Merge pull request #72 from ssalgadodev/patch-1

Update how-to-inference-onnx-automl-image-models.md

Author: prmerger-automator[bot]

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

@@ -333,7 +333,7 @@ Every ONNX model has a predefined set of input and output formats.
 
 # [Multi-class image classification](#tab/multi-class)
 
-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/main/sdk/python/jobs/automl-standalone-jobs/automl-image-classification-multiclass-task-fridge-items).
+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/main/sdk/python/jobs/automl-standalone-jobs/automl-image-classification-multiclass-task-fridge-items).
 
 ### Input format
 
@@ -354,7 +354,7 @@ The output is an array of logits for all the classes/labels.
 
 # [Multi-label image classification](#tab/multi-label)
 
-This example uses the model trained on the [multi-label fridgeObjects dataset](https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/multilabelFridgeObjects.zip) with 128 images and 4 classes/labels to explain ONNX model inference. For more information on model training for multi-label image classification, see the [multi-label image classification notebook](https://github.com/Azure/azureml-examples/tree/main/sdk/python/jobs/automl-standalone-jobs/automl-image-classification-multilabel-task-fridge-items).
+This example uses the model trained on the [multi-label fridgeObjects dataset](https://automlsamplenotebookdata.blob.core.windows.net/image-classification/multilabelFridgeObjects.zip) with 128 images and 4 classes/labels to explain ONNX model inference. For more information on model training for multi-label image classification, see the [multi-label image classification notebook](https://github.com/Azure/azureml-examples/tree/main/sdk/python/jobs/automl-standalone-jobs/automl-image-classification-multilabel-task-fridge-items).
 
 ### Input format
 
@@ -375,8 +375,7 @@ The output is an array of logits for all the classes/labels.
 
 
 # [Object detection with Faster R-CNN or RetinaNet](#tab/object-detect-cnn)
-
-This object detection example uses the model trained on the [fridgeObjects detection dataset](https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip) of 128 images and 4 classes/labels to explain ONNX model inference. This example trains Faster R-CNN models to demonstrate inference steps. For more information on training object detection models, see the [object detection notebook](https://github.com/Azure/azureml-examples/tree/main/sdk/python/jobs/automl-standalone-jobs/automl-image-object-detection-task-fridge-items).
+This object detection example uses the model trained on the [fridgeObjects detection dataset](https://automlsamplenotebookdata.blob.core.windows.net/image-object-detection/odFridgeObjects.zip) of 128 images and 4 classes/labels to explain ONNX model inference. This example trains Faster R-CNN models to demonstrate inference steps. For more information on training object detection models, see the [object detection notebook](https://github.com/Azure/azureml-examples/tree/main/sdk/python/jobs/automl-standalone-jobs/automl-image-object-detection-task-fridge-items).
 
 ### Input format
 
@@ -408,7 +407,7 @@ The following table describes boxes, labels, and scores returned for each sample
 
 # [Object detection with YOLO](#tab/object-detect-yolo)
 
-This object detection example uses the model trained on the [fridgeObjects detection dataset](https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip) of 128 images and 4 classes/labels to explain ONNX model inference. This example trains YOLO models to demonstrate inference steps. For more information on training object detection models, see the [object detection notebook](https://github.com/Azure/azureml-examples/tree/main/sdk/python/jobs/automl-standalone-jobs/automl-image-object-detection-task-fridge-items). 
+This object detection example uses the model trained on the [fridgeObjects detection dataset](https://automlsamplenotebookdata.blob.core.windows.net/image-object-detection/odFridgeObjects.zip) of 128 images and 4 classes/labels to explain ONNX model inference. This example trains YOLO models to demonstrate inference steps. For more information on training object detection models, see the [object detection notebook](https://github.com/Azure/azureml-examples/tree/main/sdk/python/jobs/automl-standalone-jobs/automl-image-object-detection-task-fridge-items). 
 
 ### Input format
 
@@ -430,7 +429,7 @@ Each cell in the list indicates box detections of a sample with shape `(n_boxes,
 
 # [Instance segmentation](#tab/instance-segmentation)
 
-For this instance segmentation example, you use the Mask R-CNN model that has been trained on the [fridgeObjects dataset](https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjectsMask.zip) with 128 images and 4 classes/labels to explain ONNX model inference. For more information on training of the instance segmentation model, see the [instance segmentation notebook](https://github.com/Azure/azureml-examples/tree/main/sdk/python/jobs/automl-standalone-jobs/automl-image-instance-segmentation-task-fridge-items).
+For this instance segmentation example, you use the Mask R-CNN model that has been trained on the [fridgeObjects dataset](https://automlsamplenotebookdata.blob.core.windows.net/image-object-detection/odFridgeObjects.zip) with 128 images and 4 classes/labels to explain ONNX model inference. For more information on training of the instance segmentation model, see the [instance segmentation notebook](https://github.com/Azure/azureml-examples/tree/main/sdk/python/jobs/automl-standalone-jobs/automl-image-instance-segmentation-task-fridge-items).
 
