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Yolov10n

Developed by onnx-community
YOLOv10 is a real-time end-to-end object detection model with efficient latency-accuracy and size-accuracy trade-offs.
Downloads 21
Release Time : 5/24/2024

Model Overview

YOLOv10 is an efficient object detection model suitable for real-time applications, supporting end-to-end detection.

Model Features

Real-time end-to-end detection
Supports efficient real-time object detection for applications requiring rapid response.
Optimized latency-accuracy trade-off
Optimizes model latency performance while maintaining high accuracy.
Optimized size-accuracy trade-off
Optimizes model size while maintaining high accuracy, suitable for resource-constrained environments.

Model Capabilities

Object detection
Real-time processing
End-to-end detection

Use Cases

Intelligent surveillance
Traffic monitoring
Used to detect vehicles and pedestrians on roads for optimized traffic management.
High-precision vehicle and pedestrian detection suitable for real-time surveillance systems.
Autonomous driving
Obstacle detection
Used for obstacle detection in autonomous driving systems to ensure road safety.
Real-time detection of road obstacles enhances the safety of autonomous driving systems.
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