Mpowerr HECMS YOLOV11M

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Mpowerr HECMS YOLOV11M — Balanced Power and Precision for Real-World Detection

This model is crafted for scenarios that demand both reliability and scale. It offers strong detection performance while maintaining efficient operation, making it well-suited for large-area monitoring and complex real-time 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.

96.14%

Precision

01

93.66%

Recall

02

97.75%

mAP@0.5

03

82.55%

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 – YOLOV11M V1 Release

  • Initial launch of the YOLOV11M model developed for advanced elephant detection across larger-scale monitoring setups.
  • Achieves 96.14% precision, 93.66% recall, and 97.75% mAP@0.5.
  • Evaluation package includes performance graphs, visual detections, and prediction analysis reports.
  • Provided with open weights, logs, and clear documentation for integration into production and research environments.
  • Balanced for strong accuracy and stability, making it ideal for critical, real-time wildlife safety systems.