diff --git a/cli/foundation-models/system/finetune/image-classification/multiclass-classification/prepare_data.py b/cli/foundation-models/system/finetune/image-classification/multiclass-classification/prepare_data.py index c46c6a95ba1..ede953c0389 100644 --- a/cli/foundation-models/system/finetune/image-classification/multiclass-classification/prepare_data.py +++ b/cli/foundation-models/system/finetune/image-classification/multiclass-classification/prepare_data.py @@ -105,7 +105,7 @@ def upload_data_and_create_jsonl_mltable_files(ml_client, dataset_parent_dir): # Download data print("Downloading data.") - download_url = "https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/fridgeObjects.zip" + download_url = "https://automlsamplenotebookdata.blob.core.windows.net/image-classification/fridgeObjects.zip" # Extract current dataset name from dataset url dataset_name = os.path.basename(download_url).split(".")[0] @@ -132,7 +132,7 @@ def upload_data_and_create_jsonl_mltable_files(ml_client, dataset_parent_dir): path=dataset_dir, type=AssetTypes.URI_FOLDER, description="Fridge-items images", - name="fridge-items-images-2", + name="fridge-items-images-mc-ft", ) uri_folder_data_asset = ml_client.data.create_or_update(my_data) diff --git a/cli/foundation-models/system/finetune/image-classification/multilabel-classification/prepare_data.py b/cli/foundation-models/system/finetune/image-classification/multilabel-classification/prepare_data.py index 6e1441673ee..169e01b1f42 100644 --- a/cli/foundation-models/system/finetune/image-classification/multilabel-classification/prepare_data.py +++ b/cli/foundation-models/system/finetune/image-classification/multilabel-classification/prepare_data.py @@ -108,7 +108,7 @@ def upload_data_and_create_jsonl_mltable_files(ml_client, dataset_parent_dir): # Download data print("Downloading data.") - download_url = "https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/multilabelFridgeObjects.zip" + download_url = "https://automlsamplenotebookdata.blob.core.windows.net/image-classification/multilabelFridgeObjects.zip" # Extract current dataset name from dataset url dataset_name = os.path.basename(download_url).split(".")[0] @@ -135,7 +135,7 @@ def upload_data_and_create_jsonl_mltable_files(ml_client, dataset_parent_dir): path=dataset_dir, type=AssetTypes.URI_FOLDER, description="Fridge-items images", - name="fridge-items-images-2", + name="fridge-items-images-ml-ft", ) uri_folder_data_asset = ml_client.data.create_or_update(my_data) diff --git a/cli/foundation-models/system/finetune/image-instance-segmentation/prepare_data.py b/cli/foundation-models/system/finetune/image-instance-segmentation/prepare_data.py index a0ca0b8a395..3cdd303e3a6 100644 --- a/cli/foundation-models/system/finetune/image-instance-segmentation/prepare_data.py +++ b/cli/foundation-models/system/finetune/image-instance-segmentation/prepare_data.py @@ -157,7 +157,7 @@ def upload_data_and_create_jsonl_mltable_files(ml_client, dataset_parent_dir): os.makedirs(dataset_parent_dir, exist_ok=True) # Download data - download_url = "https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjectsMask.zip" + download_url = "https://automlsamplenotebookdata.blob.core.windows.net/image-instance-segmentation/odFridgeObjectsMask.zip" # Extract current dataset name from dataset url dataset_name = os.path.basename(download_url).split(".")[0] @@ -184,7 +184,7 @@ def upload_data_and_create_jsonl_mltable_files(ml_client, dataset_parent_dir): path=dataset_dir, type=AssetTypes.URI_FOLDER, description="Fridge-items images instance segmentation", - name="fridge-items-images-instance-segmentation", + name="fridge-items-images-is-ft", ) uri_folder_data_asset = ml_client.data.create_or_update(my_data) @@ -198,6 +198,8 @@ def upload_data_and_create_jsonl_mltable_files(ml_client, dataset_parent_dir): subprocess.check_call( [sys.executable, "-m", "pip", "install", "scikit-image==0.19.3"] ) + # Install numpy version compatible with scikit-image==0.19.3. + subprocess.check_call([sys.executable, "-m", "pip", "install", "numpy==1.26.4"]) subprocess.check_call([sys.executable, "-m", "pip", "install", "simplification"]) print("done") diff --git a/cli/foundation-models/system/finetune/image-object-detection/prepare_data.py b/cli/foundation-models/system/finetune/image-object-detection/prepare_data.py index 468ed942332..dcd66c00d1a 100644 --- a/cli/foundation-models/system/finetune/image-object-detection/prepare_data.py +++ b/cli/foundation-models/system/finetune/image-object-detection/prepare_data.py @@ -160,7 +160,7 @@ def upload_data_and_create_jsonl_mltable_files(ml_client, dataset_parent_dir): os.makedirs(dataset_parent_dir, exist_ok=True) # download data - download_url = "https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip" + download_url = "https://automlsamplenotebookdata.blob.core.windows.net/image-object-detection/odFridgeObjects.zip" # Extract current dataset name from dataset url dataset_name = os.path.basename(download_url).split(".")[0] @@ -187,7 +187,7 @@ def upload_data_and_create_jsonl_mltable_files(ml_client, dataset_parent_dir): path=dataset_dir, type=AssetTypes.URI_FOLDER, description="Fridge-items images Object detection", - name="fridge-items-images-object-detection", + name="fridge-items-images-od-ft", ) uri_folder_data_asset = ml_client.data.create_or_update(my_data) diff --git a/cli/foundation-models/system/inference/image-classification/prepare_data.py b/cli/foundation-models/system/inference/image-classification/prepare_data.py index e3adc2860a4..85582856704 100644 --- a/cli/foundation-models/system/inference/image-classification/prepare_data.py +++ b/cli/foundation-models/system/inference/image-classification/prepare_data.py @@ -21,9 +21,9 @@ def download_and_unzip(dataset_parent_dir: str, is_multilabel_dataset: int) -> N # download data if is_multilabel_dataset == 0: - download_url = "https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/fridgeObjects.zip" + download_url = "https://automlsamplenotebookdata.blob.core.windows.net/image-classification/fridgeObjects.zip" else: - download_url = "https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/multilabelFridgeObjects.zip" + download_url = "https://automlsamplenotebookdata.blob.core.windows.net/image-classification/multilabelFridgeObjects.zip" print(f"Downloading data from {download_url}") # Extract current dataset name from dataset url diff --git a/cli/foundation-models/system/inference/image-embeddings/prepare_data.py b/cli/foundation-models/system/inference/image-embeddings/prepare_data.py index c4f025650e5..53eb325c843 100644 --- a/cli/foundation-models/system/inference/image-embeddings/prepare_data.py +++ b/cli/foundation-models/system/inference/image-embeddings/prepare_data.py @@ -20,7 +20,7 @@ def download_and_unzip(dataset_parent_dir: str) -> None: os.makedirs(dataset_parent_dir, exist_ok=True) # download data - download_url = "https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/fridgeObjects.zip" + download_url = "https://automlsamplenotebookdata.blob.core.windows.net/image-classification/fridgeObjects.zip" print(f"Downloading data from {download_url}") # Extract current dataset name from dataset url diff --git a/cli/foundation-models/system/inference/image-instance-segmentation/prepare_data.py b/cli/foundation-models/system/inference/image-instance-segmentation/prepare_data.py index b1a235c36e2..226cea4c9ea 100644 --- a/cli/foundation-models/system/inference/image-instance-segmentation/prepare_data.py +++ b/cli/foundation-models/system/inference/image-instance-segmentation/prepare_data.py @@ -19,7 +19,7 @@ def download_and_unzip(dataset_parent_dir: str) -> None: # download data - download_url = "https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjectsMask.zip" + download_url = "https://automlsamplenotebookdata.blob.core.windows.net/image-instance-segmentation/odFridgeObjectsMask.zip" print(f"Downloading data from {download_url}") # Extract current dataset name from dataset url diff --git a/cli/foundation-models/system/inference/image-object-detection/prepare_data.py b/cli/foundation-models/system/inference/image-object-detection/prepare_data.py index 6335aaf6230..2dceb0bcd7f 100644 --- a/cli/foundation-models/system/inference/image-object-detection/prepare_data.py +++ b/cli/foundation-models/system/inference/image-object-detection/prepare_data.py @@ -19,7 +19,7 @@ def download_and_unzip(dataset_parent_dir: str) -> None: # download data - download_url = "https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip" + download_url = "https://automlsamplenotebookdata.blob.core.windows.net/image-object-detection/odFridgeObjects.zip" print(f"Downloading data from {download_url}") # Extract current dataset name from dataset url diff --git a/cli/foundation-models/system/inference/image-text-embeddings/prepare_data.py b/cli/foundation-models/system/inference/image-text-embeddings/prepare_data.py index e4d1f8cb6bc..3c81bb64755 100644 --- a/cli/foundation-models/system/inference/image-text-embeddings/prepare_data.py +++ b/cli/foundation-models/system/inference/image-text-embeddings/prepare_data.py @@ -20,7 +20,7 @@ def download_and_unzip(dataset_parent_dir: str) -> None: os.makedirs(dataset_parent_dir, exist_ok=True) # download data - download_url = "https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/fridgeObjects.zip" + download_url = "https://automlsamplenotebookdata.blob.core.windows.net/image-classification/fridgeObjects.zip" print(f"Downloading data from {download_url}") # Extract current dataset name from dataset url diff --git a/cli/foundation-models/system/inference/image-to-text/prepare_data.py b/cli/foundation-models/system/inference/image-to-text/prepare_data.py index cda3c5571b0..8e953f3dffb 100644 --- a/cli/foundation-models/system/inference/image-to-text/prepare_data.py +++ b/cli/foundation-models/system/inference/image-to-text/prepare_data.py @@ -18,7 +18,7 @@ def download_and_unzip(dataset_parent_dir: str) -> None: os.makedirs(dataset_parent_dir, exist_ok=True) # download data - download_url = "https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip" + download_url = "https://automlsamplenotebookdata.blob.core.windows.net/image-object-detection/odFridgeObjects.zip" print(f"Downloading data from {download_url}") # Extract current dataset name from dataset url diff --git a/cli/foundation-models/system/inference/mask-generation/prepare_data.py b/cli/foundation-models/system/inference/mask-generation/prepare_data.py index eb279210113..718dc1a4cde 100644 --- a/cli/foundation-models/system/inference/mask-generation/prepare_data.py +++ b/cli/foundation-models/system/inference/mask-generation/prepare_data.py @@ -19,7 +19,7 @@ def download_and_unzip(dataset_parent_dir: str) -> None: # download data - download_url = "https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip" + download_url = "https://automlsamplenotebookdata.blob.core.windows.net/image-object-detection/odFridgeObjects.zip" print(f"Downloading data from {download_url}") # Extract current dataset name from dataset url diff --git a/cli/foundation-models/system/inference/visual-question-answering/prepare_data.py b/cli/foundation-models/system/inference/visual-question-answering/prepare_data.py index bcec0eadf59..da009110346 100644 --- a/cli/foundation-models/system/inference/visual-question-answering/prepare_data.py +++ b/cli/foundation-models/system/inference/visual-question-answering/prepare_data.py @@ -18,7 +18,7 @@ def download_and_unzip(dataset_parent_dir: str) -> None: os.makedirs(dataset_parent_dir, exist_ok=True) # download data - download_url = "https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip" + download_url = "https://automlsamplenotebookdata.blob.core.windows.net/image-object-detection/odFridgeObjects.zip" print(f"Downloading data from {download_url}") # Extract current dataset name from dataset url diff --git a/cli/foundation-models/system/inference/zero-shot-image-classification/prepare_data.py b/cli/foundation-models/system/inference/zero-shot-image-classification/prepare_data.py index eb744ff3e64..f09c3b100e3 100644 --- a/cli/foundation-models/system/inference/zero-shot-image-classification/prepare_data.py +++ b/cli/foundation-models/system/inference/zero-shot-image-classification/prepare_data.py @@ -21,7 +21,7 @@ def download_and_unzip(dataset_parent_dir: str) -> None: os.makedirs(dataset_parent_dir, exist_ok=True) # download data - download_url = "https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/fridgeObjects.zip" + download_url = "https://automlsamplenotebookdata.blob.core.windows.net/image-classification/fridgeObjects.zip" print(f"Downloading data from {download_url}") # Extract current dataset name from dataset url diff --git a/cli/jobs/automl-standalone-jobs/cli-automl-image-classification-multiclass-task-fridge-items/prepare_data.py b/cli/jobs/automl-standalone-jobs/cli-automl-image-classification-multiclass-task-fridge-items/prepare_data.py index 43c6411c47c..33e2f75ae39 100644 --- a/cli/jobs/automl-standalone-jobs/cli-automl-image-classification-multiclass-task-fridge-items/prepare_data.py +++ b/cli/jobs/automl-standalone-jobs/cli-automl-image-classification-multiclass-task-fridge-items/prepare_data.py @@ -105,7 +105,7 @@ def upload_data_and_create_jsonl_mltable_files(ml_client, dataset_parent_dir): # download data print("Downloading data.") - download_url = "https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/fridgeObjects.zip" + download_url = "https://automlsamplenotebookdata.blob.core.windows.net/image-classification/fridgeObjects.zip" # Extract current dataset name from dataset url dataset_name = os.path.basename(download_url).split(".")[0] @@ -132,7 +132,7 @@ def upload_data_and_create_jsonl_mltable_files(ml_client, dataset_parent_dir): path=dataset_dir, type=AssetTypes.URI_FOLDER, description="Fridge-items images", - name="fridge-items-images-2", + name="fridge-items-images-mc", ) uri_folder_data_asset = ml_client.data.create_or_update(my_data) diff --git a/cli/jobs/automl-standalone-jobs/cli-automl-image-classification-multilabel-task-fridge-items/prepare_data.py b/cli/jobs/automl-standalone-jobs/cli-automl-image-classification-multilabel-task-fridge-items/prepare_data.py index 36031e9df14..f632b5492f9 100644 --- a/cli/jobs/automl-standalone-jobs/cli-automl-image-classification-multilabel-task-fridge-items/prepare_data.py +++ b/cli/jobs/automl-standalone-jobs/cli-automl-image-classification-multilabel-task-fridge-items/prepare_data.py @@ -108,7 +108,7 @@ def upload_data_and_create_jsonl_mltable_files(ml_client, dataset_parent_dir): # download data print("Downloading data.") - download_url = "https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/multilabelFridgeObjects.zip" + download_url = "https://automlsamplenotebookdata.blob.core.windows.net/image-classification/multilabelFridgeObjects.zip" # Extract current dataset name from dataset url dataset_name = os.path.basename(download_url).split(".")[0] @@ -135,7 +135,7 @@ def upload_data_and_create_jsonl_mltable_files(ml_client, dataset_parent_dir): path=dataset_dir, type=AssetTypes.URI_FOLDER, description="Fridge-items images", - name="fridge-items-images-2", + name="fridge-items-images-ml", ) uri_folder_data_asset = ml_client.data.create_or_update(my_data) diff --git a/cli/jobs/automl-standalone-jobs/cli-automl-image-instance-segmentation-task-fridge-items/prepare_data.py b/cli/jobs/automl-standalone-jobs/cli-automl-image-instance-segmentation-task-fridge-items/prepare_data.py index 130b40dc303..260c2e4f981 100644 --- a/cli/jobs/automl-standalone-jobs/cli-automl-image-instance-segmentation-task-fridge-items/prepare_data.py +++ b/cli/jobs/automl-standalone-jobs/cli-automl-image-instance-segmentation-task-fridge-items/prepare_data.py @@ -40,7 +40,7 @@ def upload_data_and_create_jsonl_mltable_files(ml_client, dataset_parent_dir): os.makedirs(dataset_parent_dir, exist_ok=True) # download data - download_url = "https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjectsMask.zip" + download_url = "https://automlsamplenotebookdata.blob.core.windows.net/image-instance-segmentation/odFridgeObjectsMask.zip" # Extract current dataset name from dataset url dataset_name = os.path.basename(download_url).split(".")[0] @@ -67,7 +67,7 @@ def upload_data_and_create_jsonl_mltable_files(ml_client, dataset_parent_dir): path=dataset_dir, type=AssetTypes.URI_FOLDER, description="Fridge-items images instance segmentation", - name="fridge-items-images-instance-segmentation", + name="fridge-items-images-is", ) uri_folder_data_asset = ml_client.data.create_or_update(my_data) @@ -81,6 +81,8 @@ def upload_data_and_create_jsonl_mltable_files(ml_client, dataset_parent_dir): subprocess.check_call( [sys.executable, "-m", "pip", "install", "scikit-image==0.19.3"] ) + # Install numpy version compatible with scikit-image==0.19.3. + subprocess.check_call([sys.executable, "-m", "pip", "install", "numpy==1.26.4"]) subprocess.check_call([sys.executable, "-m", "pip", "install", "simplification"]) print("done") diff --git a/cli/jobs/automl-standalone-jobs/cli-automl-image-object-detection-task-fridge-items/prepare_data.py b/cli/jobs/automl-standalone-jobs/cli-automl-image-object-detection-task-fridge-items/prepare_data.py index dbf6293219b..37ede311652 100644 --- a/cli/jobs/automl-standalone-jobs/cli-automl-image-object-detection-task-fridge-items/prepare_data.py +++ b/cli/jobs/automl-standalone-jobs/cli-automl-image-object-detection-task-fridge-items/prepare_data.py @@ -143,7 +143,7 @@ def upload_data_and_create_jsonl_mltable_files(ml_client, dataset_parent_dir): os.makedirs(dataset_parent_dir, exist_ok=True) # download data - download_url = "https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip" + download_url = "https://automlsamplenotebookdata.blob.core.windows.net/image-object-detection/odFridgeObjects.zip" # Extract current dataset name from dataset url dataset_name = os.path.basename(download_url).split(".")[0] @@ -170,7 +170,7 @@ def upload_data_and_create_jsonl_mltable_files(ml_client, dataset_parent_dir): path=dataset_dir, type=AssetTypes.URI_FOLDER, description="Fridge-items images Object detection", - name="fridge-items-images-object-detection", + name="fridge-items-images-od", ) uri_folder_data_asset = ml_client.data.create_or_update(my_data) diff --git a/cli/jobs/pipelines/automl/image-instance-segmentation-task-fridge-items-pipeline/prepare_data.py b/cli/jobs/pipelines/automl/image-instance-segmentation-task-fridge-items-pipeline/prepare_data.py index 130b40dc303..6f3548538ec 100644 --- a/cli/jobs/pipelines/automl/image-instance-segmentation-task-fridge-items-pipeline/prepare_data.py +++ b/cli/jobs/pipelines/automl/image-instance-segmentation-task-fridge-items-pipeline/prepare_data.py @@ -40,7 +40,7 @@ def upload_data_and_create_jsonl_mltable_files(ml_client, dataset_parent_dir): os.makedirs(dataset_parent_dir, exist_ok=True) # download data - download_url = "https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjectsMask.zip" + download_url = "https://automlsamplenotebookdata.blob.core.windows.net/image-instance-segmentation/odFridgeObjectsMask.zip" # Extract current dataset name from dataset url dataset_name = os.path.basename(download_url).split(".")[0] @@ -67,7 +67,7 @@ def upload_data_and_create_jsonl_mltable_files(ml_client, dataset_parent_dir): path=dataset_dir, type=AssetTypes.URI_FOLDER, description="Fridge-items images instance segmentation", - name="fridge-items-images-instance-segmentation", + name="fridge-items-images-is-p", ) uri_folder_data_asset = ml_client.data.create_or_update(my_data) @@ -81,6 +81,8 @@ def upload_data_and_create_jsonl_mltable_files(ml_client, dataset_parent_dir): subprocess.check_call( [sys.executable, "-m", "pip", "install", "scikit-image==0.19.3"] ) + # Install numpy version compatible with scikit-image==0.19.3. + subprocess.check_call([sys.executable, "-m", "pip", "install", "numpy==1.26.4"]) subprocess.check_call([sys.executable, "-m", "pip", "install", "simplification"]) print("done") diff --git a/cli/jobs/pipelines/automl/image-multiclass-classification-fridge-items-pipeline/prepare_data.py b/cli/jobs/pipelines/automl/image-multiclass-classification-fridge-items-pipeline/prepare_data.py index 43c6411c47c..664d4b48a2e 100644 --- a/cli/jobs/pipelines/automl/image-multiclass-classification-fridge-items-pipeline/prepare_data.py +++ b/cli/jobs/pipelines/automl/image-multiclass-classification-fridge-items-pipeline/prepare_data.py @@ -105,7 +105,7 @@ def upload_data_and_create_jsonl_mltable_files(ml_client, dataset_parent_dir): # download data print("Downloading data.") - download_url = "https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/fridgeObjects.zip" + download_url = "https://automlsamplenotebookdata.blob.core.windows.net/image-classification/fridgeObjects.zip" # Extract current dataset name from dataset url dataset_name = os.path.basename(download_url).split(".")[0] @@ -132,7 +132,7 @@ def upload_data_and_create_jsonl_mltable_files(ml_client, dataset_parent_dir): path=dataset_dir, type=AssetTypes.URI_FOLDER, description="Fridge-items images", - name="fridge-items-images-2", + name="fridge-items-images-mc-p", ) uri_folder_data_asset = ml_client.data.create_or_update(my_data) diff --git a/cli/jobs/pipelines/automl/image-multilabel-classification-fridge-items-pipeline/prepare_data.py b/cli/jobs/pipelines/automl/image-multilabel-classification-fridge-items-pipeline/prepare_data.py index 36031e9df14..f8e4a2b07af 100644 --- a/cli/jobs/pipelines/automl/image-multilabel-classification-fridge-items-pipeline/prepare_data.py +++ b/cli/jobs/pipelines/automl/image-multilabel-classification-fridge-items-pipeline/prepare_data.py @@ -108,7 +108,7 @@ def upload_data_and_create_jsonl_mltable_files(ml_client, dataset_parent_dir): # download data print("Downloading data.") - download_url = "https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/multilabelFridgeObjects.zip" + download_url = "https://automlsamplenotebookdata.blob.core.windows.net/image-classification/multilabelFridgeObjects.zip" # Extract current dataset name from dataset url dataset_name = os.path.basename(download_url).split(".")[0] @@ -135,7 +135,7 @@ def upload_data_and_create_jsonl_mltable_files(ml_client, dataset_parent_dir): path=dataset_dir, type=AssetTypes.URI_FOLDER, description="Fridge-items images", - name="fridge-items-images-2", + name="fridge-items-images-ml-p", ) uri_folder_data_asset = ml_client.data.create_or_update(my_data) diff --git a/cli/jobs/pipelines/automl/image-object-detection-task-fridge-items-pipeline/prepare_data.py b/cli/jobs/pipelines/automl/image-object-detection-task-fridge-items-pipeline/prepare_data.py index dbf6293219b..7c56c811651 100644 --- a/cli/jobs/pipelines/automl/image-object-detection-task-fridge-items-pipeline/prepare_data.py +++ b/cli/jobs/pipelines/automl/image-object-detection-task-fridge-items-pipeline/prepare_data.py @@ -143,7 +143,7 @@ def upload_data_and_create_jsonl_mltable_files(ml_client, dataset_parent_dir): os.makedirs(dataset_parent_dir, exist_ok=True) # download data - download_url = "https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip" + download_url = "https://automlsamplenotebookdata.blob.core.windows.net/image-object-detection/odFridgeObjects.zip" # Extract current dataset name from dataset url dataset_name = os.path.basename(download_url).split(".")[0] @@ -170,7 +170,7 @@ def upload_data_and_create_jsonl_mltable_files(ml_client, dataset_parent_dir): path=dataset_dir, type=AssetTypes.URI_FOLDER, description="Fridge-items images Object detection", - name="fridge-items-images-object-detection", + name="fridge-items-images-od-p", ) uri_folder_data_asset = ml_client.data.create_or_update(my_data) diff --git a/sdk/python/foundation-models/system/evaluation/image-classification/multiclass-classification/image-multiclass-classification.ipynb b/sdk/python/foundation-models/system/evaluation/image-classification/multiclass-classification/image-multiclass-classification.ipynb index 69242997e59..f722c8593ec 100644 --- a/sdk/python/foundation-models/system/evaluation/image-classification/multiclass-classification/image-multiclass-classification.ipynb +++ b/sdk/python/foundation-models/system/evaluation/image-classification/multiclass-classification/image-multiclass-classification.ipynb @@ -10,7 +10,7 @@ "This sample shows how use the evaluate a group of models against a given set of metrics for the `image-classification` task. \n", "\n", "### Evaluation dataset\n", - "We will use the [fridgeObjects](https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/fridgeObjects.zip) dataset.\n", + "We will use the [fridgeObjects](https://automlsamplenotebookdata.blob.core.windows.net/image-classification/fridgeObjects.zip) dataset.\n", "\n", "### Model\n", "The goal of evaluating models is to compare their performance on a variety of metrics. `image-classification` is a generic task type. As such, the models you pick to compare must be finetuned for the same scenario. Given that we have the dataset, we would like to look for models finetuned for this specific scenario. We will compare `microsoft-beit-base-patch16-224-pt22k-ft22k` and `microsoft-swinv2-base-patch4-window12-192-22k` in this sample, which are available in the `azureml` system registry.\n", @@ -258,7 +258,7 @@ "source": [ "### 4. Prepare the dataset for fine-tuning the model\n", "\n", - "We will use the [fridgeObjects](https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/fridgeObjects.zip) dataset. The fridge object dataset is stored in a directory. There are four different folders inside:\n", + "We will use the [fridgeObjects](https://automlsamplenotebookdata.blob.core.windows.net/image-classification/fridgeObjects.zip) dataset. The fridge object dataset is stored in a directory. There are four different folders inside:\n", "- /water_bottle\n", "- /milk_bottle\n", "- /carton\n", @@ -288,7 +288,7 @@ "os.makedirs(dataset_parent_dir, exist_ok=True)\n", "\n", "# download data\n", - "download_url = \"https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/fridgeObjects.zip\"\n", + "download_url = \"https://automlsamplenotebookdata.blob.core.windows.net/image-classification/fridgeObjects.zip\"\n", "\n", "# Extract current dataset name from dataset url\n", "dataset_name = os.path.split(download_url)[-1].split(\".\")[0]\n", diff --git a/sdk/python/foundation-models/system/evaluation/image-classification/multilabel-classification/image-multilabel-classification.ipynb b/sdk/python/foundation-models/system/evaluation/image-classification/multilabel-classification/image-multilabel-classification.ipynb index eac3e07c234..9d069a18dc7 100644 --- a/sdk/python/foundation-models/system/evaluation/image-classification/multilabel-classification/image-multilabel-classification.ipynb +++ b/sdk/python/foundation-models/system/evaluation/image-classification/multilabel-classification/image-multilabel-classification.ipynb @@ -285,7 +285,7 @@ "os.makedirs(dataset_parent_dir, exist_ok=True)\n", "\n", "# download data\n", - "download_url = \"https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/multilabelFridgeObjects.zip\"\n", + "download_url = \"https://automlsamplenotebookdata.blob.core.windows.net/image-classification/multilabelFridgeObjects.zip\"\n", "\n", "# Extract current dataset name from dataset url\n", "dataset_name = os.path.split(download_url)[-1].split(\".\")[0]\n", diff --git a/sdk/python/foundation-models/system/evaluation/image-instance-segmentation/image-instance-segmentation.ipynb b/sdk/python/foundation-models/system/evaluation/image-instance-segmentation/image-instance-segmentation.ipynb index 47b179bb0f6..1f1a925ce08 100644 --- a/sdk/python/foundation-models/system/evaluation/image-instance-segmentation/image-instance-segmentation.ipynb +++ b/sdk/python/foundation-models/system/evaluation/image-instance-segmentation/image-instance-segmentation.ipynb @@ -10,7 +10,7 @@ "This sample shows how use the evaluate a group of models against a given set of metrics for the `image-instance-segmentation` task. \n", "\n", "### Evaluation dataset\n", - "We will use the [odfridgeObjectsMask](https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjectsMask.zip) dataset.\n", + "We will use the [odfridgeObjectsMask](https://automlsamplenotebookdata.blob.core.windows.net/image-instance-segmentation/odFridgeObjectsMask.zip) dataset.\n", "\n", "### Model\n", "The goal of evaluating models is to compare their performance on a variety of metrics. `image-instance-segmentation` is a generic task type. As such, the models you pick to compare must be finetuned for the same scenario. Given that we have the dataset, we would like to look for models finetuned for this specific scenario. We will compare different versions of `mmd-3x-mask-rcnn_swin-t-p4-w7_fpn_1x_coco` in this sample, which are available in the `azureml` system registry.\n", @@ -257,7 +257,7 @@ "source": [ "### 4. Prepare the dataset for fine-tuning the model\n", "\n", - "We will use the [odfridgeObjectsMask](https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjectsMask.zip), a toy dataset called Fridge Objects, which consists of 128 images of 4 labels of beverage container {`can`, `carton`, `milk bottle`, `water bottle`} photos taken on different backgrounds.\n", + "We will use the [odfridgeObjectsMask](https://automlsamplenotebookdata.blob.core.windows.net/image-instance-segmentation/odFridgeObjectsMask.zip), a toy dataset called Fridge Objects, which consists of 128 images of 4 labels of beverage container {`can`, `carton`, `milk bottle`, `water bottle`} photos taken on different backgrounds.\n", "\n", "All images in this notebook are hosted in [this repository](https://github.com/microsoft/computervision-recipes) and are made available under the [MIT license](https://github.com/microsoft/computervision-recipes/blob/master/LICENSE).\n", "\n", @@ -283,7 +283,7 @@ "os.makedirs(dataset_parent_dir, exist_ok=True)\n", "\n", "# Download data\n", - "download_url = \"https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjectsMask.zip\"\n", + "download_url = \"https://automlsamplenotebookdata.blob.core.windows.net/image-instance-segmentation/odFridgeObjectsMask.zip\"\n", "\n", "# Extract current dataset name from dataset url\n", "dataset_name = os.path.split(download_url)[-1].split(\".\")[0]\n", diff --git a/sdk/python/foundation-models/system/evaluation/image-object-detection/image-object-detection.ipynb b/sdk/python/foundation-models/system/evaluation/image-object-detection/image-object-detection.ipynb index 796e749e7a2..a8450f695b5 100644 --- a/sdk/python/foundation-models/system/evaluation/image-object-detection/image-object-detection.ipynb +++ b/sdk/python/foundation-models/system/evaluation/image-object-detection/image-object-detection.ipynb @@ -10,7 +10,7 @@ "This sample shows how use the evaluate a group of models against a given set of metrics for the `image-object-detection` task. \n", "\n", "### Evaluation dataset\n", - "We will use the [odfridgeObjects](https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip) dataset.\n", + "We will use the [odfridgeObjects](https://automlsamplenotebookdata.blob.core.windows.net/image-object-detection/odFridgeObjects.zip) dataset.\n", "\n", "### Model\n", "The goal of evaluating models is to compare their performance on a variety of metrics. `image-object-detection` is a generic task type. As such, the models you pick to compare must be finetuned for the same scenario. Given that we have the dataset, we would like to look for models finetuned for this specific scenario. We will compare `mmd-3x-yolof_r50_c5_8x8_1x_coco` and `mmd-3x-sparse-rcnn_r50_fpn_300-proposals_crop-ms-480-800-3x_coco` in this sample, which are available in the `azureml` system registry.\n", @@ -258,7 +258,7 @@ "source": [ "### 4. Prepare the dataset for fine-tuning the model\n", "\n", - "We will use the [odfridgeObjects](https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip), a toy dataset called Fridge Objects, which consists of 128 images of 4 labels of beverage container {`can`, `carton`, `milk bottle`, `water bottle`} photos taken on different backgrounds.\n", + "We will use the [odfridgeObjects](https://automlsamplenotebookdata.blob.core.windows.net/image-object-detection/odFridgeObjects.zip), a toy dataset called Fridge Objects, which consists of 128 images of 4 labels of beverage container {`can`, `carton`, `milk bottle`, `water bottle`} photos taken on different backgrounds.\n", "\n", "All images in this notebook are hosted in [this repository](https://github.com/microsoft/computervision-recipes) and are made available under the [MIT license](https://github.com/microsoft/computervision-recipes/blob/master/LICENSE).\n", "\n", @@ -284,7 +284,7 @@ "os.makedirs(dataset_parent_dir, exist_ok=True)\n", "\n", "# Download data\n", - "download_url = \"https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip\"\n", + "download_url = \"https://automlsamplenotebookdata.blob.core.windows.net/image-object-detection/odFridgeObjects.zip\"\n", "\n", "# Extract current dataset name from dataset url\n", "dataset_name = os.path.split(download_url)[-1].split(\".\")[0]\n", diff --git a/sdk/python/foundation-models/system/finetune/image-classification/multiclass-classification/hftransformers-fridgeobjects-multiclass-classification.ipynb b/sdk/python/foundation-models/system/finetune/image-classification/multiclass-classification/hftransformers-fridgeobjects-multiclass-classification.ipynb index d0e16cbfc5a..ca2c37a634e 100644 --- a/sdk/python/foundation-models/system/finetune/image-classification/multiclass-classification/hftransformers-fridgeobjects-multiclass-classification.ipynb +++ b/sdk/python/foundation-models/system/finetune/image-classification/multiclass-classification/hftransformers-fridgeobjects-multiclass-classification.ipynb @@ -10,7 +10,7 @@ "This sample shows how to use `transformers_image_classification_pipeline` component from the `azureml` system registry to fine tune a model for multi-class image classification task using fridgeObjects Dataset. We then deploy the fine tuned model to an online endpoint for real time inference.\n", "\n", "### Training data\n", - "We will use the [fridgeObjects](https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/fridgeObjects.zip) dataset.\n", + "We will use the [fridgeObjects](https://automlsamplenotebookdata.blob.core.windows.net/image-classification/fridgeObjects.zip) dataset.\n", "\n", "### Model\n", "We will use the `microsoft-beit-base-patch16-224-pt22k-ft22k` model in this notebook. If you need to fine tune a model that is available on HuggingFace, but not available in `azureml` system registry, you can either register the model and use the registered model or use the `model_name` parameter to instruct the components to pull the model directly from HuggingFace.