 >[!IMPORTANT]
 > Only Mask R-CNN is supported for instance segmentation tasks. The input and output formats are based on Mask R-CNN only.

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

@@ -266,7 +266,7 @@ Every ONNX model has a predefined set of input and output formats.
 
 # [Multi-class image classification](#tab/multi-class)
 
-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).
+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).
 
 ### Input format
 
@@ -287,7 +287,7 @@ The output is an array of logits for all the classes/labels.
 
 # [Multi-label image classification](#tab/multi-label)
 
-This example uses the model trained on the [multi-label fridgeObjects dataset](https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/multilabelFridgeObjects.zip) with 128 images and 4 classes/labels to explain ONNX model inference. For more information on model training for multi-label image classification, see the [multi-label image classification notebook](https://github.com/Azure/azureml-examples/tree/v1-archive/v1/python-sdk/tutorials/automl-with-azureml/image-classification-multilabel).
+This example uses the model trained on the [multi-label fridgeObjects dataset](https://automlsamplenotebookdata.blob.core.windows.net/image-classification/multilabelFridgeObjects.zip) with 128 images and 4 classes/labels to explain ONNX model inference. For more information on model training for multi-label image classification, see the [multi-label image classification notebook](https://github.com/Azure/azureml-examples/tree/v1-archive/v1/python-sdk/tutorials/automl-with-azureml/image-classification-multilabel).
 
 ### Input format
 
@@ -309,7 +309,7 @@ The output is an array of logits for all the classes/labels.
 
 # [Object detection with Faster R-CNN or RetinaNet](#tab/object-detect-cnn)
 
-This object detection example uses the model trained on the [fridgeObjects detection dataset](https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip) of 128 images and 4 classes/labels to explain ONNX model inference. This example trains Faster R-CNN models to demonstrate inference steps. For more information on training object detection models, see the [object detection notebook](https://github.com/Azure/azureml-examples/tree/v1-archive/v1/python-sdk/tutorials/automl-with-azureml/image-object-detection).
+This object detection example uses the model trained on the [fridgeObjects detection dataset](https://automlsamplenotebookdata.blob.core.windows.net/image-object-detection/odFridgeObjects.zip) of 128 images and 4 classes/labels to explain ONNX model inference. This example trains Faster R-CNN models to demonstrate inference steps. For more information on training object detection models, see the [object detection notebook](https://github.com/Azure/azureml-examples/tree/v1-archive/v1/python-sdk/tutorials/automl-with-azureml/image-object-detection).
 
 ### Input format
 
@@ -341,7 +341,7 @@ The following table describes boxes, labels and scores returned for each sample
 
 # [Object detection with YOLO](#tab/object-detect-yolo)
 
-This object detection example uses the model trained on the [fridgeObjects detection dataset](https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip) of 128 images and 4 classes/labels to explain ONNX model inference. This example trains YOLO models to demonstrate inference steps. For more information on training object detection models, see the [object detection notebook](https://github.com/Azure/azureml-examples/tree/v1-archive/v1/python-sdk/tutorials/automl-with-azureml/image-object-detection). 
+This object detection example uses the model trained on the [fridgeObjects detection dataset](https://automlsamplenotebookdata.blob.core.windows.net/image-object-detection/odFridgeObjects.zip) of 128 images and 4 classes/labels to explain ONNX model inference. This example trains YOLO models to demonstrate inference steps. For more information on training object detection models, see the [object detection notebook](https://github.com/Azure/azureml-examples/tree/v1-archive/v1/python-sdk/tutorials/automl-with-azureml/image-object-detection). 
 
 ### Input format
 
@@ -363,7 +363,7 @@ Each cell in the list indicates box detections of a sample with shape `(n_boxes,
 
 # [Instance segmentation](#tab/instance-segmentation)
 
-For this instance segmentation example, you use the Mask R-CNN model that has been trained on the [fridgeObjects dataset](https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjectsMask.zip) with 128 images and 4 classes/labels to explain ONNX model inference. For more information on training of the instance segmentation model, see the [instance segmentation notebook](https://github.com/Azure/azureml-examples/tree/v1-archive/v1/python-sdk/tutorials/automl-with-azureml/image-instance-segmentation).
+For this instance segmentation example, you use the Mask R-CNN model that has been trained on the [fridgeObjects dataset](https://automlsamplenotebookdata.blob.core.windows.net/image-object-detection/odFridgeObjects.zip) with 128 images and 4 classes/labels to explain ONNX model inference. For more information on training of the instance segmentation model, see the [instance segmentation notebook](https://github.com/Azure/azureml-examples/tree/v1-archive/v1/python-sdk/tutorials/automl-with-azureml/image-instance-segmentation).
 
 >[!IMPORTANT]
 > Only Mask R-CNN is supported for instance segmentation tasks. The input and output formats are based on Mask R-CNN only.