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Yolov8s Visdrone
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Yolov8s Visdrone
Developed by ENOT-AutoDL
YOLOv8s model optimized using the ENOT-AutoDL framework, specifically designed for object detection tasks on the VisDrone dataset.
Downloads 57
Release Time : 11/7/2023
Model Overview
This model is based on the YOLOv8s architecture, optimized through the ENOT-AutoDL framework, and trained on the VisDrone dataset for object detection. It provides performance metrics under different computational complexities.
Model Features
Computational Efficiency Optimization
Achieved model optimization under different computational complexities (GMACs) through the ENOT-AutoDL framework, reducing computational requirements while maintaining high accuracy.
High-precision Detection
Achieved 49.4 mAP50 accuracy on the VisDrone dataset, surpassing the original YOLOv8 Ultralytics baseline of 40.2.
Multi-size Support
Supports large 928Ã928 image inputs, improving detection capability for small objects.
Model Capabilities
Drone Image Analysis
Multi-object Detection
Real-time Object Detection
Use Cases
Drone Surveillance
Traffic Monitoring
Detect and count vehicles, pedestrians, and other objects on roads
Can achieve 49.4 mAP50 accuracy
Agricultural Monitoring
Detect crop growth conditions or pests in farmland
Security Surveillance
Intrusion Detection
Detect unauthorized personnel or objects in monitored areas
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