\n", @@ -265,7 +265,7 @@ "source": [ "### 4. Prepare the dataset for fine-tuning the model\n", "\n", - "We will use the [fridgeObjects](https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/fridgeObjects.zip) dataset. The fridge object dataset is stored in a directory. There are four different folders inside:\n", + "We will use the [fridgeObjects](https://automlsamplenotebookdata.blob.core.windows.net/image-classification/fridgeObjects.zip) dataset. The fridge object dataset is stored in a directory. There are four different folders inside:\n", "- /water_bottle\n", "- /milk_bottle\n", "- /carton\n", @@ -295,7 +295,7 @@ "os.makedirs(dataset_parent_dir, exist_ok=True)\n", "\n", "# Download data\n", - "download_url = \"https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/fridgeObjects.zip\"\n", + "download_url = \"https://automlsamplenotebookdata.blob.core.windows.net/image-classification/fridgeObjects.zip\"\n", "\n", "# Extract current dataset name from dataset url\n", "dataset_name = os.path.split(download_url)[-1].split(\".\")[0]\n", diff --git a/sdk/python/foundation-models/system/finetune/image-classification/multilabel-classification/hftransformers-fridgeobjects-multilabel-classification.ipynb b/sdk/python/foundation-models/system/finetune/image-classification/multilabel-classification/hftransformers-fridgeobjects-multilabel-classification.ipynb index 53c5c9cde5b..4cbbace1e81 100644 --- a/sdk/python/foundation-models/system/finetune/image-classification/multilabel-classification/hftransformers-fridgeobjects-multilabel-classification.ipynb +++ b/sdk/python/foundation-models/system/finetune/image-classification/multilabel-classification/hftransformers-fridgeobjects-multilabel-classification.ipynb @@ -292,7 +292,7 @@ "os.makedirs(dataset_parent_dir, exist_ok=True)\n", "\n", "# Download data\n", - "download_url = \"https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/multilabelFridgeObjects.zip\"\n", + "download_url = \"https://automlsamplenotebookdata.blob.core.windows.net/image-classification/multilabelFridgeObjects.zip\"\n", "\n", "# Extract current dataset name from dataset url\n", "dataset_name = os.path.split(download_url)[-1].split(\".\")[0]\n", diff --git a/sdk/python/foundation-models/system/finetune/image-instance-segmentation/mmdetection-fridgeobjects-instance-segmentation.ipynb b/sdk/python/foundation-models/system/finetune/image-instance-segmentation/mmdetection-fridgeobjects-instance-segmentation.ipynb index e7f2825f2a5..22a00fc9ec1 100644 --- a/sdk/python/foundation-models/system/finetune/image-instance-segmentation/mmdetection-fridgeobjects-instance-segmentation.ipynb +++ b/sdk/python/foundation-models/system/finetune/image-instance-segmentation/mmdetection-fridgeobjects-instance-segmentation.ipynb @@ -10,7 +10,7 @@ "This sample shows how to use `mmdetection_image_objectdetection_instancesegmentation_pipeline` component from the `azureml` system registry to fine tune a model for image instance-segmentation task using fridgeObjects Dataset. We then deploy the fine tuned model to an online endpoint for real time inference.\n", "\n", "### Training data\n", - "We will use the [odfridgeObjectsMask](https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjectsMask.zip) dataset.\n", + "We will use the [odfridgeObjectsMask](https://automlsamplenotebookdata.blob.core.windows.net/image-instance-segmentation/odFridgeObjectsMask.zip) dataset.\n", "\n", "### Model\n", "We will use the `mask-rcnn_swin-t-p4-w7_fpn_1x_coco` model in this notebook. If you need to fine tune a model that is available on MMDetection model zoo, but not available in `azureml` system registry, you can either register the model and use the registered model or use the `model_name` parameter to instruct the components to pull the model directly from MMDetection model zoo.\n", @@ -280,7 +280,7 @@ "source": [ "### 4. Prepare the dataset for fine-tuning the model\n", "\n", - "We will use the [odfridgeObjectsMask](https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjectsMask.zip), a toy dataset called Fridge Objects, which consists of 128 images of 4 labels of beverage container {`can`, `carton`, `milk bottle`, `water bottle`} photos taken on different backgrounds.\n", + "We will use the [odfridgeObjectsMask](https://automlsamplenotebookdata.blob.core.windows.net/image-instance-segmentation/odFridgeObjectsMask.zip), a toy dataset called Fridge Objects, which consists of 128 images of 4 labels of beverage container {`can`, `carton`, `milk bottle`, `water bottle`} photos taken on different backgrounds.\n", "\n", "All images in this notebook are hosted in [this repository](https://github.com/microsoft/computervision-recipes) and are made available under the [MIT license](https://github.com/microsoft/computervision-recipes/blob/master/LICENSE).\n", "\n", @@ -306,7 +306,7 @@ "os.makedirs(dataset_parent_dir, exist_ok=True)\n", "\n", "# Download data\n", - "download_url = \"https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjectsMask.zip\"\n", + "download_url = \"https://automlsamplenotebookdata.blob.core.windows.net/image-instance-segmentation/odFridgeObjectsMask.zip\"\n", "\n", "# Extract current dataset name from dataset url\n", "dataset_name = os.path.split(download_url)[-1].split(\".\")[0]\n", diff --git a/sdk/python/foundation-models/system/finetune/image-object-detection/mmdetection-fridgeobjects-object-detection.ipynb b/sdk/python/foundation-models/system/finetune/image-object-detection/mmdetection-fridgeobjects-object-detection.ipynb index 0aa27b280b5..ad8f3b5647a 100644 --- a/sdk/python/foundation-models/system/finetune/image-object-detection/mmdetection-fridgeobjects-object-detection.ipynb +++ b/sdk/python/foundation-models/system/finetune/image-object-detection/mmdetection-fridgeobjects-object-detection.ipynb @@ -10,7 +10,7 @@ "This sample shows how to use `mmdetection_image_objectdetection_instancesegmentation_pipeline` component from the `azureml` system registry to fine tune a model for image object-detection task using fridgeObjects Dataset. We then deploy the fine tuned model to an online endpoint for real time inference.\n", "\n", "### Training data\n", - "We will use the [odfridgeObjects](https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip) dataset.\n", + "We will use the [odfridgeObjects](https://automlsamplenotebookdata.blob.core.windows.net/image-object-detection/odFridgeObjects.zip) dataset.\n", "\n", "### Model\n", "We will use the `yolof_r50_c5_8x8_1x_coco` model in this notebook. If you need to fine tune a model that is available on MMDetection model zoo, but not available in `azureml` system registry, you can either register the model and use the registered model or use the `model_name` parameter to instruct the components to pull the model directly from MMDetection model zoo.\n", @@ -285,7 +285,7 @@ "source": [ "### 4. Prepare the dataset for fine-tuning the model\n", "\n", - "We will use the [odfridgeObjects](https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip), a toy dataset called Fridge Objects, which consists of 128 images of 4 labels of beverage container {`can`, `carton`, `milk bottle`, `water bottle`} photos taken on different backgrounds.\n", + "We will use the [odfridgeObjects](https://automlsamplenotebookdata.blob.core.windows.net/image-object-detection/odFridgeObjects.zip), a toy dataset called Fridge Objects, which consists of 128 images of 4 labels of beverage container {`can`, `carton`, `milk bottle`, `water bottle`} photos taken on different backgrounds.\n", "\n", "All images in this notebook are hosted in [this repository](https://github.com/microsoft/computervision-recipes) and are made available under the [MIT license](https://github.com/microsoft/computervision-recipes/blob/master/LICENSE).\n", "\n", @@ -311,7 +311,7 @@ "os.makedirs(dataset_parent_dir, exist_ok=True)\n", "\n", "# Download data\n", - "download_url = \"https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip\"\n", + "download_url = \"https://automlsamplenotebookdata.blob.core.windows.net/image-object-detection/odFridgeObjects.zip\"\n", "\n", "# Extract current dataset name from dataset url\n", "dataset_name = os.path.split(download_url)[-1].split(\".\")[0]\n", diff --git a/sdk/python/foundation-models/system/inference/image-classification/image-classification-batch-endpoint.ipynb b/sdk/python/foundation-models/system/inference/image-classification/image-classification-batch-endpoint.ipynb index ba1ff0f56e1..d13539c7cb5 100644 --- a/sdk/python/foundation-models/system/inference/image-classification/image-classification-batch-endpoint.ipynb +++ b/sdk/python/foundation-models/system/inference/image-classification/image-classification-batch-endpoint.ipynb @@ -19,7 +19,7 @@ "Models that can perform the `image-classification` task are tagged with `image-classification`. We will use the `microsoft-beit-base-patch16-224-pt22k-ft22k` model in this notebook. If you opened this notebook from a specific model card, remember to replace the specific model name. If you don't find a model that suits your scenario or domain, you can discover and [import models from HuggingFace hub](../../import/import_model_into_registry.ipynb) and then use them for inference. \n", "\n", "### Inference data\n", - "We will use the [fridgeObjects](https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/fridgeObjects.zip) dataset for image multi-class classification.\n", + "We will use the [fridgeObjects](https://automlsamplenotebookdata.blob.core.windows.net/image-classification/fridgeObjects.zip) dataset for image multi-class classification.\n", "\n", "\n", "### Outline\n", @@ -155,7 +155,7 @@ "source": [ "### 3. Prepare data for inference - using a folder of images; using a csv file with base64 images\n", "\n", - "We will use the [fridgeObjects](https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/fridgeObjects.zip) dataset for multi-class classification. The fridge object dataset is stored in a directory. There are four different folders inside:\n", + "We will use the [fridgeObjects](https://automlsamplenotebookdata.blob.core.windows.net/image-classification/fridgeObjects.zip) dataset for multi-class classification. The fridge object dataset is stored in a directory. There are four different folders inside:\n", "- /water_bottle\n", "- /milk_bottle\n", "- /carton\n", @@ -181,7 +181,7 @@ "os.makedirs(dataset_parent_dir, exist_ok=True)\n", "\n", "# Download data\n", - "download_url = \"https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/fridgeObjects.zip\"\n", + "download_url = \"https://automlsamplenotebookdata.blob.core.windows.net/image-classification/fridgeObjects.zip\"\n", "\n", "# Extract current dataset name from dataset url\n", "dataset_name = os.path.split(download_url)[-1].split(\".\")[0]\n", diff --git a/sdk/python/foundation-models/system/inference/image-classification/image-classification-online-endpoint.ipynb b/sdk/python/foundation-models/system/inference/image-classification/image-classification-online-endpoint.ipynb index f7425fc9bc5..95cbe93ab80 100644 --- a/sdk/python/foundation-models/system/inference/image-classification/image-classification-online-endpoint.ipynb +++ b/sdk/python/foundation-models/system/inference/image-classification/image-classification-online-endpoint.ipynb @@ -19,7 +19,7 @@ "Models that can perform the `image-classification` task are tagged with `image-classification`. We will use the `microsoft-beit-base-patch16-224-pt22k-ft22k` model in this notebook. If you opened this notebook from a specific model card, remember to replace the specific model name. If you don't find a model that suits your scenario or domain, you can discover and [import models from HuggingFace hub](../../import/import_model_into_registry.ipynb) and then use them for inference.\n", "\n", "### Inference data\n", - "We will use the [fridgeObjects](https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/fridgeObjects.zip) dataset.