DOCS-2056: Add vision service control panel

docs/services/vision/mlmodel.md

@@ -200,7 +200,7 @@ When done, click the **Save** button in the top right corner of the page, then p
 ## Test your detector or classifier
 
 You can test your detector or classifier with [existing images in the Viam app](#existing-images-in-the-cloud) or [live camera footage](#live-camera-footage).
-You can also test classifiers with [existing images on a computer](#existing-images-on-your-machine).
+You can also test detectors and classifiers with [existing images on a computer](#existing-images-on-your-machine).
 
 ### Existing images in the cloud
 
@@ -226,66 +226,19 @@ If the classifier's results exceed the confidence threshold, the **Run model** s
 If you intend to use the detector or classifier with a camera that is part of your machine, you can test your detector or classifier from the [**Control tab**](/fleet/control/) or with code:
 
 1. Configure a [camera component](/components/camera/).
-
    {{< alert title="Tip" color="tip" >}}
    This is the camera whose name you need to pass to vision service methods.
    {{< /alert >}}
 
-2. (Optional) If you would like to see detections or classifications from the **Control tab**, configure a [transform camera](/components/camera/transform/) with the following attributes:
-
-{{< tabs >}}
-{{% tab name="Detections" %}}
-
-```json
-{
-  "pipeline": [
-    {
-      "type": "detections",
-      "attributes": {
-        "confidence_threshold": 0.5,
-        "detector_name": "<vision-service-name>",
-        "valid_labels": ["<label>"]
-      }
-    }
-  ],
-  "source": "<camera-name>"
-}
-```
-
-{{% /tab %}}
-{{% tab name="Classifications" %}}
-
-```json
-{
-  "pipeline": [
-    {
-      "type": "classifications",
-      "attributes": {
-        "confidence_threshold": 0.5,
-        "classifier_name": "<vision-service-name>",
-        "max_classifications": <int>,
-        "valid_labels": [ "<label>" ]
-      }
-    }
-  ],
-  "source": "<camera-name>"
-}
-```
-
-{{% /tab %}}
-{{< /tabs >}}
-
-3. After adding the components and their attributes, click the **Save** button in the top right corner of the page.
-4. Navigate to the **CONTROL** tab, click on your transform camera and toggle it on.
-   If you've configured a detector, the transform camera will now show detections with bounding boxes around the object.
+2. After adding the camera, click the **Save** button in the top right corner of the page.
+3. Navigate to the **CONTROL** tab, click on the vision service and select your camera and vision service and then click **Refresh**.
+   The panel will now show detections with bounding boxes around the colors or classifications on the image.
 
    ![Viam app control tab interface showing bounding boxes around two office chairs, both labeled "chair" with confidence score "0.50."](/services/vision/chair-detector.png)
 
-   If you've configured a classifier, the transform camera will now show classifications on the image.
-
    ![Model recognizes a star on camera feed](/services/model-on-camera.png)
 
-5. The following code gets the machine’s vision service and then runs a detector or classifier vision model on an image from the machine's camera `"cam1"`.
+4. The following code gets the machine’s vision service and then runs a detector or classifier vision model on an image from the machine's camera `"cam1"`.
 
    {{% alert title="Tip" color="tip" %}}