Hi, in the blog
polygon-vs-bounding-box-computer-vision-annotation
it is described that a test was carried out with yolo8 and Polygonal boxes on Object Detection
Is there also a guide or colab on what you have to do differently during training?
Based on the provided extracts, there isn’t a specific guide or Colab notebook mentioned for training YOLOv8 with polygonal boxes on object detection. However, the blog post polygon-vs-bounding-box-computer-vision-annotation does mention that the models were trained from scratch using the YOLOv8 architecture for both the bounding box and polygon datasets.
For labeling data, you can use the Polygon annotation tool in Roboflow Annotate. To toggle polygon annotation mode, press “p” on your keyboard, or click on the icon below the bounding box icon in the Roboflow Annotate sidebar. You can also use Smart Polygon to generate annotations by clicking an object of interest. To enable Smart Polygon, click the cursor icon in the right sidebar. There are two versions of Smart Polygon: Standard, which is ideal for small items, and Enhanced, which is ideal for most use cases (source).
For more detailed instructions on how to train a model with YOLOv8, you might want to refer to the Roboflow documentation or other resources not mentioned in the extracts.
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