Paths:
Find model in path → C:\Users\warg\Ultralytics\runs
To see training results go to path → C:\Users\warg\Ultralytics\runs\detect\train
Find OG model images here → D:\Downloads\WARG AEAC MODEL 1 TRAINING.yolov8\valid\images
Summary:
Overall the model demonstrates strong performance among several metrics.
Confusion Matrix:
1514 (successful detecting landing pad)
21 (fail detecting landing pad put there are landing pads in the image),
32 (predicted as background but the image has the landing pad),
nothing in true negative(all the images have landing pads) .
F1-confidence score:
The f1-score remains high for a large range of threshold(between 0.119 to 0.8).
The model might be overfitting, since the curve is decreasing after the confidence of 0.119.
There is a sharp drop-off after 0.8 confidence threshold, indicate that the model is lack of performance after this threshold.
Precision-Recall curve:
the model’s mean Average Precision is 99.4% across all classes at the intersection of union (IOU) at the threshold 0.5.
The precision achieves both high precision and high recall, the model identify most of the landing pad with few errors.
The curve is nearly right-angle, so both high-recall and high-precision.
The high area under the curve (AUC) implies that the model performs well in distinguishing between the classes. But we only have only 1 class, so this does not suggest anything.
Precision-confidence curve:
confidence 0.05 has 95%. It has an 100% precision at the confidence threshold 0.875 after 0.06 confidence.
Precision is consistently high (close to 1.0) across most confidence thresholds
Recall-confidence curve:
It has an 100% recall at a lowest confidence threshold.
the recall decreases as the threshold for making a positive prediction is raised.
After the confidence threshold 0.8, the model will more likely fail to detect the landing pad.
Metrics:
Precision and recall metrics on the validation set increase and stabilize, indicating good model performance.
The mean Average Precision (mAP) @0.5 and mAP @0.5-0.95 metrics improve over epochs, showing the model's overall effectiveness in object detection tasks.
Box Loss, Classification Loss, and DFL Loss:
Box: bounding box, A lower box_loss means that the predicted bounding boxes are more accurate.
classification: pad-blue, measures the error in the predicted class probabilities for each object in the image compared to the ground truth. A lower cls_loss means that the model is more accurately predicting the class of the objects.
Dfl: detecting various scales of landing pad. A lower dfl_loss indicates that the model is better at handling object deformations and variations in appearance.
All these individual losses decrease over time, showing consistent improvement of the model.