Mpowerr HECMS YOLOV8N

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Mpowerr HECMS YOLOV8N — Fast, Accurate, and Optimized for Real-Time Processing

This lightweight, high-performance model is optimized for real-time object detection, delivering fast, accurate, and efficient results even on edge devices with limited computational resources.

Performance Metrics — Accuracy, Insights, Outputs

This section highlights the core metrics used to evaluate the model’s performance, including precision, recall, and mAP scores. It also features evaluation charts and inference outputs that demonstrate how the model performs across various conditions.

94.87%

Precision

01

93.73%

Recall

02

96.97%

mAP@0.5

03

76.06%

mAP@0.5:0.95

04
F1 Confidence Curve
Precision Confidence Curve
Recall Confidence Curve
Precision Recall Curve
Confusion Matrix
Normalized Confusion Matrix
Training & Validation Metrics
Label Distribution Heatmap
Label Correlogram
Model Output 1
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Changelog

V1

Mpowerr HECMS – YOLOV8N V1 Release

  • Initial release of YOLOV8N model trained for elephant detection.
  • Model achieves 94.87% precision, 93.73% recall, and 96.97% mAP@0.5.
  • Includes confusion matrix, PR/F1/Recall confidence curves, and bounding box distribution visualizations.
  • Open-source model weights and evaluation assets available for download.
  • Optimized for use with HECMS real-time detection and alert system.