\n", + "We will use the [fridgeObjects](https://automlsamplenotebookdata.blob.core.windows.net/image-classification/fridgeObjects.zip) dataset.\n", "\n", "\n", "### Outline\n", @@ -120,7 +120,7 @@ "source": [ "### 3. Prepare data for inference\n", "\n", - "We will use the [fridgeObjects](https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/fridgeObjects.zip) dataset for multi-class classification task. The fridge object dataset is stored in a directory. There are four different folders inside:\n", + "We will use the [fridgeObjects](https://automlsamplenotebookdata.blob.core.windows.net/image-classification/fridgeObjects.zip) dataset for multi-class classification task. The fridge object dataset is stored in a directory. There are four different folders inside:\n", "- /water_bottle\n", "- /milk_bottle\n", "- /carton\n", @@ -146,7 +146,7 @@ "os.makedirs(dataset_parent_dir, exist_ok=True)\n", "\n", "# Download data\n", - "download_url = \"https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/fridgeObjects.zip\"\n", + "download_url = \"https://automlsamplenotebookdata.blob.core.windows.net/image-classification/fridgeObjects.zip\"\n", "\n", "# Extract current dataset name from dataset url\n", "dataset_name = os.path.split(download_url)[-1].split(\".\")[0]\n", diff --git a/sdk/python/foundation-models/system/inference/image-embeddings/image-embeddings-batch-endpoint.ipynb b/sdk/python/foundation-models/system/inference/image-embeddings/image-embeddings-batch-endpoint.ipynb index 73e0b4bdd66..83cbcc0a90e 100644 --- a/sdk/python/foundation-models/system/inference/image-embeddings/image-embeddings-batch-endpoint.ipynb +++ b/sdk/python/foundation-models/system/inference/image-embeddings/image-embeddings-batch-endpoint.ipynb @@ -15,7 +15,7 @@ "Models that can perform the `embeddings` task are tagged with `embeddings` in the catalog. We will use the `Facebook-DinoV2-Image-Embeddings-ViT-Base` model in this notebook. If you opened this notebook from a specific model card, remember to replace the specific model name. If you don't find a model that suits your scenario or domain, you can discover and [import models from HuggingFace hub](../../import/import_model_into_registry.ipynb) and then use them for inference. \n", "\n", "### Inference data\n", - "We will use the [fridgeObjects](https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/fridgeObjects.zip) dataset.\n", + "We will use the [fridgeObjects](https://automlsamplenotebookdata.blob.core.windows.net/image-classification/fridgeObjects.zip) dataset.\n", "\n", "\n", "### Outline\n", @@ -146,7 +146,7 @@ "source": [ "### 3. Prepare data for inference - Using a folder of csv files\n", "\n", - "We will use the [fridgeObjects](https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/fridgeObjects.zip) dataset for multi-class classification. The fridge object dataset is stored in a directory. There are four different folders inside:\n", + "We will use the [fridgeObjects](https://automlsamplenotebookdata.blob.core.windows.net/image-classification/fridgeObjects.zip) dataset for multi-class classification. The fridge object dataset is stored in a directory. There are four different folders inside:\n", "- /water_bottle\n", "- /milk_bottle\n", "- /carton\n", @@ -172,7 +172,7 @@ "os.makedirs(dataset_parent_dir, exist_ok=True)\n", "\n", "# Download data\n", - "download_url = \"https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/fridgeObjects.zip\"\n", + "download_url = \"https://automlsamplenotebookdata.blob.core.windows.net/image-classification/fridgeObjects.zip\"\n", "\n", "# Extract current dataset name from dataset url\n", "dataset_name = os.path.split(download_url)[-1].split(\".\")[0]\n", diff --git a/sdk/python/foundation-models/system/inference/image-embeddings/image-embeddings-online-endpoint.ipynb b/sdk/python/foundation-models/system/inference/image-embeddings/image-embeddings-online-endpoint.ipynb index b952d2c9a02..3e421d69815 100644 --- a/sdk/python/foundation-models/system/inference/image-embeddings/image-embeddings-online-endpoint.ipynb +++ b/sdk/python/foundation-models/system/inference/image-embeddings/image-embeddings-online-endpoint.ipynb @@ -15,7 +15,7 @@ "Models that can perform the `embeddings` task are tagged with `embeddings` in the catalog. We will use the `Facebook-DinoV2-Image-Embeddings-ViT-Base` model in this notebook. If you opened this notebook from a specific model card, remember to replace the specific model name. If you don't find a model that suits your scenario or domain, you can discover and [import models from HuggingFace hub](../../import/import_model_into_registry.ipynb) and then use them for inference. \n", "\n", "### Inference data\n", - "We will use the [fridgeObjects](https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/fridgeObjects.zip) dataset.\n", + "We will use the [fridgeObjects](https://automlsamplenotebookdata.blob.core.windows.net/image-classification/fridgeObjects.zip) dataset.\n", "\n", "\n", "### Outline\n", @@ -111,7 +111,7 @@ "source": [ "### 3. Prepare data for inference\n", "\n", - "We will use the [fridgeObjects](https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/fridgeObjects.zip) dataset for multi-class classification task. The fridge object dataset is stored in a directory. There are four different folders inside:\n", + "We will use the [fridgeObjects](https://automlsamplenotebookdata.blob.core.windows.net/image-classification/fridgeObjects.zip) dataset for multi-class classification task. The fridge object dataset is stored in a directory. There are four different folders inside:\n", "- /water_bottle\n", "- /milk_bottle\n", "- /carton\n", @@ -137,7 +137,7 @@ "os.makedirs(dataset_parent_dir, exist_ok=True)\n", "\n", "# Download data\n", - "download_url = \"https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/fridgeObjects.zip\"\n", + "download_url = \"https://automlsamplenotebookdata.blob.core.windows.net/image-classification/fridgeObjects.zip\"\n", "\n", "# Extract current dataset name from dataset url\n", "dataset_name = os.path.split(download_url)[-1].split(\".\")[0]\n", diff --git a/sdk/python/foundation-models/system/inference/image-instance-segmentation/image-instance-segmentation-batch-endpoint.ipynb b/sdk/python/foundation-models/system/inference/image-instance-segmentation/image-instance-segmentation-batch-endpoint.ipynb index 42447315ca3..7b4cd0c46be 100644 --- a/sdk/python/foundation-models/system/inference/image-instance-segmentation/image-instance-segmentation-batch-endpoint.ipynb +++ b/sdk/python/foundation-models/system/inference/image-instance-segmentation/image-instance-segmentation-batch-endpoint.ipynb @@ -16,7 +16,7 @@ "Models that can perform the `image-instance-segmentation` task are tagged with `image-instance-segmentation`. We will use the `mask-rcnn_swin-t-p4-w7_fpn_1x_coco` model in this notebook. If you opened this notebook from a specific model card, remember to replace the specific model name.\n", "\n", "### Inference data\n", - "We will use the [fridgeObjects](https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjectsMask.zip) dataset for image instance segmentation.\n", + "We will use the [fridgeObjects](https://automlsamplenotebookdata.blob.core.windows.net/image-instance-segmentation/odFridgeObjectsMask.zip) dataset for image instance segmentation.\n", "\n", "\n", "### Outline\n", @@ -151,7 +151,7 @@ "source": [ "### 3. Prepare data for inference - using a folder of images; using a csv file with base64 images\n", "\n", - "We will use the [odFridgeObjectsMask](https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjectsMask.zip) dataset for image instance segmentation.\n" + "We will use the [odFridgeObjectsMask](https://automlsamplenotebookdata.blob.core.windows.net/image-instance-segmentation/odFridgeObjectsMask.zip) dataset for image instance segmentation.\n" ] }, { @@ -172,7 +172,7 @@ "os.makedirs(dataset_parent_dir, exist_ok=True)\n", "\n", "# Download data\n", - "download_url = \"https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjectsMask.zip\"\n", + "download_url = \"https://automlsamplenotebookdata.blob.core.windows.net/image-instance-segmentation/odFridgeObjectsMask.zip\"\n", "\n", "# Extract current dataset name from dataset url\n", "dataset_name = os.path.split(download_url)[-1].split(\".\")[0]\n", diff --git a/sdk/python/foundation-models/system/inference/image-instance-segmentation/image-instance-segmentation-online-endpoint.ipynb b/sdk/python/foundation-models/system/inference/image-instance-segmentation/image-instance-segmentation-online-endpoint.ipynb index cac9c1de2d9..44097b341d0 100644 --- a/sdk/python/foundation-models/system/inference/image-instance-segmentation/image-instance-segmentation-online-endpoint.ipynb +++ b/sdk/python/foundation-models/system/inference/image-instance-segmentation/image-instance-segmentation-online-endpoint.ipynb @@ -16,7 +16,7 @@ "Models that can perform the `image-instance-segmentation` task are tagged with `image-instance-segmentation`. We will use the `mask-rcnn_swin-t-p4-w7_fpn_1x_coco` model in this notebook. If you opened this notebook from a specific model card, remember to replace the specific model name.\n", "\n", "### Inference data\n", - "We will use the [fridgeObjects](https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjectsMask.zip) dataset for instance segemntation.\n", + "We will use the [fridgeObjects](https://automlsamplenotebookdata.blob.core.windows.net/image-instance-segmentation/odFridgeObjectsMask.zip) dataset for instance segemntation.\n", "\n", "\n", "### Outline\n", @@ -117,7 +117,7 @@ "source": [ "### 3. Prepare data for inference\n", "\n", - "We will use the [odFridgeObjectsMask](https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjectsMask.zip) dataset for instance segmentation task." + "We will use the [odFridgeObjectsMask](https://automlsamplenotebookdata.blob.core.windows.net/image-instance-segmentation/odFridgeObjectsMask.zip) dataset for instance segmentation task." ] }, { @@ -137,7 +137,7 @@ "os.makedirs(dataset_parent_dir, exist_ok=True)\n", "\n", "# Download data\n", - "download_url = \"https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjectsMask.zip\"\n", + "download_url = \"https://automlsamplenotebookdata.blob.core.windows.net/image-instance-segmentation/odFridgeObjectsMask.zip\"\n", "\n", "# Extract current dataset name from dataset url\n", "dataset_name = os.path.split(download_url)[-1].split(\".\")[0]\n", diff --git a/sdk/python/foundation-models/system/inference/image-object-detection/image-object-detection-batch-endpoint.ipynb b/sdk/python/foundation-models/system/inference/image-object-detection/image-object-detection-batch-endpoint.ipynb index 8c567d3db5a..0a9d27ef869 100644 --- a/sdk/python/foundation-models/system/inference/image-object-detection/image-object-detection-batch-endpoint.ipynb +++ b/sdk/python/foundation-models/system/inference/image-object-detection/image-object-detection-batch-endpoint.ipynb @@ -16,7 +16,7 @@ "Models that can perform the `image-object-detection` task are tagged with `image-object-detection`. We will use the `yolof_r50_c5_8x8_1x_coco` model in this notebook. If you opened this notebook from a specific model card, remember to replace the specific model name.\n", "\n", "### Inference data\n", - "We will use the [fridgeObjects](https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip) dataset for image object detection.\n", + "We will use the [fridgeObjects](https://automlsamplenotebookdata.blob.core.windows.net/image-object-detection/odFridgeObjects.zip) dataset for image object detection.