Update bill:effdet0 to EfficientDet-COCO

docs/tutorials/projects/guardian.md

@@ -245,7 +245,7 @@ Then test the components on the [machine's Control tab](/fleet/machines/#control
 
 ## Detect persons and pets
 
-For the guardian to be able to detect living beings, you will use a Machine Learning model from the Viam Registry called [`effdet0`](https://app.viam.com/ml-model/bill/effdet0).
+For the guardian to be able to detect living beings, you will use a machine learning model from the Viam registry called [`EfficientDet-COCO`](https://app.viam.com/ml-model/viam-labs/EfficientDet-COCO).
 The model can detect a variety of things which you can see in <file>[labels.txt](https://github.com/viam-labs/devrel-demos/raw/main/Light%20up%20bot/labels.txt)</file> file.
 
 You can also [train your own custom model](/ml/train-model/) based on images from your robot but the provided Machine Learning model is a good one to start with.
@@ -265,7 +265,7 @@ Select type `ML Model`, then select model `TFLite CPU`.
 Enter `mlmodel` as the name for your ML model service, then click **Create**.
 
 Select the **Deploy model on robot** for the **Deployment** field.
-Then select the `bill:effdet0` model from the **Models** dropdown.
+Then select the `viam-labs:EfficientDet-COCO` model from the **Models** dropdown.
 
 2. **Add a vision service.**
 

docs/tutorials/projects/integrating-viam-with-openai.md

@@ -233,7 +233,7 @@ We found that if set up this way, the following positions accurately show the co
 ### 2. Configure the ML Model and vision services to use the detector
 
 The [ML model service](/ml/) allows you to deploy a machine learning model to your robot.
-This tutorial uses a pre-trained machine learning (ML) model from the Viam registry named [`effdet0`](https://app.viam.com/ml-model/bill/effdet0).
+This tutorial uses a pre-trained machine learning (ML) model from the Viam registry named [`EfficientDet-COCO`](https://app.viam.com/ml-model/viam-labs/EfficientDet-COCO).
 This model can detect a variety of objects, which you can find in the provided <file>[labels.txt](https://github.com/viam-labs/devrel-demos/raw/main/Light%20up%20bot/labels.txt)</file> file.
 
 Click the **Config** tab and then the **Services** subtab.
@@ -243,7 +243,7 @@ Your robot will register this as a machine learning model and make it available
 {{<imgproc src="/tutorials/ai-integration/mlmodels_service_add.png" resize="500x" declaredimensions=true alt="Adding the ML Models Service." style="border:1px solid #000">}}
 
 Select the **Deploy model on robot** for the **Deployment** field.
-Then select the `bill:effdet0` model from the **Models** dropdown.
+Then select the `viam-labs:EfficientDet-COCO` model from the **Models** dropdown.
 
 Now, create a new service of **type** `vision`, **model** `ML Model` named 'vis-stuff-detector'.
 Your companion robot will use this to interface with the machine learning model allowing you to - well, detect stuff!

docs/tutorials/projects/light-up.md

@@ -78,7 +78,7 @@ Navigate to the **Control** tab where you can see your camera working.
 
 ## Configure your services
 
-This tutorial uses a pre-trained machine learning (ML) model from the Viam registry named [`effdet0`](https://app.viam.com/ml-model/bill/effdet0).
+This tutorial uses a pre-trained machine learning (ML) model from the Viam registry named [`EfficientDet-COCO`](https://app.viam.com/ml-model/viam-labs/EfficientDet-COCO).
 This model can detect a variety of objects, which you can find in the provided <file>[labels.txt](https://github.com/viam-labs/devrel-demos/raw/main/Light%20up%20bot/labels.txt)</file> file.
 
 If you want to train your own model instead, follow the instructions to [train a model](/ml/train-model/).
@@ -98,7 +98,7 @@ In the new ML Model service panel, configure your service.
 ![mlmodel service panel with empty sections for Model Path, and Optional Settings such as Label Path and Number of threads.](/tutorials/tipsy/app-service-ml-before.png)
 
 Select the **Deploy model on robot** for the **Deployment** field.
-Then select the `bill:effdet0` model from the **Models** dropdown.
+Then select the `viam-labs:EfficientDet-COCO` model from the **Models** dropdown.
 
