A tutorial on using Python, Ultralytics YOLO, and Supervision for computer vision tasks like object detection and image annotation.
Explores fast, one-stage object detection models like YOLO, SSD, and RetinaNet, comparing them to slower two-stage R-CNN models.
Explores the R-CNN family of models for object detection, covering R-CNN, Fast R-CNN, Faster R-CNN, and Mask R-CNN with technical details.
Explores classic CNN architectures for image classification, including AlexNet, VGG, and ResNet, as foundational models for object detection.
An introductory guide to the fundamental concepts of object detection, covering image gradients, HOG, and segmentation, as a precursor to deep learning methods.
