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Yolov10s

Developed by onnx-community
YOLOv10 is an efficient real-time object detection model developed by Tsinghua University's MIG Lab, offering end-to-end detection capabilities.
Downloads 13
Release Time : 5/24/2024

Model Overview

YOLOv10 is an efficient object detection model focused on real-time performance and end-to-end detection capabilities, suitable for various computer vision applications.

Model Features

Real-time Performance
The model optimizes the balance between latency and accuracy, making it suitable for real-time applications.
End-to-end Detection
Provides a complete end-to-end object detection solution.
Size Optimization
The model achieves a good balance between size and accuracy.

Model Capabilities

Object detection
Real-time image analysis
Multi-category object recognition

Use Cases

Smart Surveillance
Traffic Monitoring
Real-time detection of vehicles and pedestrians on roads
Accurately identifies targets such as vehicles and pedestrians
Autonomous Driving
Environmental Perception
Detects obstacles and other vehicles on the road
Provides real-time environmental perception capabilities
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