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Azure · Nov 24, 2023 · 504c5bf · 504c5bf
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diff --git a/...m/finetune/image-instance-segmentation/mmdetection-fridgeobjects-instance-segmentation.sh b/...m/finetune/image-instance-segmentation/mmdetection-fridgeobjects-instance-segmentation.sh
@@ -137,7 +137,7 @@ fi
 # 5. Submit finetuning job using pipeline.yaml for a open-mmlab mmdetection model
 
 # If you want to use a MMDetection model, specify the inputs.model_name instead of inputs.mlflow_model_path.path like below
-# inputs.model_name="mask_rcnn_swin-t-p4-w7_fpn_1x_coco"
+# inputs.model_name="mask-rcnn_swin-t-p4-w7_fpn_1x_coco"
 
 mmdetection_parent_job_name=$( az ml job create \
   --file ./mmdetection-fridgeobjects-instance-segmentation-pipeline.yaml \

diff --git a/cli/foundation-models/system/finetune/image-instance-segmentation/readme.md b/cli/foundation-models/system/finetune/image-instance-segmentation/readme.md
@@ -7,5 +7,5 @@ For using this component for instance segmentation, run the shell script file `b
 Currently following models are supported:
 | Model Name | Source |
 | :------------: | :-------:  |
-| [mask_rcnn_swin-t-p4-w7_fpn_1x_coco](https://ml.azure.com/registries/azureml/models/mask_rcnn_swin-t-p4-w7_fpn_1x_coco/version/8) | azureml registry |
-| [Image instance-segmentation models from MMDetection](https://github.com/open-mmlab/mmdetection/blob/v2.28.2/docs/en/model_zoo.md) | MMDetection |
+| [mask-rcnn_swin-t-p4-w7_fpn_1x_coco](https://ml.azure.com/registries/azureml/models/mmd-3x-mask-rcnn_swin-t-p4-w7_fpn_1x_coco/version/8) | azureml registry |
+| [Image instance-segmentation models from MMDetection](https://github.com/open-mmlab/mmdetection/blob/v3.1.0/docs/en/model_zoo.md) | MMDetection |
diff --git a/cli/foundation-models/system/finetune/image-object-detection/readme.md b/cli/foundation-models/system/finetune/image-object-detection/readme.md
@@ -7,10 +7,10 @@ For using this component for object detection, run the shell script file `bash .
 Currently following models are supported:
 | Model Name | Source |
 | :------------: | :-------:  |
-| [deformable_detr_twostage_refine_r50_16x2_50e_coco](https://ml.azure.com/registries/azureml/models/deformable_detr_twostage_refine_r50_16x2_50e_coco/version/8) | azureml registry |
-| [sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco](https://ml.azure.com/registries/azureml/models/sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco/version/8) | azureml registry |
-| [sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco](https://ml.azure.com/registries/azureml/models/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco/version/8) | azureml registry |
-| [vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco](https://ml.azure.com/registries/azureml/models/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco/version/8) | azureml registry |
-| [vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco](https://ml.azure.com/registries/azureml/models/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco/version/8) | azureml registry |
-| [yolof_r50_c5_8x8_1x_coco](https://ml.azure.com/registries/azureml/models/yolof_r50_c5_8x8_1x_coco/version/8) | azureml registry |
-| [Image object detection models from MMDetection](https://github.com/open-mmlab/mmdetection/blob/v2.28.2/docs/en/model_zoo.md) | MMDetection |
+| [deformable-detr_refine_twostage_r50_16xb2-50e_coco](https://ml.azure.com/registries/azureml/models/mmd-3x-deformable-detr_refine_twostage_r50_16xb2-50e_coco/version/8) | azureml registry |
+| [sparse-rcnn_r50_fpn_300-proposals_crop-ms-480-800-3x_coco](https://ml.azure.com/registries/azureml/models/mmd-3x-sparse-rcnn_r50_fpn_300-proposals_crop-ms-480-800-3x_coco/version/8) | azureml registry |
+| [sparse-rcnn_r101_fpn_300-proposals_crop-ms-480-800-3x_coco](https://ml.azure.com/registries/azureml/models/mmd-3x-sparse-rcnn_r101_fpn_300-proposals_crop-ms-480-800-3x_coco/version/8) | azureml registry |
+| [vfnet_r50-mdconv-c3-c5_fpn_ms-2x_coco](https://ml.azure.com/registries/azureml/models/mmd-3x-vfnet_r50-mdconv-c3-c5_fpn_ms-2x_coco/version/8) | azureml registry |
+| [vfnet_x101-64x4d-mdconv-c3-c5_fpn_ms-2x_coco](https://ml.azure.com/registries/azureml/models/mmd-3x-vfnet_x101-64x4d-mdconv-c3-c5_fpn_ms-2x_coco/version/8) | azureml registry |
+| [yolof_r50_c5_8x8_1x_coco](https://ml.azure.com/registries/azureml/models/mmd-3x-yolof_r50_c5_8x8_1x_coco/version/8) | azureml registry |
+| [Image object detection models from MMDetection](https://github.com/open-mmlab/mmdetection/blob/v3.1.0/docs/en/model_zoo.md) | MMDetection |
diff --git a/...-models/system/docs/component_docs/image_finetune/mmd_model_import_component.md b/...-models/system/docs/component_docs/image_finetune/mmd_model_import_component.md
@@ -1,5 +1,5 @@
 # MMDetection Model Import Component
-The component copies the input model folder to the component output directory when the model is passed as an input to the `pytorch_model` or `mlflow_model` nodes. If `model_name `is selected, the component will download the config for the model from [MMDetection model zoo](https://github.com/open-mmlab/mmdetection/blob/v2.28.2/docs/en/model_zoo.md). The component can be seen in your workspace component page - [mmdetection_image_objectdetection_instancesegmentation_model_import](https://ml.azure.com/registries/azureml/components/mmdetection_image_objectdetection_instancesegmentation_model_import).
+The component copies the input model folder to the component output directory when the model is passed as an input to the `pytorch_model` or `mlflow_model` nodes. If `model_name `is selected, the component will download the config for the model from [MMDetection model zoo](https://github.com/open-mmlab/mmdetection/blob/v3.1.0/docs/en/model_zoo.md). The component can be seen in your workspace component page - [mmdetection_image_objectdetection_instancesegmentation_model_import](https://ml.azure.com/registries/azureml/components/mmdetection_image_objectdetection_instancesegmentation_model_import).
 
