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#Customer intent: I'm a data scientist with ML knowledge in the computer vision space, looking to build ML models using image data in Azure Machine Learning with full control of the model architecture, hyperparameters, and training and deployment environments.
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* Familiarize yourself with the accepted [schemas for JSONL files for AutoML computer vision experiments](reference-automl-images-schema.md).
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## Get labeled data
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In order to train computer vision models using AutoML, you need to first get labeled training data. The images need to be uploaded to the cloud and label annotations need to be in JSONL format. You can either use the Azure Machine Learning Data Labeling tool to label your data or you could start with pre-labeled image data.
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In order to train computer vision models using AutoML, you need to first get labeled training data. The images need to be uploaded to the cloud and label annotations need to be in JSONL format. You can either use the Azure Machine Learning Data Labeling tool to label your data or you could start with prelabeled image data.
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### Using Azure Machine Learning Data Labeling tool to label your training data
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If you don't have pre-labeled data, you can use Azure Machine Learning's [data labeling tool](how-to-create-image-labeling-projects.md) to manually label images. This tool automatically generates the data required for training in the accepted format.
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If you don't have prelabeled data, you can use Azure Machine Learning's [data labeling tool](how-to-create-image-labeling-projects.md) to manually label images. This tool automatically generates the data required for training in the accepted format.
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It helps to create, manage, and monitor data labeling tasks for
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+ Image classification (multi-class and multi-label)
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+ Object detection (bounding box)
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+ Instance segmentation (polygon)
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If you already have a data labeling project and you want to use that data, you can [export your labeled data as an Azure Machine Learning Dataset](how-to-create-image-labeling-projects.md#export-the-labels) and then access the dataset under 'Datasets' tab in Azure Machine Learning Studio. This exported dataset can then be passed as an input using `azureml:<tabulardataset_name>:<version>` format. Here is an example on how to pass existing dataset as input for training computer vision models.
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If you already have a data labeling project and you want to use that data, you can [export your labeled data as an Azure Machine Learning Dataset](how-to-create-image-labeling-projects.md#export-the-labels) and then access the dataset under 'Datasets' tab in Azure Machine Learning studio. This exported dataset can then be passed as an input using `azureml:<tabulardataset_name>:<version>` format. Here's an example of how to pass existing dataset as input for training computer vision models.
### Using pre-labeled training data from local machine
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If you have previously labeled data that you would like to use to train your model, you will first need to upload the images to the default Azure Blob Storage of your Azure Machine Learning Workspace and register it as a [data asset](how-to-create-data-assets.md).
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### Using prelabeled training data from local machine
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If you have previously labeled data that you would like to use to train your model, you'll first need to upload the images to the default Azure Blob Storage of your Azure Machine Learning Workspace and register it as a [data asset](how-to-create-data-assets.md).
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The following script uploads the image data on your local machine at path "./data/odFridgeObjects" to datastore in Azure Blob Storage. It then creates a new data asset with the name "fridge-items-images-object-detection" in your Azure Machine Learning Workspace.
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If there already exists a data asset with the name "fridge-items-images-object-detection" in your Azure Machine Learning Workspace, it will update the version number of the data asset and point it to the new location where the image data uploaded.
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If there already exists a data asset with the name "fridge-items-images-object-detection" in your Azure Machine Learning Workspace, it updates the version number of the data asset and point it to the new location where the image data uploaded.
To upload the images as a data asset, you run the following CLI v2 command with the path to your .yml file, workspace name, resource group and subscription ID.
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To upload the images as a data asset, you run the following CLI v2 command with the path to your .yml file, workspace name, resource group, and subscription ID.
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```azurecli
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az ml data create -f [PATH_TO_YML_FILE] --workspace-name [YOUR_AZURE_WORKSPACE] --resource-group [YOUR_AZURE_RESOURCE_GROUP] --subscription [YOUR_AZURE_SUBSCRIPTION]
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---
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If you already have your data present in an existing datastore and want to create a data asset out of it, you can do so by providing the path to the data in the datastore, instead of providing the path of your local machine. Update the code [above](how-to-prepare-datasets-for-automl-images.md#using-pre-labeled-training-data-from-local-machine) with the following snippet.
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If you already have your data present in an existing datastore and want to create a data asset out of it, you can do so by providing the path to the data in the datastore, instead of providing the path of your local machine. Update the code [above](how-to-prepare-datasets-for-automl-images.md#using-prelabeled-training-data-from-local-machine) with the following snippet.
Next, you will need to get the label annotations in JSONL format. The schema of labeled data depends on the computer vision task at hand. Refer to [schemas for JSONL files for AutoML computer vision experiments](reference-automl-images-schema.md) to learn more about the required JSONL schema for each task type.
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Next, you'll need to get the label annotations in JSONL format. The schema of labeled data depends on the computer vision task at hand. Refer to [schemas for JSONL files for AutoML computer vision experiments](reference-automl-images-schema.md) to learn more about the required JSONL schema for each task type.
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If your training data is in a different format (like, pascal VOC or COCO), [helper scripts](https://github.com/Azure/azureml-examples/blob/v1-archive/v1/python-sdk/tutorials/automl-with-azureml/image-object-detection/coco2jsonl.py) to convert the data to JSONL are available in [notebook examples](https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/automl-standalone-jobs).
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Once you have created jsonl file following the above steps, you can register it as a data asset using UI. Make sure you select `stream` type in schema section as shown below.
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### Using pre-labeled training data from Azure Blob storage
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### Using prelabeled training data from Azure Blob storage
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If you have your labeled training data present in a container in Azure Blob storage, then you can access it directly from there by [creating a datastore referring to that container](how-to-datastore.md#create-an-azure-blob-datastore).
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