Pick and Place - recognition of peppers with the appropriate rotation

I have top view images:

How to annotate images for best results- I need to find peppers with hole on top:
A) Just Bounding box of properly rotated peppers?
B) Instance Segmentation of peppers (label # 1) and holes (label #2) and calculate the hole size, eccentrity from pepper?
C) Other?

Based on the information provided in the extracts, there are several ways you can approach this task using Roboflow’s tools:

  1. Bounding Box Annotations: You can use the bounding box annotation tool to draw boxes around each pepper. This is a simple and straightforward method, but it might not be as precise if you need to detect specific features like holes on the peppers. You can find more information on how to use the bounding box annotation tool in the Roboflow documentation.

  2. Instance Segmentation: If you need more precise annotations, you can use instance segmentation. This involves drawing polygons around the peppers and the holes. This method can be more time-consuming, but it will provide more detailed information about the shape and location of each object. Roboflow’s Smart Polygon tool can help speed up this process.

  3. Label Assist: Roboflow’s Label Assist feature can automatically annotate images in your dataset using either a previous version of your model or one of the 50,000+ public models on Roboflow Universe. This can help speed up the annotation process and improve the accuracy of your labels.

In terms of which method to choose, it depends on your specific needs. If you need to calculate the hole size and eccentricity from the pepper, instance segmentation might be the best choice. However, if you simply need to detect the presence of holes, bounding box annotations might be sufficient.

Remember to make your labels as tight as possible around the object of interest for the best results, as suggested in the Roboflow blog.

Lastly, it’s recommended to train models often to see how they perform against new images. This will not only speed up your labeling process, but it will also show you which classes your model performs the worst with, allowing you to make necessary adjustments.

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