Fridge Items dataset prepare JSONL files fails on base_jsonl_converter and voc_jsonl_converter

[neilmca-inc]

Operating System

Windows

Version Information

This example - https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/automl-standalone-jobs/automl-image-object-detection-task-fridge-items/automl-image-object-detection-task-fridge-items.ipynb

... is linked from the official MS Tutorial page on preparing images https://learn.microsoft.com/en-us/azure/machine-learning/tutorial-auto-train-image-models?view=azureml-api-2&tabs=python

When running it fails on base_jsonl_converter and voc_jsonl_converter on step 2.4. Convert annotation file from COCO to JSONL

Steps to reproduce

1 Run the IPYNB file up to step 2.4
2 Fails on ModuleNotFoundError: No module named 'base_jsonl_converter'

Expected behavior

It works

Actual behavior

`{
"name": "ModuleNotFoundError",
"message": "No module named 'base_jsonl_converter'",
"stack": "---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
Cell In[11], line 2
1 import sys
----> 2 import base_jsonl_converter
3 import voc_jsonl_converter
5 sys.path.insert(0, "../jsonl-conversion/")

ModuleNotFoundError: No module named 'base_jsonl_converter'"
}`

Addition information

I've tried
!pip install base_jsonl_converter
!pip install voc_jsonl_converter
...but these do not work - so assumed that these are imported from somewhere else, but where isn't clear

This is a complicated starter tutorial that so far doesn't work

[marxxxx]

I am struggling at the same step.

[marxxxx]

@neilmca-inc FYI, i got the jsonl conversion working using the following steps

• clone repository locally
• install anaconda locally
• create anaconda environment (using python 3.10)
• activate anaconda environment
• install all the dependencies mentioned in the notebook via conda install (various package sources required)
• install torchvision-cpu (required to run the conversion on my local workstation as i don't have a capable graphic card available)
• install vscode extensions required to run jupyter notebooks
• open workbook in vs code
• change kernel to conda environment created above
• run the steps for creation (if azure ml is not configured locally, replace uri_folder_data_asset.path with a hardcoded url from the data asset)