\n", "\n", "\n", "### Outline\n", @@ -151,7 +151,7 @@ "source": [ "### 3. Prepare data for inference - using a folder of images; using a csv file with base64 images\n", "\n", - "We will use the [odFridgeObjects](https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip) dataset for image object detection.\n" + "We will use the [odFridgeObjects](https://automlsamplenotebookdata.blob.core.windows.net/image-object-detection/odFridgeObjects.zip) dataset for image object detection.\n" ] }, { @@ -172,7 +172,7 @@ "os.makedirs(dataset_parent_dir, exist_ok=True)\n", "\n", "# Download data\n", - "download_url = \"https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip\"\n", + "download_url = \"https://automlsamplenotebookdata.blob.core.windows.net/image-object-detection/odFridgeObjects.zip\"\n", "\n", "# Extract current dataset name from dataset url\n", "dataset_name = os.path.split(download_url)[-1].split(\".\")[0]\n", diff --git a/sdk/python/foundation-models/system/inference/image-object-detection/image-object-detection-online-endpoint.ipynb b/sdk/python/foundation-models/system/inference/image-object-detection/image-object-detection-online-endpoint.ipynb index 07b36996ac1..83b4ceed696 100644 --- a/sdk/python/foundation-models/system/inference/image-object-detection/image-object-detection-online-endpoint.ipynb +++ b/sdk/python/foundation-models/system/inference/image-object-detection/image-object-detection-online-endpoint.ipynb @@ -16,7 +16,7 @@ "Models that can perform the `image-object-detection` task are tagged with `image-object-detection`. We will use the `yolof_r50_c5_8x8_1x_coco` model in this notebook. If you opened this notebook from a specific model card, remember to replace the specific model name.\n", "\n", "### Inference data\n", - "We will use the [fridgeObjects](https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip) dataset for object detection.\n", + "We will use the [fridgeObjects](https://automlsamplenotebookdata.blob.core.windows.net/image-object-detection/odFridgeObjects.zip) dataset for object detection.\n", "\n", "\n", "### Outline\n", @@ -117,7 +117,7 @@ "source": [ "### 3. Prepare data for inference\n", "\n", - "We will use the [odFridgeObjects](https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip) dataset for object detection." + "We will use the [odFridgeObjects](https://automlsamplenotebookdata.blob.core.windows.net/image-object-detection/odFridgeObjects.zip) dataset for object detection." ] }, { @@ -137,7 +137,7 @@ "os.makedirs(dataset_parent_dir, exist_ok=True)\n", "\n", "# Download data\n", - "download_url = \"https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip\"\n", + "download_url = \"https://automlsamplenotebookdata.blob.core.windows.net/image-object-detection/odFridgeObjects.zip\"\n", "\n", "# Extract current dataset name from dataset url\n", "dataset_name = os.path.split(download_url)[-1].split(\".\")[0]\n", diff --git a/sdk/python/foundation-models/system/inference/image-text-embeddings/image-text-embeddings-batch-endpoint.ipynb b/sdk/python/foundation-models/system/inference/image-text-embeddings/image-text-embeddings-batch-endpoint.ipynb index 38dbb2ddfd1..d4e3c5e7edf 100644 --- a/sdk/python/foundation-models/system/inference/image-text-embeddings/image-text-embeddings-batch-endpoint.ipynb +++ b/sdk/python/foundation-models/system/inference/image-text-embeddings/image-text-embeddings-batch-endpoint.ipynb @@ -16,7 +16,7 @@ "Models that can perform the `embeddings` task are tagged with `embeddings`. We will use the `OpenAI-CLIP-Image-Text-Embeddings-vit-base-patch32` model in this notebook. If you opened this notebook from a specific model card, remember to replace the specific model name. If you don't find a model that suits your scenario or domain, you can discover and [import models from HuggingFace hub](../../import/import_model_into_registry.ipynb) and then use them for inference. \n", "\n", "### Inference data\n", - "We will use the [fridgeObjects](https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/fridgeObjects.zip) dataset.\n", + "We will use the [fridgeObjects](https://automlsamplenotebookdata.blob.core.windows.net/image-classification/fridgeObjects.zip) dataset.\n", "\n", "\n", "### Outline\n", @@ -151,7 +151,7 @@ "source": [ "### 3. Prepare data for inference - Using a folder of csv files\n", "\n", - "We will use the [fridgeObjects](https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/fridgeObjects.zip) dataset for multi-class classification. The fridge object dataset is stored in a directory. There are four different folders inside:\n", + "We will use the [fridgeObjects](https://automlsamplenotebookdata.blob.core.windows.net/image-classification/fridgeObjects.zip) dataset for multi-class classification. The fridge object dataset is stored in a directory. There are four different folders inside:\n", "- /water_bottle\n", "- /milk_bottle\n", "- /carton\n", @@ -177,7 +177,7 @@ "os.makedirs(dataset_parent_dir, exist_ok=True)\n", "\n", "# Download data\n", - "download_url = \"https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/fridgeObjects.zip\"\n", + "download_url = \"https://automlsamplenotebookdata.blob.core.windows.net/image-classification/fridgeObjects.zip\"\n", "\n", "# Extract current dataset name from dataset url\n", "dataset_name = os.path.split(download_url)[-1].split(\".\")[0]\n", diff --git a/sdk/python/foundation-models/system/inference/image-text-embeddings/image-text-embeddings-online-endpoint.ipynb b/sdk/python/foundation-models/system/inference/image-text-embeddings/image-text-embeddings-online-endpoint.ipynb index 26d29f75da1..a62ebcf79a5 100644 --- a/sdk/python/foundation-models/system/inference/image-text-embeddings/image-text-embeddings-online-endpoint.ipynb +++ b/sdk/python/foundation-models/system/inference/image-text-embeddings/image-text-embeddings-online-endpoint.ipynb @@ -16,7 +16,7 @@ "Models that can perform the `embeddings` task are tagged with `embeddings`. We will use the `OpenAI-CLIP-Image-Text-Embeddings-vit-base-patch32` model in this notebook. If you opened this notebook from a specific model card, remember to replace the specific model name. If you don't find a model that suits your scenario or domain, you can discover and [import models from HuggingFace hub](../../import/import_model_into_registry.ipynb) and then use them for inference. \n", "\n", "### Inference data\n", - "We will use the [fridgeObjects](https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/fridgeObjects.zip) dataset.\n", + "We will use the [fridgeObjects](https://automlsamplenotebookdata.blob.core.windows.net/image-classification/fridgeObjects.zip) dataset.\n", "\n", "\n", "### Outline\n", @@ -114,7 +114,7 @@ "source": [ "### 3. Prepare data for inference\n", "\n", - "We will use the [fridgeObjects](https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/fridgeObjects.zip) dataset for multi-class classification task. The fridge object dataset is stored in a directory. There are four different folders inside:\n", + "We will use the [fridgeObjects](https://automlsamplenotebookdata.blob.core.windows.net/image-classification/fridgeObjects.zip) dataset for multi-class classification task. The fridge object dataset is stored in a directory. There are four different folders inside:\n", "- /water_bottle\n", "- /milk_bottle\n", "- /carton\n", @@ -140,7 +140,7 @@ "os.makedirs(dataset_parent_dir, exist_ok=True)\n", "\n", "# Download data\n", - "download_url = \"https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/fridgeObjects.zip\"\n", + "download_url = \"https://automlsamplenotebookdata.blob.core.windows.net/image-classification/fridgeObjects.zip\"\n", "\n", "# Extract current dataset name from dataset url\n", "dataset_name = os.path.split(download_url)[-1].split(\".\")[0]\n", diff --git a/sdk/python/foundation-models/system/inference/image-text-embeddings/text-to-image-retrieval.ipynb b/sdk/python/foundation-models/system/inference/image-text-embeddings/text-to-image-retrieval.ipynb index 47c21e35f87..7d2d8bce1ee 100644 --- a/sdk/python/foundation-models/system/inference/image-text-embeddings/text-to-image-retrieval.ipynb +++ b/sdk/python/foundation-models/system/inference/image-text-embeddings/text-to-image-retrieval.ipynb @@ -15,7 +15,7 @@ "Models that can perform the `embeddings` task are tagged with `embeddings`. We will use the `OpenAI-CLIP-Image-Text-Embeddings-vit-base-patch32` model in this notebook. If you don't find a model that suits your scenario or domain, you can discover and [import models from HuggingFace hub](../../import/import_model_into_registry.ipynb) and then use them for inference. \n", "\n", "### Inference data\n", - "We will use the [fridgeObjects](https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/fridgeObjects.zip) dataset.\n", + "We will use the [fridgeObjects](https://automlsamplenotebookdata.blob.core.windows.net/image-classification/fridgeObjects.zip) dataset.\n", "\n", "\n", "### Outline\n", @@ -88,7 +88,7 @@ "source": [ "### 2. Prepare data for inference\n", "\n", - "We will use the [fridgeObjects](https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/fridgeObjects.zip) dataset for multi-class classification task. The fridge object dataset is stored in a directory. There are four different folders inside:\n", + "We will use the [fridgeObjects](https://automlsamplenotebookdata.blob.core.windows.net/image-classification/fridgeObjects.zip) dataset for multi-class classification task. The fridge object dataset is stored in a directory. There are four different folders inside:\n", "- /water_bottle\n", "- /milk_bottle\n", "- /carton\n", @@ -112,7 +112,7 @@ "os.makedirs(dataset_parent_dir, exist_ok=True)\n", "\n", "# download data\n", - "download_url = \"https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/fridgeObjects.zip\"\n", + "download_url = \"https://automlsamplenotebookdata.blob.core.windows.net/image-classification/fridgeObjects.zip\"\n", "\n", "# Extract current dataset name from dataset url\n", "dataset_name = os.path.split(download_url)[-1].split(\".\")[0]\n", diff --git a/sdk/python/foundation-models/system/inference/image-to-text/image-to-text-batch-endpoint.ipynb b/sdk/python/foundation-models/system/inference/image-to-text/image-to-text-batch-endpoint.ipynb index 61b3a63960d..f5f9d0896dc 100644 --- a/sdk/python/foundation-models/system/inference/image-to-text/image-to-text-batch-endpoint.ipynb +++ b/sdk/python/foundation-models/system/inference/image-to-text/image-to-text-batch-endpoint.ipynb @@ -16,7 +16,7 @@ "Models that can perform the `image-to-text` task are tagged with `image-to-text`. We will use the `Salesforce/blip-image-captioning-base` model in this notebook. If you opened this notebook from a specific model card, remember to replace the specific model name. If you don't find a model that suits your scenario or domain, you can discover and [import models from HuggingFace hub](../../import/import_model_into_registry.ipynb) and then use them for inference.\n", "\n", "### Inference data\n", - "We will use the [odFridgeObjects](https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip) dataset.\n", + "We will use the [odFridgeObjects](https://automlsamplenotebookdata.blob.core.windows.net/image-object-detection/odFridgeObjects.zip) dataset.\n", "\n", "\n", "### Outline\n", @@ -150,7 +150,7 @@ "source": [ "### 3. Prepare data for inference - Using a folder of CSV files with base64 images\n", "\n", - "We will use the [odFridgeObjects](https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip) dataset for this image-to-text task.\n", + "We will use the [odFridgeObjects](https://automlsamplenotebookdata.blob.core.windows.net/image-object-detection/odFridgeObjects.zip) dataset for this image-to-text task.