Roboflow provides a comprehensive guide on how to convert bounding boxes, masks, and polygons. In your case, you want to convert bounding boxes to the xyxy format. Here’s how you can do it using the supervision library:
# Import Supervision
import supervision as sv
# Import bounding box data
detections = sv.Detections.from_roboflow(prediction,classes)
# Export as xyxy data
bounding_boxes = detections.xyxy
This code will convert your bounding box data into the xyxy format. You can find more details in the Ultimate Guide to Converting Bounding Boxes, Masks and Polygons.
However, it’s important to note that if you are using polygon annotations in an object detection project, when you import a version of the dataset into most model training processes, they will automatically convert to bounding boxes. This was mentioned in a forum post by a Roboflow support agent.
If you need to convert your data into bounding boxes for a reason outside of model training, you can use the guide mentioned above. If you’re using Roboflow’s native train, it will handle the distinction and train an object detection dataset. If you want to train outside of the Roboflow platform, you’ll need to do the conversion on your own.
Remember, the quality and quantity of the dataset have a significant impact on the performance of object detection models. High-quality annotations, with accurate object boundaries and precise labeling, play a crucial role in effectively training the model.
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