 # 1. Inputs
 
@@ -16,13 +16,13 @@ The component copies the input model folder to the component output directory wh
     Following models are registered in azureml registry, and can be used directly.
     | Model Name | Source |
     | :------------: | :-------:  |
-    | [deformable_detr_twostage_refine_r50_16x2_50e_coco](https://ml.azure.com/registries/azureml/models/deformable_detr_twostage_refine_r50_16x2_50e_coco/version/3) | azureml registry |
-    | [sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco](https://ml.azure.com/registries/azureml/models/sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco/version/3) | azureml registry |
-    | [sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco](https://ml.azure.com/registries/azureml/models/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco/version/3) | azureml registry |
-    | [vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco](https://ml.azure.com/registries/azureml/models/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco/version/3) | azureml registry |
-    | [vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco](https://ml.azure.com/registries/azureml/models/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco/version/3) | azureml registry |
-    | [yolof_r50_c5_8x8_1x_coco](https://ml.azure.com/registries/azureml/models/yolof_r50_c5_8x8_1x_coco/version/3) | azureml registry |
-    | [mask_rcnn_swin-t-p4-w7_fpn_1x_coco](https://ml.azure.com/registries/azureml/models/mask_rcnn_swin-t-p4-w7_fpn_1x_coco/version/3) | azureml registry |
+    | [deformable-detr_refine_twostage_r50_16xb2-50e_coco](https://ml.azure.com/registries/azureml/models/mmd-3x-deformable-detr_refine_twostage_r50_16xb2-50e_coco/version/8) | azureml registry |
+    | [sparse-rcnn_r50_fpn_300-proposals_crop-ms-480-800-3x_coco-800_3x_coco](https://ml.azure.com/registries/azureml/models/mmd-3x-sparse-rcnn_r50_fpn_300-proposals_crop-ms-480-800-3x_coco-800_3x_coco/version/8) | azureml registry |
+    | [sparse-rcnn_r101_fpn_300-proposals_crop-ms-480-800-3x_coco-800_3x_coco](https://ml.azure.com/registries/azureml/models/mmd-3x-sparse-rcnn_r101_fpn_300-proposals_crop-ms-480-800-3x_coco-800_3x_coco/version/8) | azureml registry |
+    | [vfnet_r50-mdconv-c3-c5_fpn_ms-2x_coco](https://ml.azure.com/registries/azureml/models/mmd-3x-vfnet_r50-mdconv-c3-c5_fpn_ms-2x_coco/version/8) | azureml registry |
+    | [vfnet_x101-64x4d-mdconv-c3-c5_fpn_ms-2x_coco](https://ml.azure.com/registries/azureml/models/mmd-3x-vfnet_x101-64x4d-mdconv-c3-c5_fpn_ms-2x_coco/version/8) | azureml registry |
+    | [yolof_r50_c5_8x8_1x_coco](https://ml.azure.com/registries/azureml/models/mmd-3x-yolof_r50_c5_8x8_1x_coco/version/8) | azureml registry |
+    | [mmd-3x-mask-rcnn_swin-t-p4-w7_fpn_1x_coco](https://ml.azure.com/registries/azureml/models/mmd-3x-mask-rcnn_swin-t-p4-w7_fpn_1x_coco/version/8) | azureml registry |
 
     Below is the folder structure of a registered MLFlow model.
 
@@ -52,7 +52,7 @@ The component copies the input model folder to the component output directory wh
 
 2. _model_name_ (string, optional)
 
-    Please select models from AzureML Model Assets for all supported models. For MMDetection models, which are not supported in AzureML model registry, the model's config name is required, same as it's specified in MMDetection Model Zoo. For e.g. fast_rcnn_r101_fpn_1x_coco for [this config file](https://github.com/open-mmlab/mmdetection/blob/master/configs/fast_rcnn/fast_rcnn_r101_fpn_1x_coco.py). You can see the comprehensive list of model configs [here](https://github.com/open-mmlab/mmdetection/tree/v2.28.2/configs) and the documentation of model zoo [here](https://github.com/open-mmlab/mmdetection/blob/v2.28.2/docs/en/model_zoo.md).
+    Please select models from AzureML Model Assets for all supported models. For MMDetection models, which are not supported in AzureML model registry, the model's config name is required, same as it's specified in MMDetection Model Zoo. For e.g. fast_rcnn_r101_fpn_1x_coco for [this config file](https://github.com/open-mmlab/mmdetection/blob/master/configs/fast_rcnn/fast_rcnn_r101_fpn_1x_coco.py). You can see the comprehensive list of model configs [here](https://github.com/open-mmlab/mmdetection/tree/v3.1.0/configs) and the documentation of model zoo [here](https://github.com/open-mmlab/mmdetection/blob/v3.1.0/docs/en/model_zoo.md).
     Please note that it is the user responsibility to comply with the model's license terms.
 
 3. __download_from_source__ (boolean, optional)

diff --git a/...inetune/image-instance-segmentation/mmdetection-fridgeobjects-instance-segmentation.ipynb b/...inetune/image-instance-segmentation/mmdetection-fridgeobjects-instance-segmentation.ipynb
@@ -247,7 +247,7 @@
    "source": [
     "### 3. Pick a foundation model to fine tune\n",
     "\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",
+    "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",
     "\n",
     "Currently following models are supported:\n",
     "\n",
@@ -263,7 +263,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "mmdetection_model_name = \"mask_rcnn_swin-t-p4-w7_fpn_1x_coco\"\n",
+    "mmdetection_model_name = \"mask-rcnn_swin-t-p4-w7_fpn_1x_coco\"\n",
     "\n",
     "aml_registry_model_name = \"mmd-3x-mask-rcnn_swin-t-p4-w7_fpn_1x_coco\"\n",
     "foundation_models = registry_ml_client.models.list(name=aml_registry_model_name)\n",

