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Yolov10b

Developed by onnx-community
YOLOv10 is a real-time end-to-end object detection model that offers a balance between efficient detection performance and accuracy.
Downloads 14
Release Time : 5/24/2024

Model Overview

YOLOv10 is an efficient object detection model that supports real-time end-to-end detection, suitable for object recognition tasks in various scenarios.

Model Features

Real-Time End-to-End Detection
Supports efficient real-time object detection, ideal for applications requiring quick responses.
Balance Between Accuracy and Latency
Optimizes latency while maintaining high accuracy, suitable for various hardware environments.
Trade-off Between Size and Accuracy
Offers model variants of different sizes, allowing users to choose the right balance between accuracy and performance based on their needs.

Model Capabilities

Object Detection
Real-Time Detection
Multi-Class Recognition

Use Cases

Smart Surveillance
Traffic Monitoring
Detects vehicles and pedestrians on roads for traffic flow analysis and violation monitoring.
High-precision detection of vehicles and pedestrians, supporting real-time analysis.
Autonomous Driving
Obstacle Detection
Real-time detection of obstacles on roads to provide environmental awareness for autonomous driving systems.
Efficiently identifies various obstacles, enhancing the safety of autonomous driving.
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