Mpowerr HECMS YOLOV12S

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Mpowerr HECMS YOLOV12S — Balanced Speed and Accuracy for Scalable Detection

Built to handle practical real-world detection tasks, this model offers a fine balance between fast inference and reliable accuracy. It fits well in both edge and cloud environments where performance and efficiency must go hand in hand.

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.

97.26%

Precision

01

96.67%

Recall

02

98.89%

mAP@0.5

03

83.23%

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 – YOLOV12S V1 Release

  • Initial release of the YOLOV12S model optimized for balanced detection across edge and scalable environments.
  • Achieves 97.26% precision, 96.67% recall, and 98.89% mAP@0.5.
  • Evaluation suite includes confidence analytics, prediction visualizations, and detection maps.
  • Open-source weights, evaluation logs, and integration-ready files are available for public use and feedback.
  • Best suited for real-time monitoring applications that require dependable results with minimal latency.