Mpowerr HECMS YOLOV11L

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Mpowerr HECMS YOLOV11L — High-Capacity Detection with Consistent Performance

Designed for robust operations, this model delivers dependable results in high-demand environments. It's built to handle complex workloads with consistency, making it a strong candidate for mission-critical 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.

96.52%

Precision

01

95.58%

Recall

02

98.37%

mAP@0.5

03

82.51%

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 – YOLOV11L V1 Release

  • Initial release of the YOLOV11L model tailored for high-capacity, high-accuracy elephant detection in complex field conditions.
  • Achieves 96.52% precision, 95.58% recall, and 98.37% mAP@0.5.
  • Comprehensive evaluation includes detection maps, performance visualizations, and confidence analysis tools.
  • Distributed with open weights, results, and documentation to support scalable implementation and testing.
  • Ideal for demanding detection pipelines where consistency and reliability are essential in real-time operations.