Yolo Darknet: How to label images

Question

I am new to Object Detection with Yolo and I have questions regarding the labeling (for custom objects): Is there any guideline or tips on how to label images to have high accuracy at the end? Anything I have to take care of?

For example what if I have one object twice next to each other like in the following picture: enter image description here

https://www.manchestereveningnews.co.uk/news/greater-manchester-news/greater-manchester-28-new-buses-17777605.amp

How would you label the black bus? Just the black part or would you assume the whole bus and thus create a box that would include the blue bus as well?

Answer 1

Update:

Below are two examples of images labeled in COCO Dataset that show complex cases. You may explore further on the dataset to find out how they handled different cases.

Another resource, http://vision.stanford.edu/pdf/bbox_submission.pdf.

Image 1:

Image 2:

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These links below may help.

• PASCAL Visual Object Classes Challenge 2007 (VOC2007) Annotation Guidelines on what and how to label,

  http://host.robots.ox.ac.uk/pascal/VOC/voc2007/guidelines.html

• Quote from link below on labeling best practices,

  For occluded objects, label them entirely. If an object is out of view due to another object being in front of it, label the object out of view as if you could see its entirety. Your model will begin to understand the true bounds of objects this way.

  https://blog.roboflow.ai/getting-started-with-labelimg-for-labeling-object-detection-data/

• This article below states to label occluded objects entirely.

  https://towardsdatascience.com/how-to-train-a-custom-object-detection-model-with-yolo-v5-917e9ce13208

Answer 2

You can create labels by color. For example if there are buses with different colors like black,red,blue and others you can label them with names like black_bus, red_bus, blue_bus and default_bus. But accuracy depends on the number of training images. You need thousands of images of each colored bus to get better accuracy.

You can label the example image like this: