Criteria for defining angles in labels and predictions (90º vs. 135º) about ultralytics

[glenn-jocher]

@ekthaliz hello,

Thank you for your detailed questions regarding the OBB angles and their handling in the Ultralytics YOLO codebase. Let's address each of your queries:

1. Angle Definitions in Labels and Predictions:
   The discrepancy in angle ranges between the labels (0 to 90 degrees) and predictions (45 to 135 degrees) is indeed intriguing. Your theories are insightful. Here's a clarification:

   • Theory 1: The conversion process between the label angles and prediction angles is not explicitly visible in the loss computation because the model's learning focuses on minimizing the difference between predicted and actual bounding box coordinates, rather than directly on the angles.
   • Theory 2: The angle range in the labels (0 to 90 degrees) serves as a reference during the annotation phase. However, the model's prediction range (45 to 135 degrees) is designed to ensure a broader and more flexible representation of object orientations during inference.

   The conversion from label angles to prediction angles likely happens implicitly within the model's architecture and training process, ensuring that the predicted angles are within a meaningful range for object detection tasks.

2. Relationship Between OBB Train Model Output and pred_dist:
   The pred_dist tensor represents the predicted distances from anchor points to the bounding box edges. This is crucial for defining the bounding box coordinates during the decoding phase. The relationship between the OBB train model output and pred_dist lies in how the model interprets these distances to construct the final bounding boxes.

3. Different Definitions of xy in xyxyxyxy2xywhr and dist2rbox:
   The difference in the definition of xy before and after the conversion is due to the different stages of the bounding box representation:

   • In xyxyxyxy2xywhr, the function converts the corner points of the bounding box to a center-based representation (cx, cy, w, h, rotation).
   • In dist2rbox, the function decodes the predicted distances back into the corner points (xyxyxyxy) for the final bounding box representation.

   This dual representation ensures that the model can effectively learn and predict bounding boxes by leveraging both center-based and corner-based coordinates at different stages of the process.

If you encounter any specific issues or need further clarification, please provide a minimum reproducible code example. This will help us investigate the problem more effectively. You can refer to our guide on creating a minimum reproducible example here.

Additionally, ensure you are using the latest versions of torch and ultralytics to avoid any compatibility issues.

Feel free to reach out if you have more questions or need further assistance. 😊