momererkoc/uap-uai-object-detection-yolo12x

🔍 UAP-UAI Detection with YOLOv8-12S

This YOLO12X model is fine-tuned on a custom dataset consisting of annotated aerial images for detecting two object classes: UAP and UAI. The training was conducted using the Ultralytics framework.

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📁 Dataset

• Images: Custom drone footage frames
• Classes:
  • 0: UAP
  • 1: UAI
• Annotation format: YOLO (TXT with class x_center y_center width height)
• Split: No separate val/test — trained and evaluated on the full dataset

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🧠 Model Details

• Base: YOLOv12X
• Params: 4.8M
• Trained for: 100 epochs
• Image size: 640×640
• Batch size: 16
• Optimizer: SGD (default Ultralytics settings)
• Loss Function: CIoU + BCE
• Augmentations: mosaic, random affine, color jitter, horizontal flip

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📊 Evaluation Results

Aggregate Metrics

Metric	Value
mAP@0.5	0.995
mAP@0.5:0.95	0.983
box_loss	0.2368
cls_loss	0.207
dfl_loss	0.8056

🖼️ Sample Predictions

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🚀 Inference Example

from ultralytics import YOLO

model = YOLO("momererkoc/uap-uai-object-detection-yolo12x")
results = model("path/to/image.jpg")
results[0].show()  # show prediction