diff --git a/...segmentation-task-fridge-items/automl-image-instance-segmentation-task-fridge-items.ipynb b/...segmentation-task-fridge-items/automl-image-instance-segmentation-task-fridge-items.ipynb
@@ -774,7 +774,7 @@
    "source": [
     "### 4.2.1 Individual runs with models from MMDetection (Preview)\n",
     "\n",
-    "In addition to the models supported natively by AutoML, you can launch individual runs to explore any model from MMDetection version 2.28.2 that supports instance segmentation. Please refer to this [documentation](https://github.com/open-mmlab/mmdetection/blob/v2.28.2/docs/en/model_zoo.md) for the list of models.\n",
+    "In addition to the models supported natively by AutoML, you can launch individual runs to explore any model from MMDetection version 3.1.0 that supports instance segmentation. Please refer to this [documentation](https://github.com/open-mmlab/mmdetection/blob/v3.1.0/docs/en/model_zoo.md) for the list of models.\n",
     "\n",
     "While you can use any model from MMDetection to support this task, we have curated a set of models in our registry. We provide a set of sensible default hyperparameters for these models. You can fetch the list of curated models using code snippet below."
    ]
@@ -812,7 +812,7 @@
     ]
    },
    "source": [
-    "If you wish to try a model (say `mask_rcnn_swin-t-p4-w7_fpn_1x_coco`), you can specify the job for your AutoML Image runs as follows:"
+    "If you wish to try a model (say `mask-rcnn_swin-t-p4-w7_fpn_1x_coco`), you can specify the job for your AutoML Image runs as follows:"
    ]
   },
   {
@@ -838,7 +838,7 @@
     "image_instance_segmentation_job.set_limits(timeout_minutes=60)\n",
     "\n",
     "image_instance_segmentation_job.set_training_parameters(\n",
-    "    model_name=\"mask_rcnn_swin-t-p4-w7_fpn_1x_coco\"\n",
+    "    model_name=\"mask-rcnn_swin-t-p4-w7_fpn_1x_coco\"\n",
     ")"
    ]
   },
@@ -1018,9 +1018,9 @@
    "source": [
     "### 4.3.1 Manual hyperparameter sweeping for models from MMDetection (Preview)\n",
     "\n",
-    "Similar to how you can use any model from MMDetection version 2.28.2 for individual runs, you can also include these models to perform a hyperparameter sweep. You can also choose a combination of models supported natively by [AutoML](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models?tabs=CLI-v2#configure-model-algorithms-and-hyperparameters) and models from [MMDetection](https://github.com/open-mmlab/mmdetection/blob/v2.28.2/docs/en/model_zoo.md).\n",
+    "Similar to how you can use any model from MMDetection version 3.1.0 for individual runs, you can also include these models to perform a hyperparameter sweep. You can also choose a combination of models supported natively by [AutoML](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models?tabs=CLI-v2#configure-model-algorithms-and-hyperparameters) and models from [MMDetection](https://github.com/open-mmlab/mmdetection/blob/v3.1.0/docs/en/model_zoo.md).\n",
     "\n",
-    "In this example, we sweep over `mask_rcnn_swin-t-p4-w7_fpn_1x_coco`, and `maskrcnn_resnet50_fpn`, models choosing from a range of values for learning_rate, min_size, etc., to generate a model with the optimal 'MeanAveragePrecision'.."
+    "In this example, we sweep over `mask-rcnn_swin-t-p4-w7_fpn_1x_coco`, and `maskrcnn_resnet50_fpn`, models choosing from a range of values for learning_rate, min_size, etc., to generate a model with the optimal 'MeanAveragePrecision'.."
    ]
   },
   {
@@ -1060,7 +1060,7 @@
     "            min_size=Choice([600, 800]),\n",
     "        ),\n",
     "        SearchSpace(\n",
-    "            model_name=Choice([\"mask_rcnn_swin-t-p4-w7_fpn_1x_coco\"]),\n",
+    "            model_name=Choice([\"mask-rcnn_swin-t-p4-w7_fpn_1x_coco\"]),\n",
     "            learning_rate=Uniform(0.00001, 0.0001),\n",
     "            number_of_epochs=Choice([10, 15]),\n",
     "        ),\n",

