Import Google Vertex AI custom model

[user080975]

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Question

Hi,

Currently we have a custom trained model and labeled images in Google Vertex AI. Is it possible to export and import this into Ultralytics HUB for use as a custom model?

If so, what would the steps be?

Thank You!

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[github-actions]

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[kalenmike]

@user080975 Thanks for asking the question. At the moment there is no import capabilities within HUB. What is your intended use case? Perhaps we can add it the feature list provided there is enough interest.

[user080975]

Thank you for your reply! I wrote a small python script to convert the dataset exported from Google Vertex into bounding boxes and associated images into the required format for Ultralytics Hub.

If we need to create segmentation masks using Yolov8 and want to use our own custom model and the original data consists of images / bounding boxes, when I upload the zip folder to Ultralytics Hub, should I select "Detect" or "Segment"?

[github-actions]

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[UltralyticsAssistant]

@user080975 you're on the right track with preparing your dataset. If you are planning to use YOLOv8 for instance segmentation and your original data consists of images with bounding boxes, when uploading the dataset to the Ultralytics HUB, you would typically select "Segment" to work with instance segmentation tasks.

However, if your dataset currently only contains bounding boxes and doesn't include segmentation masks, you may need to first convert your bounding box annotations to segmentation masks. YOLOv8 would require those masks for segmentation tasks. If you don't have segmentation masks and are only performing object detection tasks, then "Detect" would be the appropriate choice.

Remember that for segmentation, your dataset needs to be in a format that includes pixel-wise mask annotations, rather than just bounding boxes. If this process sounds complex or you need further information on dataset preparation and formats, you can check the Ultralytics HUB Docs for guidance.