Object Detection
Object detection has traditionally been a closed-set problem: you train on a fixed list of classes and cannot recognize new ones. Grounding DINO breaks this mold, becoming an open-set, language-conditioned
Object detection has come a long way, especially with the rise of transformer-based models. RF-DETR, developed by Roboflow, is one such model that offers both speed and accuracy. Using Roboflow’s
Imagine you have multiple warehouses in different places where you don’t have time to monitor everything at a time, and you can’t afford a lot of computes due to their
Object detection has undergone tremendous advancements, with models like YOLOv12, YOLOv11, and Darknet-Based YOLOv7 leading the way in real-time detection. While these models perform exceptionally well on general object detection
A comprehensive step-by-step guide on fine-tuning RetinaNet using PyTorch to achieve 79% accuracy on wildlife detection tasks. In this tutorial, we dive deep into RetinaNet’s architecture, explain the benefits of
Real-time object detection has become essential for many practical applications, and the YOLO (You Only Look Once) series by Ultralytics has always been a state-of-the-art model series, providing a robust
