Mpowerr HECMS YOLOV11N

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Mpowerr HECMS YOLOV11N — Lightweight and Exceptionally Accurate

This compact model delivers outstanding detection results with minimal resource usage. It's ideal for real-time applications on edge devices, offering a rare balance of speed, size, and reliable accuracy in practical deployments.

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.

96.99%

Precision

01

95.60%

Recall

02

98.52%

mAP@0.5

03

82.07%

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 – YOLOV11N V1 Release

  • First release of the YOLOV11N model designed for lightweight yet accurate elephant detection systems.
  • Achieves 96.99% precision, 95.60% recall, and 98.52% mAP@0.5.
  • Comes with full evaluation resources including confidence plots, prediction visualizations, and detection heatmaps.
  • Openly available with model weights, performance logs, and integration-ready assets for developers.
  • Engineered for edge deployment where fast and dependable detection is critical with limited resources.