Overview:
The Object Segmentation project aims to detect cars using YOLOv8m, a powerful object detection model. The system processes images at 640×640 resolution and is trained to detect and segment vehicles, including cars and trucks, across 10 different classes. The goal is to provide an accurate real-time solution for vehicle detection in traffic or parking scenarios.
Key Features:
1. YOLOv8m for Vehicle Detection:
The project uses YOLOv8m to detect and segment cars and trucks with high accuracy across various scenes, providing efficient real-time object detection.
2. 10 Vehicle Classes:
The system is trained to recognize and distinguish between 10 different vehicle types, such as cars, trucks, and other common road vehicles.
3. Optimized Image Size:
The model processes images at a 640×640 resolution, balancing performance and computational efficiency for real-time applications.
4. Training Process:
The data is split into 70% for training, 20% for validation, and 10% for testing, ensuring robust model development and testing.
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