Understanding Grounding Dino's Thresholds: A Deeper Dive

Grounding Dino (GD) and YOLOv8 are both powerful object detection models, but they employ slightly different strategies for filtering predictions. One area of confusion often lies in the thresholds used by GD. To explain the threshold, let's understand 2 key terms in GD. box_threshold and text_threshold:

1. box_threshold: Controls the minimum confidence score required for a predicted bounding box to be considered valid.
2. text_threshold: Similarly, determines the minimum confidence score for a detected text region to be considered valid.

Now let's relate the 2 similar terms used widely in YOLOv8.

• Confidence: In YOLOv8, the confidence parameter is used to filter predictions based on their likelihood of being correct. It's analogous to GD's thresholds.
• IOU: YOLOv8 also utilizes IOU (Intersection over Union) to evaluate the accuracy of predicted bounding boxes. It's a metric that measures the overlap between a predicted and ground truth bounding box.

Key differences:

• GD's thresholds directly control the confidence level for bounding box and text region predictions.
• YOLOv8's confidence is used in conjunction with IOU to filter predictions based on both confidence and localization accuracy.
• While both models use confidence-based filtering, the specific implementation and the role of other metrics (like IOU) can differ.

Here is the summary for better understanding.

In essence, GD's thresholds are a direct way to control the confidence level for predictions, similar to the confidence parameter in YOLOv8. However, GD's approach is more focused on specific prediction types (bounding boxes and text regions), while YOLOv8 combines confidence with IOU for a more comprehensive evaluation.