# Real-Time Object Detection on a Raspberry Pi

Turn a Raspberry Pi and USB webcam into a real-time object detector — three config steps, no custom code.

## What you'll build

A live object detection pipeline running on a Raspberry Pi. The EfficientDet-COCO model (trained on 80 everyday object classes) will draw labeled bounding boxes around whatever your webcam sees — keyboards, cups, scissors, bowls, and more — directly in the Viam app's test panel.

## What you'll need

**Hardware**  
- Raspberry Pi 4 or 5  
- USB webcam

**Software / Accounts**  
- [Viam account](https://app.viam.com/)  
- viam-server installed and running on your Pi

## Before you begin: Set up your Pi

Create a machine in [app.viam.com](https://app.viam.com/), install viam-server on your Raspberry Pi, and add the **raspberry-pi:rpi4** or **raspberry-pi:rpi5** board component (matching your Pi model) so Viam can communicate with your hardware. Follow the [Viam setup guide](https://docs.viam.com/operate/install/setup/) to get your machine connected and the board component configured before continuing.

## Step 1: Add a webcam

Plug your USB webcam into the Raspberry Pi. Open your machine's **Configure** tab in [app.viam.com](https://app.viam.com/) and click **+** to open the Configuration Blocks panel.

Search for **webcam** and select **camera/webcam** — this component is built into viam-server, so no extra module is needed.

Name the component **camera** and click **Add component**. The webcam component finds your video path automatically; if your Pi has a single USB webcam attached, no further attributes are required.

_Don't forget to save!_

## Step 2: Add an ML model service and choose an ML model

Click **+** again. Search for **tfl** and select **tflite_cpu/tflite_cpu**.

Name the service **mlmodel** and click **Add component**. Viam will automatically install the one supporting module this service needs.

After adding, click **Select a model** and switch to the **Registry** tab to browse community and open-source models — or use your own custom model. Select **viam-labs:EfficientDet-COCO** with version **Latest**.

_Don't forget to save!_

## Step 3: Implement your ML model in a vision service

Click **+** one more time. Search for **vision/mlmodel** and add it with the name **object-detector**.

In the component's configuration panel, set:
- **ML Model** → mlmodel  
- **Default Camera** → camera  
- **Minimum confidence threshold** → 0.50 (a.k.a. 50% confidence — the model only draws a bounding box when it's at least this confident)

_Don't forget to save!_

## See it in action

Open the **TEST** section of the object-detector component. Set the refresh rate to **Refresh every second** and watch bounding boxes appear over detected objects in your webcam feed. The **Labels** panel on the right shows each detected class and its count in real time.

You now have multi-object detection powered by EfficientDet trained on the COCO dataset, running live on a Raspberry Pi — with zero lines of code written.

## What's next?

Ready to build something more? The [Your First Project tutorial](https://docs.viam.com/operate/hello-world/first-project/) walks you through setting up a full machine from scratch and is a great next step after this demo.

- **Browse more ML models** — explore the [Viam Registry](https://app.viam.com/registry?type=ML+Model) for classification, pose detection, and custom object detection models  
- **Capture training data** — use Viam's [data management service](https://docs.viam.com/data-ai/capture-data/capture-sync/) to collect labeled images and train your own model  
- **Trigger actions on detections** — use [Event Manager](https://app.viam.com/module/viam/event-manager) to actuate hardware or send alerts when a specific object is detected

Start building at [app.viam.com](https://app.viam.com/).