I found the following piece of Python code useful in parsing the XML file and converting to the YoloV3 annotation format. XML format that you need to parse to convert to Yolo format Most of these tools allow you to annotate an image and export directly in the YoloV3 format mentioned above, however some tools export an XML file in the following format and you have to convert it to the acceptable format. I also found this guide useful in selecting a tool from the myriad of options available.
RECTLABEL YOLO HOW TO
Here is a post i wrote on how to use Cloud Annotations to annotate your images. I have used RectLabel, LabelMe, and my personal choice is Cloud Annotations (because of how you can link it with IBM cloud for storage and allow your team to collaborate on the task). There are plenty of tools that let you annotate images and export to the right format. width, height represent width and height of the bounding box The format of the annotation is ex: 0 0.25 0.44 0.5 0.8 class is the object class, (x,y) are centre coordinates of the bounding box. If you want to train custom data on YoloV3 to detect objects in an image, you will need to annotate(label or draw bounding boxes around objects of interest) your custom data(images) first and then pass on these annotations to the model for training. I won't get in to the details of how Yolo works but you can read about it here I will be using a YoloV3 annotation format as an example to illustrate the process. If you are looking for ways to annotate your images and pass them to your object detection algorithm then here is a short read about how to get it right. Annotating a cricket ball in an image for object detection.
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