Do we have an offset for bounding box in Yolov5-seg

[anewworl]

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Question

For some cases the bounding box is to tight lead to loose some part of the objects because i want all of my object fit into the bounding box. The under is the image you can see on the corner is lost some part of the object.

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[glenn-jocher]

@anewworl hello! Thanks for reaching out. In YOLOv5, bounding boxes are generated directly based on the model's predictions, and there isn't a built-in offset parameter to uniformly enlarge them. However, you can manually adjust the bounding box coordinates in the post-processing step of your pipeline. You can add a fixed offset to the x,y coordinates and increase the width and height by a certain factor to ensure your objects are fully enclosed.

For more detailed guidance on post-processing and custom modifications, please refer to our documentation at https://docs.ultralytics.com/yolov5/.

If you need further assistance, feel free to ask. Happy coding! 😊

[anewworl]

@glenn-jocher hello. Thanks a lot for your responses but i have some question is that the yolov5-seg do the detection first then the segmentation later right. And can you show me which line or fine contain the post-processing x,y coordinates and increase the width and height because i dont sure which one.

[glenn-jocher]

@anewworl hello again! YOLOv5-seg performs detection and segmentation simultaneously, though yes, the post-processing of bounding boxes and segmentation masks does happen after the model inference.

For YOLOv5, the post-processing code that deals with bounding box coordinates is not concentrated in a single line or file, as it's part of the inference pipeline. However, you can look into the detect.py script, where the outputs of the model are processed. After the detections are made, you can adjust the bounding box coordinates (x,y,width,height) before they are used for any further processing or visualization.

You would need to add your custom offset logic there, something like:

# Example of adding an offset to the bounding boxes
x1, y1, x2, y2 = original_box_coordinates  # These would be your original bounding box coordinates
offset = 10  # Example offset value
x1 -= offset
y1 -= offset
x2 += offset
y2 += offset
# Now (x1, y1, x2, y2) have the offset applied

Remember to ensure that the new coordinates do not go out of the image boundaries.

For more specific guidance, please refer to the documentation and consider exploring the code to understand where and how the bounding boxes are handled after the model's predictions. If you're not comfortable making these changes, you might want to seek help from someone with more experience in Python and PyTorch.

Keep up the great work! 😄

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