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

Author: ssalgadodev

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

@@ -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.