Mpowerr HECMS YOLOV12M

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

Mpowerr HECMS YOLOV12M — Reliable Detection for Scaled and Dynamic Use Cases

This model is fine-tuned for environments that demand consistency, precision, and broad coverage. It balances speed and scalability, making it an excellent choice for monitoring systems that operate across varying 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.

97.01%

Precision

01

96.31%

Recall

02

98.62%

mAP@0.5

03

82.99%

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 – YOLOV12M V1 Release

  • Official release of the YOLOV12M model designed for accurate elephant detection across broader surveillance setups.
  • Achieves 97.01% precision, 96.31% recall, and 98.62% mAP@0.5.
  • Evaluation suite includes visual outputs, performance curves, and confidence analysis insights.
  • Openly available with model weights, test logs, and documentation for easy deployment and evaluation.
  • Balanced for performance and efficiency, perfect for both on-device and centralized monitoring environments.