Mpowerr HECMS YOLOV12N

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Mpowerr HECMS YOLOV12N — Compact Detection Optimized for Real-Time Systems

Engineered for speed and portability, this model maintains exceptional accuracy while operating efficiently on limited hardware. It's a reliable solution for edge-based systems where detection must be both fast and lightweight.

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.

96.78%

Precision

01

96.39%

Recall

02

98.75%

mAP@0.5

03

82.82%

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 – YOLOV12N V1 Release

  • First release of the YOLOV12N model built for real-time elephant detection on lightweight, resource-constrained devices.
  • Achieves 96.78% precision, 96.39% recall, and 98.75% mAP@0.5.
  • Backed with evaluation reports including confidence plots, detection previews, and model performance insights.
  • Publicly released with open weights, logs, and documentation for integration, testing, and community collaboration.
  • Tailored for scenarios where speed and responsiveness are critical without sacrificing detection accuracy.