Mpowerr HECMS YOLOV11X

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Mpowerr HECMS YOLOV11X — High-Accuracy Detection for Scalable Operations

Built for environments that demand both performance and reach, this model delivers reliable accuracy across large-scale setups. Its consistency under varying conditions makes it a strong fit for intelligent detection systems.

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.

95.23%

Precision

01

91.28%

Recall

02

96.49%

mAP@0.5

03

70.56%

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 – YOLOV11X V1 Release

  • First release of the YOLOV11X model designed for scalable, high-accuracy elephant detection across wide operational zones.
  • Achieves 95.23% precision, 91.28% recall, and 96.49% mAP@0.5.
  • Includes detailed performance evaluations, visual detections, and metric summaries for deployment readiness.
  • Shared openly with model weights, logs, and configuration files for developers and conservation tech researchers.
  • Built for systems where reliable detection across large datasets or continuous streams is a critical requirement.