Model 1 - Results

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

Overview:

Overall the model demonstrates strong performance among several metrics.

Confusion Matrix:

Open

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:

Open

f1-confidence curve

• 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.