Mpowerr HECMS YOLOV8X

Shape Leaf Left - Mpowerr HECMS
Shape Leaf Right - Mpowerr HECMS
About Image - Mpowerr HECMS

Mpowerr HECMS YOLOV8X — Advanced Accuracy for Intensive Use Cases

This model is designed for high-performance environments that demand top-level detection accuracy. It provides powerful and consistent results, making it a great fit for use cases where every detection counts under complex conditions.

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.

88.93%

Precision

01

85.41%

Recall

02

92.42%

mAP@0.5

03

63.00%

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
Model Output 2
Model Output 3
Model Output 4
Model Output 5
Model Output 6
Model Output 7
Model Output 8
Model Output 9
Model Output 10
Model Output 11
Model Output 12

Changelog

V1

Mpowerr HECMS – YOLOV8X V1 Release

  • Initial release of the YOLOV8X model tailored for high-capacity elephant detection systems.
  • Achieves 88.93% precision, 85.41% recall, and 92.42% mAP@0.5.
  • Includes comprehensive evaluation reports with PR curves, confidence metrics, and bounding box visuals.
  • Model weights, documentation, and performance summaries are made available for developers and researchers.
  • Built to support detection scenarios where maximum accuracy and detail are essential for decision-making.