Doc related changes (#2536)

* updates for sdk/cli inference examples * deploying from azureml-staging erroring out * updates to sdk, cli examples * updating score col names while fetching batch scoring output * updating score col names while fetching batch scoring output for classification * fixing bash issues
Azure · Aug 4, 2023 · 94f72c4 · 94f72c4
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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
@@ -154,7 +154,7 @@ az ml online-deployment create --file ./deploy.yaml $workspace_info --all-traffi
     echo "deployment create failed"; exit 1;
 }
 
-# 8. Try a sample scoring request on the deployed MMDetection Transformers model
+# 8. Try a sample scoring request on the deployed MMDetection model
 
 # Check if scoring data file exists
 if [ -f $mmdetection_sample_request_data ] 

diff --git a/...tion-models/system/finetune/image-object-detection/mmdetection-fridgeobjects-detection.sh b/...tion-models/system/finetune/image-object-detection/mmdetection-fridgeobjects-detection.sh
@@ -153,7 +153,7 @@ az ml online-deployment create --file ./deploy.yaml $workspace_info --all-traffi
     echo "deployment create failed"; exit 1;
 }
 
-# 8. Try a sample scoring request on the deployed MMDetection Transformers model
+# 8. Try a sample scoring request on the deployed MMDetection model
 
 # Check if scoring data file exists
 if [ -f $mmdetection_sample_request_data ] 

diff --git a/...inetune/image-instance-segmentation/mmdetection-fridgeobjects-instance-segmentation.ipynb b/...inetune/image-instance-segmentation/mmdetection-fridgeobjects-instance-segmentation.ipynb
@@ -5,7 +5,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Image instance-segmentation using transformers specific pipeline component\n",
+    "## Image instance-segmentation using MMDetection specific pipeline component\n",
     "\n",
     "This sample shows how to use `mmdetection_image_objectdetection_instancesegmentation_pipeline` component from the `azureml-staging` 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",
@@ -20,7 +20,7 @@
     "2. Setup pre-requisites such as compute\n",
     "3. Pick a model to fine tune\n",
     "4. Prepare dataset for finetuning the model\n",
-    "5. Submit the fine tuning job using transformers specific image instance-segmentation and instance-segmentation component\n",
+    "5. Submit the fine tuning job using MMDetection specific image instance-segmentation and instance-segmentation component\n",
     "6. Review training and evaluation metrics\n",
     "7. Register the fine tuned model\n",
     "8. Deploy the fine tuned model for real time inference\n",
@@ -467,8 +467,8 @@
     "#### 5.2 Create arguments to be passed to `mmdetection_image_objectdetection_instancesegmentation_pipeline` component\n",
     "\n",
     "The `mmdetection_image_objectdetection_instancesegmentation_pipeline` component consists of model selection and finetuning components. The detailed arguments for each component can be found at following README files:\n",
-    "- [Model Import Component](../../../docs/component_docs/image_finetune/mmd_model_import_component.md)\n",
-    "- [Finetune Component](../../../docs/component_docs/image_finetune/mmd_finetune_component.md)"
+    "- [Model Import Component](../../docs/component_docs/image_finetune/mmd_model_import_component.md)\n",
+    "- [Finetune Component](../../docs/component_docs/image_finetune/mmd_finetune_component.md)"
    ]
   },
   {
@@ -568,7 +568,7 @@
     "\n",
     "\n",
     "@pipeline()\n",
-    "def create_pipeline_transformers():\n",
+    "def create_pipeline_mmdetection():\n",
     "    \"\"\"Create pipeline.\"\"\"\n",
     "\n",
     "    mmdetection_pipeline_component: PipelineComponent = pipeline_component_mmdetection_func(\n",
@@ -600,7 +600,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "mmdetection_pipeline_object = create_pipeline_transformers()\n",
+    "mmdetection_pipeline_object = create_pipeline_mmdetection()\n",
     "\n",
     "mmdetection_pipeline_object.display_name = (\n",
     "    use_model_name + \"_mmdetection_pipeline_component_run_\" + \"is\"\n",

diff --git a/...s/system/finetune/image-object-detection/mmdetection-fridgeobjects-object-detection.ipynb b/...s/system/finetune/image-object-detection/mmdetection-fridgeobjects-object-detection.ipynb
@@ -5,22 +5,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Image object-detection using transformers specific pipeline component\n",
+    "## Image object-detection using MMDetection specific pipeline component\n",
     "\n",
     "This sample shows how to use `mmdetection_image_objectdetection_instancesegmentation_pipeline` component from the `azureml-staging` 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",
     "\n",
     "### Model\n",
-    "We will use the `vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_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-staging` 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 `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-staging` 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",
     "### Outline\n",
     "1. Install dependencies\n",
     "2. Setup pre-requisites such as compute\n",
     "3. Pick a model to fine tune\n",
     "4. Prepare dataset for finetuning the model\n",
-    "5. Submit the fine tuning job using transformers specific image object-detection and instance-segmentation component\n",
+    "5. Submit the fine tuning job using MMDetection specific image object-detection and instance-segmentation component\n",
     "6. Review training and evaluation metrics\n",
     "7. Register the fine tuned model\n",
     "8. Deploy the fine tuned model for real time inference\n",
@@ -710,7 +710,7 @@
     "\n",
     "\n",
     "@pipeline()\n",
-    "def create_pipeline_transformers():\n",
+    "def create_pipeline_mmdetection():\n",
     "    \"\"\"Create pipeline.\"\"\"\n",
     "\n",
     "    mmdetection_pipeline_component: PipelineComponent = pipeline_component_mmdetection_func(\n",
@@ -742,7 +742,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "mmdetection_pipeline_object = create_pipeline_transformers()\n",
+    "mmdetection_pipeline_object = create_pipeline_mmdetection()\n",
     "\n",
     "mmdetection_pipeline_object.display_name = (\n",
     "    use_model_name + \"_mmdetection_pipeline_component_run_\" + \"od\"\n",