# Real-time object detection

D-FINE is a real-time object detection model that achieves exceptional localization accuracy by redefining the bounding box regression task in the DETR model.

D-FINE is a powerful real-time object detector that achieves exceptional localization accuracy by redefining the bounding box regression task in DETR models.

D-FINE is a powerful real-time object detection model that achieves excellent positioning accuracy by redefining the bounding box regression task in the DETR model.

D-FINE is a powerful real-time object detection model that achieves excellent positioning accuracy by redefining the bounding box regression task in the DETR model.

D-FINE is a real-time object detection model that achieves exceptional localization accuracy by redefining the bounding box regression task.

D-FINE is a real-time object detection model that achieves exceptional localization accuracy by redefining the bounding box regression task in the DETR model.

YOLOE is an efficient, unified, and open model for object detection and segmentation, supporting various prompting mechanisms including text, visual inputs, and prompt-free paradigms, achieving real-time universal visual perception.

D-FINE is a real-time object detection model based on an improved DETR architecture, achieving exceptional localization accuracy by redefining the bounding box regression task.

RT-DETRv2 is an improved real-time object detection model based on the DETR architecture, optimizing detection performance through innovations like selective multi-scale feature extraction and dynamic data augmentation.

RT-DETRv2 is an improved real-time object detection Transformer model that enhances performance through strategies such as selective multi-scale feature extraction and dynamic data augmentation.

RT-DETRv2 is an improved version of the real-time object detection Transformer model, enhancing performance through multi-scale feature extraction and optimized training strategies.

RT-DETRv2 is an optimized real-time object detection model based on the RT-DETR architecture. It improves detection accuracy while maintaining real-time performance through selective multi-scale feature extraction and enhanced training strategies.

YOLOv8 is an efficient object detection model developed by Ultralytics, based on the YOLO (You Only Look Once) architecture, suitable for real-time object detection tasks.

YOLOv5 is the world's most popular visual AI model, developed by Ultralytics, incorporating thousands of hours of R&D experience to provide efficient object detection capabilities.

RT-DETRv2 is an improved real-time detection transformer that enhances flexibility and practicality through plug-and-play optimization schemes and achieves stronger performance with optimized training strategies.

The first real-time end-to-end object detector based on Transformer architecture, eliminating the need for non-maximum suppression, surpassing YOLO series in speed and accuracy

RT-DETR is the first real-time end-to-end object detector, achieving efficient detection through hybrid encoders and query selection mechanisms without NMS post-processing.

YOLOv10 is a real-time end-to-end object detection model proposed by Tsinghua University, with significant improvements in both speed and accuracy.

YOLOv10 is a real-time end-to-end object detection model with efficient inference speed and high detection accuracy.

YOLOv10 is a real-time end-to-end object detection model with efficient latency-accuracy and size-accuracy trade-offs.

YOLOv10 is a real-time end-to-end object detection model developed by Tsinghua University's MIG Lab, offering efficient detection performance and lightweight deployment capabilities.

YOLOv10 is an efficient real-time object detection model developed by Tsinghua University's MIG Lab, offering end-to-end detection capabilities.

YOLOv10 is an efficient real-time object detection model without extra training costs. By optimizing architecture and training strategies, it improves detection accuracy while maintaining real-time performance.

The first real-time end-to-end object detector, achieving efficient NMS-free detection through hybrid encoder and query selection mechanism

Object detection model based on YOLOv9 (GELAN-C) architecture, optimized ONNX version for Transformers.js

RT-DETR is the first real-time end-to-end object detection Transformer model, achieving high-speed and high-precision detection through an efficient hybrid encoder and query selection mechanism

YOLOv9-C is an object detection model based on the YOLOv9 architecture, suitable for real-time detection of multiple objects in images.

YOLOv8 is the latest generation object detection model developed by Ultralytics, building on the success of previous YOLO versions with new features and improvements to further enhance performance and flexibility.

A YOLOv8-based object detection model specifically designed to detect various items such as bombs, boxes, cans, filters, and bags.

Aircraft object detection model based on YOLOv8n, optimized specifically for aircraft detection tasks.

YOLOv5s is an efficient lightweight version of the YOLO series object detection model, implemented based on PyTorch, suitable for real-time object detection tasks.

YOLOv5n-v7.0 is a lightweight object detection model based on the YOLOv5 architecture, suitable for real-time object detection tasks.