I am working on training yolov8 model using azure ml studio and I use data labeling tool to label my dataset but after this I have difficulty in understanding how this labeled dataset when exported (in MLTable format) can be consumed for training yolo model. I found some reference code for doing so but there are many gaps with this documentation provided by azure like here azureml-examples

1. How do I split my dataset which I exported it to table? below code does not give clarity on this. I did run an AutomatedML job using yolov5 model and it has the functionality of splitting the exported dataset but don't know the implementation.

   from azure.ai.ml import Input
   from azure.ai.ml.constants import AssetTypes
   
   # Training MLTable defined locally, with local data to be uploaded
   my_training_data_input = Input(type=AssetTypes.MLTABLE, path=training_mltable_path)
   
   # Validation MLTable defined locally, with local data to be uploaded
   my_validation_data_input = Input(type=AssetTypes.MLTABLE, path=validation_mltable_path)
   
   # WITH REMOTE PATH: If available already in the cloud/workspace-blob-store
   # my_training_data_input = Input(type=AssetTypes.MLTABLE, 
   path="azureml://datastores/workspaceblobstore/paths/vision-classification/train")
   # my_validation_data_input = Input(type=AssetTypes.MLTABLE, 
   path="azureml://datastores/workspaceblobstore/paths/vision-classification/valid")
   

2. Do I need to download the images from blob container and annotation from labeling tool then convert it into yolo format and then upload it again to data-asset in ml workspace to train the model? because this post here suggests the same, but I found this time-consuming process if we can directly consume it.

3. If I have to go with option 2 How do it convert exported annotation which is in MLTable format to yolo format? Is there any reference code available please share it will be highly appreciated