Image classification (multi-class and multi-label).It helps to create, manage, and monitor data labeling tasks for This tool automatically generates the data required for training in the accepted format. If you don't have pre-labeled data, you can use Azure Machine Learning's data labeling tool to manually label images. Using Azure Machine Learning Data Labeling tool to label your training data You can either use the Azure Machine Learning Data Labeling tool to label your data or you could start with pre-labeled image data. The images need to be uploaded to the cloud and label annotations need to be in JSONL format. In order to train computer vision models using AutoML, you need to first get labeled training data. Familiarize yourself with the accepted schemas for JSONL files for AutoML computer vision experiments.Alternatively, you can use Azure Machine Learning's data labeling tool to manually label images, and export the labeled data to use for training your AutoML model. If your labeled training data is in a different format (like, pascal VOC or COCO), you can use a conversion script to first convert it to JSONL, and then create an MLTable. You can create an MLTable from labeled training data in JSONL format. To generate models for computer vision tasks with automated machine learning, you need to bring labeled image data as input for model training in the form of an MLTable. In this article, you learn how to prepare image data for training computer vision models with automated machine learning in Azure Machine Learning. For more information, see Supplemental Terms of Use for Microsoft Azure Previews. Certain features might not be supported or might have constrained capabilities. Support for training computer vision models with automated ML in Azure Machine Learning is an experimental public preview feature.
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