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Formats
Google Cloud AutoML Vision CSV
Convert to
Sagemaker GroundTruth Manifest
Google Cloud AutoML Vision CSV
The CSV format needed for Google's AutoML Vision tool.
Sagemaker GroundTruth Manifest
The proprietary annotation format used by Amazon SageMaker GroundTruth.

How To Convert Google Cloud AutoML Vision CSV to Sagemaker GroundTruth Manifest

If your labeling tool exported annotations in the

AutoML Vision

format, but you’re trying to use a

model that needs

GroundTruth

annotations, we’ve got you covered. You can convert those in 3 clicks with Roboflow.

Step 1: Create a Free Roboflow Account

Roboflow is the universal conversion tool for computer vision annotation formats. It's completely free for your first 1,000 images. You'll need an account to convert your dataset.

Create Your Free Roboflow Account

Step 2: Upload your data into Roboflow

Once your account has been created, click Create Dataset.

Upload your data to Roboflow by dragging and dropping your
Google Cloud AutoML Vision CSV
images and annotations into the upload space.
Upload Data To Roboflow

To learn more about Google Cloud AutoML Vision, check out our full Cloud AutoML Review where we put it through its paces and compared it against the competing AutoML tools from AWS and Azure.

Step 3: Generate Dataset Version

Next, you can choose Preprocessing and Augmentation options for your dataset version and then click Generate.

Step 4: Export Dataset Version

After generating, you will be prompted to Export your dataset. You can choose to receive your dataset as a .zip file or a curl download link. You can also choose which format you would like to export. Choose

Sagemaker GroundTruth Manifest

.

Congratulations, you have successfully converted your dataset from

Google Cloud AutoML Vision CSV

format to

Sagemaker GroundTruth Manifest

format!

Next Steps

Ready to use your new

GroundTruth

dataset?

To learn more about Amazon's related hosted cloud training service, see our full breakdown of Amazon Rekognition Custom Labels.

Here are some compatible models:

Try an end-to-end computer vision tutorial, check out your dataset health check or experiment with some augmentations.