressing class imbalance using SMOTE. Additionally, it offers high accuracy and robustness thanks to techniques like data augmentation and transfer learning using DenseNet201. This project is essential for weather monitoring and prediction in various environments, such as agriculture, transportation, and disaster management.
Unique Classification: Detects a wide range of weather conditions, including rare phenomena like 'dew' and 'glaze'. High Accuracy: Leveraging DenseNet201 for transfer learning to enhance model performance. Real-time Prediction: Provides real-time predictions for uploaded images. SMOTE for Class Imbalance: Implements Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance, ensuring the model is well-trained across all classes. Data Augmentation: Uses data augmentation techniques to enhance the training dataset and improve model robustness. End-to-End Solution: Comprehensive workflow from image collection to full deployment as a web application using this url https://weatherimage.streamlit.app/
Weather image detection models are crucial in various environments to ensure safety and preparedness:
Agriculture: Helps farmers monitor weather conditions to protect crops and plan activities. Transportation: Ensures the safety of road, air, and sea transport by predicting adverse weather conditions. Disaster Management: Aids in early detection and response to extreme weather events, reducing risks and damages. Urban Planning: Assists in designing infrastructure resilient to various weather conditions. Recreational Activities: Informs the public about weather conditions for outdoor activities and events.
The deployment process includes end-to-end implementation from image collection to full deployment, ensuring a seamless user experience. This webapp can be accessed at this url https://weatherimage.streamlit.app/
TensorFlow Keras OpenCV Special thanks to all contributors and the open-source community.