Mpowerr HECMS YOLOV8L

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Mpowerr HECMS YOLOV8L — Strong Detection for Demanding Environments

This model is built to handle complex detection tasks with a focus on reliability. It delivers strong results across various conditions, making it well-suited for situations where stable and accurate detection is essential.

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.

93.22%

Precision

01

91.25%

Recall

02

95.75%

mAP@0.5

03

73.18%

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
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Changelog

V1

Mpowerr HECMS – YOLOV8L V1 Release

  • First version of the YOLOV8L model trained specifically for elephant detection under varied environmental conditions.
  • Achieves 93.22% precision, 91.25% recall, and 95.75% mAP@0.5.
  • Model evaluation includes detailed visual reports like PR curves, confidence trends, and detection overlays.
  • Public release includes weights, performance logs, and technical notes for review and integration.
  • Designed to offer steady, reliable detection where consistent results are essential for response systems.