Mpowerr HECMS YOLOV8M

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Mpowerr HECMS YOLOV8M — Enhanced Accuracy with Scalable Performance

Designed for applications that demand higher accuracy while maintaining efficient performance, this model delivers robust object detection capabilities. It is ideal for deployments where precision is critical but moderate computational resources are available.

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.

95.87%

Precision

01

93.38%

Recall

02

97.11%

mAP@0.5

03

77.88%

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 – YOLOV8M V1 Release

  • Initial release of the YOLOV8M model developed for elephant detection in diverse field conditions.
  • Achieves 95.87% precision, 93.38% recall, and 97.11% mAP@0.5.
  • Includes full evaluation with visualizations such as PR curves, confidence graphs, and prediction maps.
  • Open access to model weights, metrics, and documentation provided for transparency and community use.
  • Offers improved consistency in detection quality, making it a great choice for critical real-time applications.