D

Dfine Medium Obj2coco

Developed by ustc-community
D-FINE is a real-time object detection model that achieves exceptional localization accuracy by redefining the bounding box regression task.
Downloads 3,610
Release Time : 3/28/2025

Model Overview

D-FINE is a DETR-based object detection model that improves localization accuracy through fine-grained distribution optimization and global optimal localization self-distillation techniques. Suitable for real-time object detection scenarios such as autonomous driving and security surveillance.

Model Features

Fine-grained Distribution Refinement (FDR)
Redefines the bounding box regression task to enhance localization accuracy.
Global Optimal Localization Self-Distillation (GO-LSD)
Optimizes model performance through self-distillation techniques.
Real-time detection
Suitable for real-time object detection scenarios requiring high-speed response.

Model Capabilities

Object detection
Real-time image analysis
Multi-object recognition

Use Cases

Autonomous driving
Vehicle and pedestrian detection
Detects vehicles and pedestrians on the road in real-time to enhance autonomous driving safety.
Security surveillance
Abnormal behavior detection
Detects abnormal behavior or suspicious items in surveillance videos.
Retail analytics
Product recognition
Identifies products on shelves for inventory management and customer behavior analysis.
Featured Recommended AI Models
AIbase
Empowering the Future, Your AI Solution Knowledge Base
© 2025AIbase