 ### Configure an mlmodel detector
 

docs/tutorials/projects/send-security-photo.md

@@ -89,7 +89,7 @@ Navigate to the **Control** tab where you can see your camera working.
 
 ### Configure your services
 
-This tutorial uses a pre-trained Machine Learning model from the Viam Registry called [`effdet0`](https://app.viam.com/ml-model/bill/effdet0).
+This tutorial uses a pre-trained Machine Learning model from the Viam Registry called [`EfficientDet-COCO`](https://app.viam.com/ml-model/viam-labs/EfficientDet-COCO).
 The model can detect a variety of things, including `Persons`.
 You can see a full list of what the model can detect in <file>[labels.txt](https://github.com/viam-labs/devrel-demos/raw/main/Light%20up%20bot/labels.txt)</file> file.
 
@@ -111,7 +111,7 @@ Click the **Services** subtab.
    ![mlmodel service panel with empty sections for Model Path, and Optional Settings such as Label Path and Number of threads.](/tutorials/tipsy/app-service-ml-before.png)
 
    Select the **Deploy model on robot** for the **Deployment** field.
-   Then select the `bill:effdet0` model from the **Models** dropdown.
+   Then select the `viam-labs:EfficientDet-COCO` model from the **Models** dropdown.
 
 1. **Configure an mlmodel detector**
 

docs/tutorials/projects/tipsy.md

@@ -306,7 +306,7 @@ On the control tab, you will see panels for each of your configured components.
 
 ## Configure your services
 
-This tutorial uses a pre-trained machine learning (ML) model from the Viam registry named [`effdet0`](https://app.viam.com/ml-model/bill/effdet0).
+This tutorial uses a pre-trained machine learning (ML) model from the Viam registry named [`EfficientDet-COCO`](https://app.viam.com/ml-model/viam-labs/EfficientDet-COCO).
 This model can detect a variety of objects, which you can find in the provided <file>[labels.txt](https://github.com/viam-labs/devrel-demos/raw/main/Light%20up%20bot/labels.txt)</file> file.
 
 If you want to train your own model instead, follow the instructions to [train a model](/ml/train-model/).
@@ -330,7 +330,7 @@ Click on the **Services** subtab.
    ![mlmodel service panel with empty sections for Model Path, and Optional Settings such as Label Path and Number of threads.](/tutorials/tipsy/app-service-ml-before.png)
 
    Select the **Deploy model on robot** for the **Deployment** field.
-   Then select the `bill:effdet0` model from the **Models** dropdown.
+   Then select the `viam-labs:EfficientDet-COCO` model from the **Models** dropdown.
 
 1. **Configure an ML model detector**
 

docs/tutorials/projects/verification-system.md

@@ -82,15 +82,15 @@ In order for your machine's camera to be able to detect the presence of a person
 ### Use an existing ML model
 
 The [ML model service](/ml/) allows you to deploy a machine learning model to your robot.
-For your machine to be able to detect people, you will use a Machine Learning model from the Viam registry called [`effdet0`](https://app.viam.com/ml-model/bill/effdet0).
+For your machine to be able to detect people, you will use a Machine Learning model from the Viam registry called [`EfficientDet-COCO`](https://app.viam.com/ml-model/viam-labs/EfficientDet-COCO).
 The model can detect a variety of things which you can see in <file>[labels.txt](https://github.com/viam-labs/devrel-demos/raw/main/Light%20up%20bot/labels.txt)</file> file including `person`s.
 
 1. Navigate to your machine's **Config** tab on the [Viam app](https://app.viam.com/Machines).
 2. Click **Create service** in the lower-left corner of the page.
 3. Select type `ML Model`, then select model `TFLite CPU`.
 4. Enter `persondetect` as the name for your ML model service, then click **Create**.
 5. Select the **Deploy model on robot** for the **Deployment** field.
-6. Then select the `bill:effdet0` model from the **Models** dropdown.
+6. Then select the `viam-labs:EfficientDet-COCO` model from the **Models** dropdown.
 
 Finally, configure an `mlmodel` detector vision service to use your new `"persondetect"` ML model: