https://github.com/anidipta/vdvwc

Vehicle Detection in Various Weather Conditions
https://github.com/anidipta/vdvwc

computer-vision deep-learning python3 yolov8

Last synced: 22 days ago
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Vehicle Detection in Various Weather Conditions

• Host: GitHub
• URL: https://github.com/anidipta/vdvwc
• Owner: Anidipta
• License: apache-2.0
• Created: 2024-07-24T16:20:04.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2024-08-19T16:24:44.000Z (5 months ago)
• Last Synced: 2024-08-19T19:24:05.469Z (5 months ago)
• Topics: computer-vision, deep-learning, python3, yolov8
• Language: Jupyter Notebook
• Homepage:
• Size: 5.26 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
#  Vehicle Detection 🚗 in Various Weather Conditions 🌦️

This advanced model is designed to detect and classify vehicles across a spectrum of weather conditions—sunny, rainy, foggy, and overcast. Leveraging state-of-the-art deep learning methodologies, it ensures reliable performance even in challenging atmospheric scenarios.

### **Key Features:**

- **🔍 Advanced Deep Learning Techniques**: Employs Convolutional Neural Networks (CNNs) to capture and learn intricate patterns and features from images, providing high accuracy in vehicle detection and classification.

- **📊 Training Data**: Trained on a meticulously curated dataset featuring diverse vehicle types and various weather conditions. This extensive dataset enhances the model’s ability to generalize and perform effectively across different scenarios.

- **🔄 Epochs**: The model is trained over 25 epochs, balancing accuracy and generalization. This training regimen allows the model to learn from the data comprehensively and improve performance through multiple iterations.

- **🌐 Deployment**: Designed for integration into real-time vehicle detection systems. The model's predictions can be utilized to enhance safety and monitoring in dynamic environments, making it ideal for applications in autonomous driving, traffic management, and vehicular surveillance.

### **Access the Model**:

- **📂 [Click Here to View the Model](https://drive.google.com/file/d/1ORDPEvBxcDiFXlft47L5bK3vBPSRWVq4/view?usp=sharing)**

This model's robust design and extensive training ensure reliable vehicle detection and classification, contributing to enhanced safety and efficiency across various weather conditions.