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
This blog goes through the architecture of DETR
YOLO11 is here! Continuing the legacy of the YOLO series, YOLO11 sets new standards in speed and efficiency. With enhanced architecture and multi-task capabilities, it outperforms previous models, making it
This research article explains a data-centric fine-tuning approach using YOLOv10 models for kidney stone detection.
YOLOv10 introduces a dual-head architecture for NMS-free training and efficiency-accuracy driven model design. It combines one-to-one and one-to-many label assignments to improve performance without extra computation. YOLOv10 uses lightweight classification
This research article discusses about how data preparation matters for Fine-tuning Faster R-CNN on aerial small object detection.
This article will help you to quickly build and showcase your own deep learning models, using Gradio and OpenCV's DNN module.
