This advanced Computer Vision course by Roboflow focuses on modern object detection, tracking, and segmentation techniques used in real-world AI systems. It is designed for developers and engineers who want to work with state-of-the-art vision models.
The course begins with guidance on how to choose the best computer vision model for a given project, helping learners understand trade-offs between accuracy, speed, and complexity. It then introduces YOLOv8, one of the most popular object detection models, and explains how to train it on custom datasets.
Learners explore object tracking using ByteTrack and Supervision, enabling real-time tracking and counting of objects in video streams. The course also covers advanced applications such as people counting, football player tracking, and live webcam detection systems.
In addition to YOLO-based models, the course introduces transformer-based object detection using DETR, segmentation techniques using YOLOv7, and state-of-the-art models like YOLO-NAS.
Modern AI tools such as Grounding DINO and Segment Anything Model (SAM) are also covered, showing how zero-shot detection and automatic segmentation work in practice.
The course concludes with practical pipelines for annotation acceleration, real-time object counting, and full computer vision system deployment.
By the end, learners gain strong hands-on experience with cutting-edge computer vision technologies used in industry today.