\n", "\n" ] }, @@ -172,7 +172,7 @@ "os.makedirs(dataset_parent_dir, exist_ok=True)\n", "\n", "# Download data\n", - "download_url = \"https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip\"\n", + "download_url = \"https://automlsamplenotebookdata.blob.core.windows.net/image-object-detection/odFridgeObjects.zip\"\n", "\n", "# Extract current dataset name from dataset url\n", "dataset_name = os.path.split(download_url)[-1].split(\".\")[0]\n", diff --git a/sdk/python/foundation-models/system/inference/image-to-text/image-to-text-online-endpoint.ipynb b/sdk/python/foundation-models/system/inference/image-to-text/image-to-text-online-endpoint.ipynb index 9a4284fd85f..a0b9c0a0373 100644 --- a/sdk/python/foundation-models/system/inference/image-to-text/image-to-text-online-endpoint.ipynb +++ b/sdk/python/foundation-models/system/inference/image-to-text/image-to-text-online-endpoint.ipynb @@ -16,7 +16,7 @@ "Models that can perform the `image-to-text` task are tagged with `image-to-text`. We will use the `Salesforce/blip-image-captioning-base` model in this notebook. If you opened this notebook from a specific model card, remember to replace the specific model name. If you don't find a model that suits your scenario or domain, you can discover and [import models from HuggingFace hub](../../import/import_model_into_registry.ipynb) and then use them for inference.\n", "\n", "### Inference data\n", - "We will use the [odFridgeObjects](https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip) dataset.\n", + "We will use the [odFridgeObjects](https://automlsamplenotebookdata.blob.core.windows.net/image-object-detection/odFridgeObjects.zip) dataset.\n", "\n", "\n", "### Outline\n", @@ -116,7 +116,7 @@ "source": [ "### 3. Prepare data for inference\n", "\n", - "We will use the [odFridgeObjects](https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip) dataset for this image-to-text task." + "We will use the [odFridgeObjects](https://automlsamplenotebookdata.blob.core.windows.net/image-object-detection/odFridgeObjects.zip) dataset for this image-to-text task." ] }, { @@ -136,7 +136,7 @@ "os.makedirs(dataset_parent_dir, exist_ok=True)\n", "\n", "# Download data\n", - "download_url = \"https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip\"\n", + "download_url = \"https://automlsamplenotebookdata.blob.core.windows.net/image-object-detection/odFridgeObjects.zip\"\n", "\n", "# Extract current dataset name from dataset url\n", "dataset_name = os.path.split(download_url)[-1].split(\".\")[0]\n", diff --git a/sdk/python/foundation-models/system/inference/mask-generation/mask-generation-batch-endpoint.ipynb b/sdk/python/foundation-models/system/inference/mask-generation/mask-generation-batch-endpoint.ipynb index a6703a0c74e..ec3d08b33bd 100644 --- a/sdk/python/foundation-models/system/inference/mask-generation/mask-generation-batch-endpoint.ipynb +++ b/sdk/python/foundation-models/system/inference/mask-generation/mask-generation-batch-endpoint.ipynb @@ -15,7 +15,7 @@ "Models that can perform the `mask-generation` task are tagged with `mask-generation`. We will use the `facebook/sam-vit-huge` model in this notebook. If you opened this notebook from a specific model card, remember to replace the specific model name.\n", "\n", "### Inference data\n", - "We will use the [odFridgeObjects](https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip) dataset.\n", + "We will use the [odFridgeObjects](https://automlsamplenotebookdata.blob.core.windows.net/image-object-detection/odFridgeObjects.zip) dataset.\n", "\n", "\n", "### Outline\n", @@ -142,7 +142,7 @@ "source": [ "### 3. Prepare data for inference\n", "\n", - "We will use the [odFridgeObjects](https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip) dataset for this mask-generation task.\n", + "We will use the [odFridgeObjects](https://automlsamplenotebookdata.blob.core.windows.net/image-object-detection/odFridgeObjects.zip) dataset for this mask-generation task.\n", "\n" ] }, @@ -164,7 +164,7 @@ "os.makedirs(dataset_parent_dir, exist_ok=True)\n", "\n", "# Download data\n", - "download_url = \"https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip\"\n", + "download_url = \"https://automlsamplenotebookdata.blob.core.windows.net/image-object-detection/odFridgeObjects.zip\"\n", "\n", "# Extract current dataset name from dataset url\n", "dataset_name = os.path.split(download_url)[-1].split(\".\")[0]\n", diff --git a/sdk/python/foundation-models/system/inference/mask-generation/mask-generation-online-endpoint.ipynb b/sdk/python/foundation-models/system/inference/mask-generation/mask-generation-online-endpoint.ipynb index c49f892a4ca..c63452dfdc8 100644 --- a/sdk/python/foundation-models/system/inference/mask-generation/mask-generation-online-endpoint.ipynb +++ b/sdk/python/foundation-models/system/inference/mask-generation/mask-generation-online-endpoint.ipynb @@ -15,7 +15,7 @@ "Models that can perform the `mask-generation` task are tagged with `mask-generation`. We will use the `facebook-sam-vit-huge` model in this notebook. If you opened this notebook from a specific model card, remember to replace the specific model name.\n", "\n", "### Inference data\n", - "We will use the [odFridgeObjects](https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip) dataset.\n", + "We will use the [odFridgeObjects](https://automlsamplenotebookdata.blob.core.windows.net/image-object-detection/odFridgeObjects.zip) dataset.\n", "\n", "\n", "### Outline\n", @@ -109,7 +109,7 @@ "source": [ "### 3. Prepare data for inference\n", "\n", - "We will use the [odFridgeObjects](https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip) dataset for this mask-generation task." + "We will use the [odFridgeObjects](https://automlsamplenotebookdata.blob.core.windows.net/image-object-detection/odFridgeObjects.zip) dataset for this mask-generation task." ] }, { @@ -129,7 +129,7 @@ "os.makedirs(dataset_parent_dir, exist_ok=True)\n", "\n", "# Download data\n", - "download_url = \"https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip\"\n", + "download_url = \"https://automlsamplenotebookdata.blob.core.windows.net/image-object-detection/odFridgeObjects.zip\"\n", "\n", "# Extract current dataset name from dataset url\n", "dataset_name = os.path.split(download_url)[-1].split(\".\")[0]\n", diff --git a/sdk/python/foundation-models/system/inference/visual-question-answering/visual-question-answering-batch-endpoint.ipynb b/sdk/python/foundation-models/system/inference/visual-question-answering/visual-question-answering-batch-endpoint.ipynb index e15f270a02c..0d582645995 100644 --- a/sdk/python/foundation-models/system/inference/visual-question-answering/visual-question-answering-batch-endpoint.ipynb +++ b/sdk/python/foundation-models/system/inference/visual-question-answering/visual-question-answering-batch-endpoint.ipynb @@ -16,7 +16,7 @@ "Models that can perform the `visual-question-answering` task are tagged with `visual-question-answering`. We will use the `Salesforce/blip-vqa-base` model in this notebook. If you opened this notebook from a specific model card, remember to replace the specific model name.\n", "\n", "### Inference data\n", - "We will use the [odFridgeObjects](https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip) dataset.\n", + "We will use the [odFridgeObjects](https://automlsamplenotebookdata.blob.core.windows.net/image-object-detection/odFridgeObjects.zip) dataset.\n", "\n", "\n", "### Outline\n", @@ -150,7 +150,7 @@ "source": [ "### 3. Prepare data for inference - Using a folder of csv files with base64 images\n", "\n", - "We will use the [odFridgeObjects](https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip) dataset for this image-to-text task.\n", + "We will use the [odFridgeObjects](https://automlsamplenotebookdata.blob.core.windows.net/image-object-detection/odFridgeObjects.zip) dataset for this image-to-text task.\n", "\n" ] }, @@ -172,7 +172,7 @@ "os.makedirs(dataset_parent_dir, exist_ok=True)\n", "\n", "# Download data\n", - "download_url = \"https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip\"\n", + "download_url = \"https://automlsamplenotebookdata.blob.core.windows.net/image-object-detection/odFridgeObjects.zip\"\n", "\n", "# Extract current dataset name from dataset url\n", "dataset_name = os.path.split(download_url)[-1].split(\".\")[0]\n", diff --git a/sdk/python/foundation-models/system/inference/visual-question-answering/visual-question-answering-online-endpoint.ipynb b/sdk/python/foundation-models/system/inference/visual-question-answering/visual-question-answering-online-endpoint.ipynb index f3341e62f2e..18315e9188c 100644 --- a/sdk/python/foundation-models/system/inference/visual-question-answering/visual-question-answering-online-endpoint.ipynb +++ b/sdk/python/foundation-models/system/inference/visual-question-answering/visual-question-answering-online-endpoint.ipynb @@ -16,7 +16,7 @@ "Models that can perform the `visual-question-answering` task are tagged with `visual-question-answering`. We will use the `Salesforce/blip-vqa-base` model in this notebook. If you opened this notebook from a specific model card, remember to replace the specific model name.\n", "\n", "### Inference data\n", - "We will use the [odFridgeObjects](https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip) dataset.\n", + "We will use the [odFridgeObjects](https://automlsamplenotebookdata.blob.core.windows.net/image-object-detection/odFridgeObjects.zip) dataset.\n", "\n", "\n", "### Outline\n", @@ -116,7 +116,7 @@ "source": [ "### 3. Prepare data for inference\n", "\n", - "We will use the [odFridgeObjects](https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip) dataset for this image-to-text task." + "We will use the [odFridgeObjects](https://automlsamplenotebookdata.blob.core.windows.net/image-object-detection/odFridgeObjects.zip) dataset for this image-to-text task." ] }, { @@ -136,7 +136,7 @@ "os.makedirs(dataset_parent_dir, exist_ok=True)\n", "\n", "# Download data\n", - "download_url = \"https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip\"\n", + "download_url = \"https://automlsamplenotebookdata.blob.core.windows.net/image-object-detection/odFridgeObjects.zip\"\n", "\n", "# Extract current dataset name from dataset url\n", "dataset_name = os.path.split(download_url)[-1].split(\".\")[0]\n", diff --git a/sdk/python/foundation-models/system/inference/zero-shot-image-classification/zero-shot-image-classification-batch-endpoint.ipynb b/sdk/python/foundation-models/system/inference/zero-shot-image-classification/zero-shot-image-classification-batch-endpoint.ipynb index 2d3c944a51e..47d3e37c72e 100644 --- a/sdk/python/foundation-models/system/inference/zero-shot-image-classification/zero-shot-image-classification-batch-endpoint.ipynb +++ b/sdk/python/foundation-models/system/inference/zero-shot-image-classification/zero-shot-image-classification-batch-endpoint.ipynb @@ -16,7 +16,7 @@ "Models that can perform the `zero-shot-image-classification` task are tagged with `zero-shot-image-classification`. We will use the `openai/clip-vit-base-patch32` model in this notebook. If you opened this notebook from a specific model card, remember to replace the specific model name. If you don't find a model that suits your scenario or domain, you can discover and [import models from HuggingFace hub](../../import/import_model_into_registry.ipynb) and then use them for inference. \n", "\n", "### Inference data\n", - "We will use the [fridgeObjects](https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/fridgeObjects.zip) dataset for image multi-class classification.