Yolov10n
YOLOv10 is a real-time end-to-end object detection model proposed by Tsinghua University, known for its efficiency and accuracy.
Downloads 3,326
Release Time : 6/1/2024
Model Overview
YOLOv10 is a real-time end-to-end object detection model that focuses on achieving fast inference while maintaining high precision.
Model Features
Real-Time End-to-End Detection
The model supports real-time object detection without complex post-processing steps.
Efficient Inference
Achieves fast inference while maintaining high precision.
Easy to Use
Provides simple API interfaces for quick integration and usage.
Model Capabilities
Object Detection
Real-Time Inference
Image Analysis
Use Cases
Security Surveillance
Real-Time Monitoring
Used for real-time detection of target objects in surveillance videos.
Autonomous Driving
Road Object Detection
Detects vehicles, pedestrians, and other targets on the road.
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