diff --git a/...-object-detection-task-fridge-items/automl-image-object-detection-task-fridge-items.ipynb b/...-object-detection-task-fridge-items/automl-image-object-detection-task-fridge-items.ipynb
@@ -735,7 +735,7 @@
    "source": [
     "### 4.2.1 Individual runs with models from MMDetection (Preview)\n",
     "\n",
-    "In addition to the models supported natively by AutoML, you can launch individual runs to explore any model from MMDetection version 2.28.2 that supports object detection. Please refer to this [documentation](https://github.com/open-mmlab/mmdetection/blob/v2.28.2/docs/en/model_zoo.md) for the list of models.\n",
+    "In addition to the models supported natively by AutoML, you can launch individual runs to explore any model from MMDetection version 3.1.0 that supports object detection. Please refer to this [documentation](https://github.com/open-mmlab/mmdetection/blob/v3.1.0/docs/en/model_zoo.md) for the list of models.\n",
     "\n",
     "While you can use any model from MMDetection to support this task, we have curated a set of models in our registry. We provide a set of sensible default hyperparameters for these models. You can fetch the list of curated models using code snippet below."
    ]
@@ -773,7 +773,7 @@
     ]
    },
    "source": [
-    "If you wish to try a model (say `vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco`), you can specify the job for your AutoML Image runs as follows:"
+    "If you wish to try a model (say `vfnet_r50-mdconv-c3-c5_fpn_ms-2x_coco`), you can specify the job for your AutoML Image runs as follows:"
    ]
   },
   {
@@ -801,7 +801,7 @@
     "\n",
     "# Pass the fixed settings or parameters\n",
     "image_object_detection_job.set_training_parameters(\n",
-    "    model_name=\"vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco\"\n",
+    "    model_name=\"vfnet_r50-mdconv-c3-c5_fpn_ms-2x_coco\"\n",
     ")"
    ]
   },
@@ -1032,9 +1032,9 @@
    "source": [
     "### 4.3.1 Manual hyperparameter sweeping for models from MMDetection (Preview)\n",
     "\n",
-    "Similar to how you can use any model from MMDetection version 2.28.2 for individual runs, you can also include these models to perform a hyperparameter sweep. You can also choose a combination of models supported natively by [AutoML](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models?tabs=CLI-v2#configure-model-algorithms-and-hyperparameters) and models from [MMDetection](https://github.com/open-mmlab/mmdetection/blob/v2.28.2/docs/en/model_zoo.md).\n",
+    "Similar to how you can use any model from MMDetection version 3.1.0 for individual runs, you can also include these models to perform a hyperparameter sweep. You can also choose a combination of models supported natively by [AutoML](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models?tabs=CLI-v2#configure-model-algorithms-and-hyperparameters) and models from [MMDetection](https://github.com/open-mmlab/mmdetection/blob/v3.1.0/docs/en/model_zoo.md).\n",
     "\n",
-    "In this example, we sweep over `deformable_detr_twostage_refine_r50_16x2_50e_coco`, `sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco`, and `yolov5`, models choosing from a range of values for learning_rate, model_size, etc., to generate a model with the optimal 'MeanAveragePrecision'.."
+    "In this example, we sweep over `deformable-detr_refine_twostage_r50_16xb2-50e_coco`, `sparse-rcnn_r50_fpn_300-proposals_crop-ms-480-800-3x_coco`, and `yolov5`, models choosing from a range of values for learning_rate, model_size, etc., to generate a model with the optimal 'MeanAveragePrecision'.."
    ]
   },
   {
@@ -1085,8 +1085,8 @@
     "        SearchSpace(\n",
     "            model_name=Choice(\n",
     "                [\n",
-    "                    \"deformable_detr_twostage_refine_r50_16x2_50e_coco\",\n",
-    "                    \"sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco\",\n",
+    "                    \"deformable-detr_refine_twostage_r50_16xb2-50e_coco\",\n",
+    "                    \"sparse-rcnn_r50_fpn_300-proposals_crop-ms-480-800-3x_coco\",\n",
     "                ]\n",
     "            ),\n",
     "            learning_rate=Uniform(0.00001, 0.0001),\n",