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Update how-to-inference-onnx-automl-image-models.md
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articles/machine-learning/v1/how-to-inference-onnx-automl-image-models.md

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@@ -266,7 +266,7 @@ Every ONNX model has a predefined set of input and output formats.
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# [Multi-class image classification](#tab/multi-class)
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This example applies the model trained on the [fridgeObjects](https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/fridgeObjects.zip) dataset with 134 images and 4 classes/labels to explain ONNX model inference. For more information on training an image classification task, see the [multi-class image classification notebook](https://github.com/Azure/azureml-examples/tree/v1-archive/v1/python-sdk/tutorials/automl-with-azureml/image-classification-multiclass).
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This example applies the model trained on the [fridgeObjects](https://automlsamplenotebookdata.blob.core.windows.net/image-object-detection/odFridgeObjects.zip) dataset with 134 images and 4 classes/labels to explain ONNX model inference. For more information on training an image classification task, see the [multi-class image classification notebook](https://github.com/Azure/azureml-examples/tree/v1-archive/v1/python-sdk/tutorials/automl-with-azureml/image-classification-multiclass).
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### Input format
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