D

Dfine Small Coco

Developed by ustc-community
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.
Downloads 3,202
Release Time : 2/11/2025

Model Overview

The D-FINE model significantly improves the localization accuracy of object detection through two core components: Fine-grained Distribution Refinement (FDR) and Global Optimal Localization Self-Distillation (GO-LSD), making it suitable for various real-time detection scenarios.

Model Features

Fine-grained Distribution Refinement (FDR)
Redefines the bounding box regression task as a fine-grained distribution optimization problem, significantly improving localization accuracy.
Global Optimal Localization Self-Distillation (GO-LSD)
Achieves globally optimal localization through self-distillation techniques, enhancing model performance.
Real-time detection capability
Optimized design makes the model suitable for real-time object detection applications.

Model Capabilities

Object detection
Bounding box regression
Multi-category recognition

Use Cases

Autonomous driving
Road object detection
Real-time detection of vehicles, pedestrians, and other objects on the road
High-precision localization and recognition
Surveillance systems
Security monitoring
Real-time detection of suspicious objects or individuals in surveillance footage
Rapid response and high accuracy
Retail analytics
Shelf product detection
Automatically identifies product types and positions on shelves
Improves inventory management efficiency
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