Mpowerr HECMS YOLOV11S

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Mpowerr HECMS YOLOV11S — Fast, Efficient, and Highly Reliable

This model combines quick processing with excellent detection results, making it ideal for real-time systems that require both speed and reliability. Its efficient size and high performance make it a great fit for scalable deployments.

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.

97.49%

Precision

01

96.63%

Recall

02

98.89%

mAP@0.5

03

84.20%

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 – YOLOV11S V1 Release

  • Initial release of the YOLOV11S model built for high-speed, high-accuracy elephant detection across various conditions.
  • Achieves 97.49% precision, 96.63% recall, and 98.89% mAP@0.5.
  • Includes detailed evaluation data such as PR curves, confidence analysis, and bounding box visual insights.
  • Model weights, logs, and documentation are provided for easy use in real-world projects and research tasks.
  • Optimized for scenarios that demand both responsiveness and precision in detection pipelines.