\n", + "We will use the [fridgeObjects](https://automlsamplenotebookdata.blob.core.windows.net/image-classification/fridgeObjects.zip) dataset for image multi-class classification.\n", "\n", "\n", "### Outline\n", @@ -151,7 +151,7 @@ "source": [ "### 3. Prepare data for inference - Using a folder of csv files with base64 images\n", "\n", - "We will use the [fridgeObjects](https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/fridgeObjects.zip) dataset for multi-class classification. The fridge object dataset is stored in a directory. There are four different folders inside:\n", + "We will use the [fridgeObjects](https://automlsamplenotebookdata.blob.core.windows.net/image-classification/fridgeObjects.zip) dataset for multi-class classification. The fridge object dataset is stored in a directory. There are four different folders inside:\n", "- /water_bottle\n", "- /milk_bottle\n", "- /carton\n", @@ -177,7 +177,7 @@ "os.makedirs(dataset_parent_dir, exist_ok=True)\n", "\n", "# Download data\n", - "download_url = \"https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/fridgeObjects.zip\"\n", + "download_url = \"https://automlsamplenotebookdata.blob.core.windows.net/image-classification/fridgeObjects.zip\"\n", "\n", "# Extract current dataset name from dataset url\n", "dataset_name = os.path.split(download_url)[-1].split(\".\")[0]\n", diff --git a/sdk/python/foundation-models/system/inference/zero-shot-image-classification/zero-shot-image-classification-online-endpoint.ipynb b/sdk/python/foundation-models/system/inference/zero-shot-image-classification/zero-shot-image-classification-online-endpoint.ipynb index 685dbf7f698..dadeb2ab019 100644 --- a/sdk/python/foundation-models/system/inference/zero-shot-image-classification/zero-shot-image-classification-online-endpoint.ipynb +++ b/sdk/python/foundation-models/system/inference/zero-shot-image-classification/zero-shot-image-classification-online-endpoint.ipynb @@ -17,7 +17,7 @@ "Models that can perform the `zero-shot-image-classification` task are tagged with `zero-shot-image-classification`. We will use the `openai/clip-vit-base-patch32` model in this notebook. If you opened this notebook from a specific model card, remember to replace the specific model name. If you don't find a model that suits your scenario or domain, you can discover and [import models from HuggingFace hub](../../import/import_model_into_registry.ipynb) and then use them for inference.\n", "\n", "### Inference data\n", - "We will use the [fridgeObjects](https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/fridgeObjects.zip) dataset.\n", + "We will use the [fridgeObjects](https://automlsamplenotebookdata.blob.core.windows.net/image-classification/fridgeObjects.zip) dataset.\n", "\n", "\n", "### Outline\n", @@ -117,7 +117,7 @@ "source": [ "### 3. Prepare data for inference\n", "\n", - "We will use the [fridgeObjects](https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/fridgeObjects.zip) dataset for multi-class classification task. The fridge object dataset is stored in a directory. There are four different folders inside:\n", + "We will use the [fridgeObjects](https://automlsamplenotebookdata.blob.core.windows.net/image-classification/fridgeObjects.zip) dataset for multi-class classification task. The fridge object dataset is stored in a directory. There are four different folders inside:\n", "- /water_bottle\n", "- /milk_bottle\n", "- /carton\n", @@ -143,7 +143,7 @@ "os.makedirs(dataset_parent_dir, exist_ok=True)\n", "\n", "# Download data\n", - "download_url = \"https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/fridgeObjects.zip\"\n", + "download_url = \"https://automlsamplenotebookdata.blob.core.windows.net/image-classification/fridgeObjects.zip\"\n", "\n", "# Extract current dataset name from dataset url\n", "dataset_name = os.path.split(download_url)[-1].split(\".\")[0]\n", diff --git a/sdk/python/jobs/automl-standalone-jobs/automl-image-instance-segmentation-task-fridge-items/automl-image-instance-segmentation-task-fridge-items.ipynb b/sdk/python/jobs/automl-standalone-jobs/automl-image-instance-segmentation-task-fridge-items/automl-image-instance-segmentation-task-fridge-items.ipynb index e6b574c330d..1f58d184c98 100644 --- a/sdk/python/jobs/automl-standalone-jobs/automl-image-instance-segmentation-task-fridge-items/automl-image-instance-segmentation-task-fridge-items.ipynb +++ b/sdk/python/jobs/automl-standalone-jobs/automl-image-instance-segmentation-task-fridge-items/automl-image-instance-segmentation-task-fridge-items.ipynb @@ -143,7 +143,7 @@ "os.makedirs(dataset_parent_dir, exist_ok=True)\n", "\n", "# download data\n", - "download_url = \"https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjectsMask.zip\"\n", + "download_url = \"https://automlsamplenotebookdata.blob.core.windows.net/image-instance-segmentation/odFridgeObjectsMask.zip\"\n", "\n", "# Extract current dataset name from dataset url\n", "dataset_name = os.path.split(download_url)[-1].split(\".\")[0]\n", diff --git a/sdk/python/jobs/automl-standalone-jobs/automl-image-object-detection-task-fridge-items-batch-scoring/image-object-detection-batch-scoring-non-mlflow-model.ipynb b/sdk/python/jobs/automl-standalone-jobs/automl-image-object-detection-task-fridge-items-batch-scoring/image-object-detection-batch-scoring-non-mlflow-model.ipynb index 7b302a82f0b..d858ad71eef 100644 --- a/sdk/python/jobs/automl-standalone-jobs/automl-image-object-detection-task-fridge-items-batch-scoring/image-object-detection-batch-scoring-non-mlflow-model.ipynb +++ b/sdk/python/jobs/automl-standalone-jobs/automl-image-object-detection-task-fridge-items-batch-scoring/image-object-detection-batch-scoring-non-mlflow-model.ipynb @@ -128,7 +128,7 @@ "os.makedirs(dataset_parent_dir, exist_ok=True)\n", "\n", "# download data\n", - "download_url = \"https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip\"\n", + "download_url = \"https://automlsamplenotebookdata.blob.core.windows.net/image-object-detection/odFridgeObjects.zip\"\n", "\n", "# Extract current dataset name from dataset url\n", "dataset_name = os.path.split(download_url)[-1].split(\".\")[0]\n", diff --git a/sdk/python/jobs/automl-standalone-jobs/automl-image-object-detection-task-fridge-items/automl-image-object-detection-task-fridge-items.ipynb b/sdk/python/jobs/automl-standalone-jobs/automl-image-object-detection-task-fridge-items/automl-image-object-detection-task-fridge-items.ipynb index 8e63a5dbc8f..1edac2f751f 100644 --- a/sdk/python/jobs/automl-standalone-jobs/automl-image-object-detection-task-fridge-items/automl-image-object-detection-task-fridge-items.ipynb +++ b/sdk/python/jobs/automl-standalone-jobs/automl-image-object-detection-task-fridge-items/automl-image-object-detection-task-fridge-items.ipynb @@ -143,7 +143,7 @@ "os.makedirs(dataset_parent_dir, exist_ok=True)\n", "\n", "# download data\n", - "download_url = \"https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip\"\n", + "download_url = \"https://automlsamplenotebookdata.blob.core.windows.net/image-object-detection/odFridgeObjects.zip\"\n", "\n", "# Extract current dataset name from dataset url\n", "dataset_name = os.path.split(download_url)[-1].split(\".\")[0]\n", diff --git a/sdk/python/jobs/pipelines/1h_automl_in_pipeline/automl-image-classification-multiclass-in-pipeline/automl-image-classification-multiclass-in-pipeline.ipynb b/sdk/python/jobs/pipelines/1h_automl_in_pipeline/automl-image-classification-multiclass-in-pipeline/automl-image-classification-multiclass-in-pipeline.ipynb index 941bd595a42..71c043062bb 100644 --- a/sdk/python/jobs/pipelines/1h_automl_in_pipeline/automl-image-classification-multiclass-in-pipeline/automl-image-classification-multiclass-in-pipeline.ipynb +++ b/sdk/python/jobs/pipelines/1h_automl_in_pipeline/automl-image-classification-multiclass-in-pipeline/automl-image-classification-multiclass-in-pipeline.ipynb @@ -124,7 +124,7 @@ "os.makedirs(dataset_parent_dir, exist_ok=True)\n", "\n", "# download data\n", - "download_url = \"https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/fridgeObjects.zip\"\n", + "download_url = \"https://automlsamplenotebookdata.blob.core.windows.net/image-classification/fridgeObjects.zip\"\n", "\n", "# Extract current dataset name from dataset url\n", "dataset_name = os.path.split(download_url)[-1].split(\".\")[0]\n", diff --git a/sdk/python/jobs/pipelines/1h_automl_in_pipeline/automl-image-classification-multilabel-in-pipeline/automl-image-classification-multilabel-in-pipeline.ipynb b/sdk/python/jobs/pipelines/1h_automl_in_pipeline/automl-image-classification-multilabel-in-pipeline/automl-image-classification-multilabel-in-pipeline.ipynb index 8fcefb4f992..f611cead9e6 100644 --- a/sdk/python/jobs/pipelines/1h_automl_in_pipeline/automl-image-classification-multilabel-in-pipeline/automl-image-classification-multilabel-in-pipeline.ipynb +++ b/sdk/python/jobs/pipelines/1h_automl_in_pipeline/automl-image-classification-multilabel-in-pipeline/automl-image-classification-multilabel-in-pipeline.ipynb @@ -120,7 +120,7 @@ "os.makedirs(dataset_parent_dir, exist_ok=True)\n", "\n", "# download data\n", - "download_url = \"https://cvbp-secondary.z19.web.core.windows.net/datasets/image_classification/multilabelFridgeObjects.zip\"\n", + "download_url = \"https://automlsamplenotebookdata.blob.core.windows.net/image-classification/multilabelFridgeObjects.zip\"\n", "\n", "# Extract current dataset name from dataset url\n", "dataset_name = os.path.split(download_url)[-1].split(\".\")[0]\n", diff --git a/sdk/python/jobs/pipelines/1h_automl_in_pipeline/automl-image-instance-segmentation-in-pipeline/automl-image-instance-segmentation-in-pipeline.ipynb b/sdk/python/jobs/pipelines/1h_automl_in_pipeline/automl-image-instance-segmentation-in-pipeline/automl-image-instance-segmentation-in-pipeline.ipynb index 88ba780a009..198651e64ac 100644 --- a/sdk/python/jobs/pipelines/1h_automl_in_pipeline/automl-image-instance-segmentation-in-pipeline/automl-image-instance-segmentation-in-pipeline.ipynb +++ b/sdk/python/jobs/pipelines/1h_automl_in_pipeline/automl-image-instance-segmentation-in-pipeline/automl-image-instance-segmentation-in-pipeline.ipynb @@ -119,7 +119,7 @@ "os.makedirs(dataset_parent_dir, exist_ok=True)\n", "\n", "# download data\n", - "download_url = \"https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjectsMask.zip\"\n", + "download_url = \"https://automlsamplenotebookdata.blob.core.windows.net/image-instance-segmentation/odFridgeObjectsMask.zip\"\n", "\n", "# Extract current dataset name from dataset url\n", "dataset_name = os.path.split(download_url)[-1].split(\".\")[0]\n", diff --git a/sdk/python/jobs/pipelines/1h_automl_in_pipeline/automl-image-object-detection-in-pipeline/automl-image-object-detection-in-pipeline.ipynb b/sdk/python/jobs/pipelines/1h_automl_in_pipeline/automl-image-object-detection-in-pipeline/automl-image-object-detection-in-pipeline.ipynb index 6e188962fba..3e614abebe5 100644 --- a/sdk/python/jobs/pipelines/1h_automl_in_pipeline/automl-image-object-detection-in-pipeline/automl-image-object-detection-in-pipeline.ipynb +++ b/sdk/python/jobs/pipelines/1h_automl_in_pipeline/automl-image-object-detection-in-pipeline/automl-image-object-detection-in-pipeline.ipynb @@ -119,7 +119,7 @@ "os.makedirs(dataset_parent_dir, exist_ok=True)\n", "\n", "# download data\n", - "download_url = \"https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip\"\n", + "download_url = \"https://automlsamplenotebookdata.blob.core.windows.net/image-object-detection/odFridgeObjects.zip\"\n", "\n", "# Extract current dataset name from dataset url\n", "dataset_name = os.path.split(download_url)[-1].split(\".\")[0]\n",