Computer Vision - Detection and Segmentation with Yolo V9

Hammad Tahir

ML Engineer
AI Model Developer
AI Developer
OpenCV
Python
PyTorch
Unlock the power of computer vision with YOLOv9, the latest advancement in object detection! This revolutionary algorithm boasts improved accuracy and efficiency, making it perfect for real-time applications. Our project harnesses the power of YOLOv9 for detection and segmentation, leveraging its:
Programmable Gradient Information (PGI) for enhanced learning capacity
Generalized Efficient Layer Aggregation Network (GELAN) for optimized parameters and inference speed
Four models (v9-S, v9-M, v9-C, and v9-E) catering to different computational needs and accuracy requirements

Advantages:

Superior accuracy and efficiency
Reduced parameters and calculations
Real-time object detection capabilities
Flexibility for various applications (logistics, autonomous vehicles, people counting, sports analytics)
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