As our product line expands, it can get confusing trying to keep track of all the different Raspberry Pi boards out there. Here is a high-level breakdown of Raspberry Pi models, including our flagship series, Zero series, Compute Module series, and Pico microcontrollers.

Raspberry Pi makes computers in several different series:

• The flagship series, often referred to by the shorthand ‘Raspberry Pi’, offers high-performance hardware, a full Linux operating system, and a variety of common ports in a form factor roughly the size of a credit card.

• The Zero series offers a full Linux operating system and essential ports at an affordable price point in a minimal form factor with low power consumption.

• The Compute Module series, often referred to by the shorthand ‘CM’, offers high-performance hardware and a full Linux operating system in a minimal form factor suitable for industrial and embedded applications. Compute Module models feature hardware equivalent to the corresponding flagship models but with fewer ports and no on-board GPIO pins. Instead, users should connect Compute Modules to a separate baseboard that provides the ports and pins required for a given application.

Additionally, Raspberry Pi makes the Pico series of tiny, versatile microcontroller boards. Pico models do not run Linux or allow for removable storage, but instead allow programming by flashing a binary onto on-board flash storage.

Flagship series

Model B indicates the presence of an Ethernet port. Model A indicates a lower-cost model in a smaller form factor with no Ethernet port, reduced RAM, and fewer USB ports to limit board height.

Model	SoC	Memory	GPIO	Connectivity
Raspberry Pi Model B	BCM2835	256MB, 512MB	26-pin GPIO header	HDMI, 2 × USB 2.0, CSI camera port, DSI display port, 3.5mm audio jack, RCA composite video, Ethernet (100Mb/s), SD card slot, micro USB power
Raspberry Pi Model A	BCM2835	256MB	26-pin GPIO header	HDMI, USB 2.0, CSI camera port, DSI display port, 3.5mm audio jack, RCA composite video, SD card slot, micro USB power
Raspberry Pi Model B+	BCM2835	512MB	40-pin GPIO header	HDMI, 4 × USB 2.0, CSI camera port, DSI display port, 3.5mm AV jack, Ethernet (100Mb/s), microSD card slot, micro USB power
Raspberry Pi Model A+	BCM2835	256MB, 512MB	40-pin GPIO header	HDMI, USB 2.0, CSI camera port, DSI display port, 3.5mm AV jack, microSD card slot, micro USB power
Raspberry Pi 2 Model B	BCM2836 (in version 1.2, switched to BCM2837)	1 GB	40-pin GPIO header	HDMI, 4 × USB 2.0, CSI camera port, DSI display port, 3.5mm AV jack, Ethernet (100Mb/s), microSD card slot, micro USB power
Raspberry Pi 3 Model B	BCM2837	1 GB	40-pin GPIO header	HDMI, 4 × USB 2.0, CSI camera port, DSI display port, 3.5mm AV jack, Ethernet (100Mb/s), 2.4GHz single-band 802.11n Wi-Fi (35Mb/s), Bluetooth 4.1, Bluetooth Low Energy (BLE), microSD card slot, micro USB power
Raspberry Pi 3 Model B+	BCM2837b0	1GB	40-pin GPIO header	HDMI, 4 × USB 2.0, CSI camera port, DSI display port, 3.5mm AV jack, PoE-capable Ethernet (300Mb/s), 2.4/5GHz dual-band 802.11ac Wi-Fi (100Mb/s), Bluetooth 4.2, Bluetooth Low Energy (BLE), microSD card slot, micro USB power
Raspberry Pi 3 Model A+	BCM2837b0	512 MB	40-pin GPIO header	HDMI, USB 2.0, CSI camera port, DSI display port, 3.5mm AV jack, 2.4/5GHz dual-band 802.11ac Wi-Fi (100Mb/s), Bluetooth 4.2, Bluetooth Low Energy (BLE), microSD card slot, micro USB power
Raspberry Pi 4 Model B	BCM2711	1GB, 2GB, 4GB, 8GB	40-pin GPIO header	2 × micro HDMI, 2 × USB 2.0, 2 × USB 3.0, CSI camera port, DSI display port, 3.5 mm AV jack, PoE-capable Gigabit Ethernet (1Gb/s), 2.4/5GHz dual-band 802.11ac Wi-Fi (120Mb/s), Bluetooth 5, Bluetooth Low Energy (BLE), microSD card slot, USB-C power (5V, 3A (15W))
Raspberry Pi 400	BCM2711	4GB	40-pin GPIO header	2 × micro HDMI, 2 × USB 2.0, 2 × USB 3.0, Gigabit Ethernet (1Gb/s), 2.4/5GHz dual-band 802.11ac Wi-Fi (120Mb/s), Bluetooth 5, Bluetooth Low Energy (BLE), microSD card slot, USB-C power (5V, 3A (15W))
Raspberry Pi 5	BCM2712 (2GB version uses BCM2712D0)	2GB, 4GB, 8GB	40-pin GPIO header	2 × micro HDMI, 2 × USB 2.0, 2 × USB 3.0, 2 ×  CSI camera/DSI display ports, single-lane PCIe FFC connector, UART connector, RTC battery connector, four-pin JST-SH PWM fan connector, PoE+-capable Gigabit Ethernet (1Gb/s), 2.4/5GHz dual-band 802.11ac Wi-Fi 5 (300Mb/s), Bluetooth 5, Bluetooth Low Energy (BLE), microSD card slot, USB-C power (5V, 5A (25W) or 5V, 3A (15W) with a 600mA peripheral limit)

For more information about the ports on the Raspberry Pi flagship series, see the Schematics and mechanical drawings.

Zero series

Models with the H suffix have header pins pre-soldered to the GPIO header. Models that lack the H suffix do not come with header pins attached to the GPIO header; the user must solder pins manually or attach a third-party pin kit.

All Zero models have the following connectivity:

• a microSD card slot
• a CSI camera port (version 1.3 of the original Zero introduced this port)
• a mini HDMI port
• 2 × micro USB ports (one for input power, one for external devices)

Model	SoC	Memory	GPIO	Wireless Connectivity
Raspberry Pi Zero	BCM2835	512MB	40-pin GPIO header (unpopulated)	none
Raspberry Pi Zero W	BCM2835	512MB	40-pin GPIO header (unpopulated)	2.4GHz single-band 802.11n Wi-Fi (35Mb/s), Bluetooth 4.0, Bluetooth Low Energy (BLE)
Raspberry Pi Zero WH	BCM2835	512MB	40-pin GPIO header	2.4GHz single-band 802.11n Wi-Fi (35Mb/s), Bluetooth 4.0, Bluetooth Low Energy (BLE)
Raspberry Pi Zero 2 W	RP3A0	512MB	40-pin GPIO header (unpopulated)	2.4GHz single-band 802.11n Wi-Fi (35Mb/s), Bluetooth 4.2, Bluetooth Low Energy (BLE)

Compute Module series

Model	SoC	Memory	Storage	Form factor	Wireless Connectivity
Raspberry Pi Compute Module 1	BCM2835	512MB	4GB	DDR2 SO-DIMM	none
Raspberry Pi Compute Module 3	BCM2837	1GB	0GB (Lite), 4GB	DDR2 SO-DIMM	none
Raspberry Pi Compute Module 3+	BCM2837b0	1GB	0GB (Lite), 8GB, 16GB, 32GB	DDR2 SO-DIMM	none
Raspberry Pi Compute Module 4S	BCM2711	1GB, 2GB, 4GB, 8GB	0GB (Lite), 8GB, 16GB, 32GB	DDR2 SO-DIMM	none
Raspberry Pi Compute Module 4	BCM2711	1GB, 2GB, 4GB, 8GB	0GB (Lite), 8GB, 16GB, 32GB	dual 100-pin high density connectors	optional: 2.4/5GHz dual-band 802.11ac Wi-Fi 5 (300Mb/s), Bluetooth 5, Bluetooth Low Energy (BLE)

For more information about Raspberry Pi Compute Modules, see the Compute Module documentation.

Pico microcontrollers

Models with the H suffix have header pins pre-soldered to the GPIO header. Models that lack the H suffix do not come with header pins attached to the GPIO header; the user must solder pins manually or attach a third-party pin kit.

Model	SoC	Memory	Storage	GPIO	Wireless Connectivity
Raspberry Pi Pico	RP2040	264KB	2MB	two 20-pin GPIO headers (unpopulated)	none
Raspberry Pi Pico H	RP2040	264KB	2MB	two 20-pin GPIO headers	none
Raspberry Pi Pico W	RP2040	264KB	2MB	two 20-pin GPIO headers (unpopulated)	2.4GHz single-band 802.11n Wi-Fi (10Mb/s), Bluetooth 5.2, Bluetooth Low Energy (BLE)
Raspberry Pi Pico WH	RP2040	264KB	2MB	two 20-pin GPIO headers	2.4GHz single-band 802.11n Wi-Fi (10Mb/s), Bluetooth 5.2, Bluetooth Low Energy (BLE)
Raspberry Pi Pico 2	RP2350	520KB	4MB	two 20-pin GPIO headers (unpopulated)	none

For more information about Raspberry Pi Pico models, see the Pico documentation.

If you’re interested in schematics, mechanical drawings, and information on thermal control, visit our documentation page.

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If you’ve got your hands on the Raspberry Pi AI Camera that we launched a few weeks ago, you might be looking for a bit of help to get up and running with it – it’s a bit different from our other camera products. We’ve raided our documentation to bring you this Getting started guide. If you work through the steps here you’ll have your camera performing object detection and pose estimation, even if all this is new to you. Then you can dive into the rest of our AI Camera documentation to take things further.

Here we describe how to run the pre-packaged MobileNet SSD (object detection) and PoseNet (pose estimation) neural network models on the Raspberry Pi AI Camera.

Prerequisites

We’re assuming that you’re using the AI Camera attached to either a Raspberry Pi 4 or a Raspberry Pi 5. With minor changes, you can follow these instructions on other Raspberry Pi models with a camera connector, including the Raspberry Pi Zero 2 W and Raspberry Pi 3 Model B+.

First, make sure that your Raspberry Pi runs the latest software. Run the following command to update:

sudo apt update && sudo apt full-upgrade

The AI Camera has an integrated RP2040 chip that handles neural network model upload to the camera, and we’ve released a new RP2040 firmware that greatly improves upload speed. AI Cameras shipping from now onwards already have this update, and if you have an earlier unit, you can update it yourself by following the firmware update instructions in this forum post. This should take no more than one or two minutes, but please note before you start that it’s vital nothing disrupts the process. If it does – for example, if the camera becomes disconnected, or if your Raspberry Pi loses power – the camera will become unusable and you’ll need to return it to your reseller for a replacement. Cameras with the earlier firmware are entirely functional, and their performance is identical in every respect except for model upload speed.

Install the IMX500 firmware

In addition to updating the RP2040 firmware if required, the AI camera must download runtime firmware onto the IMX500 sensor during startup. To install these firmware files onto your Raspberry Pi, run the following command:

sudo apt install imx500-all

This command:

• installs the /lib/firmware/imx500_loader.fpk and /lib/firmware/imx500_firmware.fpk firmware files required to operate the IMX500 sensor
• places a number of neural network model firmware files in /usr/share/imx500-models/
• installs the IMX500 post-processing software stages in rpicam-apps
• installs the Sony network model packaging tools

NOTE: The IMX500 kernel device driver loads all the firmware files when the camera starts, and this may take several minutes if the neural network model firmware has not been previously cached. The demos we’re using here display a progress bar on the console to indicate firmware loading progress.

Reboot

Now that you’ve installed the prerequisites, restart your Raspberry Pi:

sudo reboot

Run example applications

Once all the system packages are updated and firmware files installed, we can start running some example applications. As mentioned earlier, the Raspberry Pi AI Camera integrates fully with libcamera, rpicam-apps, and Picamera2. This blog post concentrates on rpicam-apps, but you’ll find more in our AI Camera documentation.

rpicam-apps

The rpicam-apps camera applications include IMX500 object detection and pose estimation stages that can be run in the post-processing pipeline. For more information about the post-processing pipeline, see the post-processing documentation.

The examples on this page use post-processing JSON files located in /usr/share/rpicam-assets/.

Object detection

The MobileNet SSD neural network performs basic object detection, providing bounding boxes and confidence values for each object found. imx500_mobilenet_ssd.json contains the configuration parameters for the IMX500 object detection post-processing stage using the MobileNet SSD neural network.

imx500_mobilenet_ssd.json declares a post-processing pipeline that contains two stages:

1. imx500_object_detection, which picks out bounding boxes and confidence values generated by the neural network in the output tensor
2. object_detect_draw_cv, which draws bounding boxes and labels on the image

The MobileNet SSD tensor requires no significant post-processing on your Raspberry Pi to generate the final output of bounding boxes. All object detection runs directly on the AI Camera.

The following command runs rpicam-hello with object detection post-processing:

rpicam-hello -t 0s --post-process-file /usr/share/rpi-camera-assets/imx500_mobilenet_ssd.json --viewfinder-width 1920 --viewfinder-height 1080 --framerate 30

After running the command, you should see a viewfinder that overlays bounding boxes on objects recognised by the neural network:

To record video with object detection overlays, use rpicam-vid instead:

rpicam-vid -t 10s -o output.264 --post-process-file /usr/share/rpi-camera-assets/imx500_mobilenet_ssd.json --width 1920 --height 1080 --framerate 30

You can configure the imx500_object_detection stage in many ways.

For example, max_detections defines the maximum number of objects that the pipeline will detect at any given time. threshold defines the minimum confidence value required for the pipeline to consider any input as an object.

The raw inference output data of this network can be quite noisy, so this stage also performs some temporal filtering and applies hysteresis. To disable this filtering, remove the temporal_filter config block.

Pose estimation

The PoseNet neural network performs pose estimation, labelling key points on the body associated with joints and limbs. imx500_posenet.json contains the configuration parameters for the IMX500 pose estimation post-processing stage using the PoseNet neural network.

imx500_posenet.json declares a post-processing pipeline that contains two stages:

1. imx500_posenet, which fetches the raw output tensor from the PoseNet neural network
2. plot_pose_cv, which draws line overlays on the image

The AI Camera performs basic detection, but the output tensor requires additional post-processing on your host Raspberry Pi to produce final output.

The following command runs rpicam-hello with pose estimation post-processing:

rpicam-hello -t 0s --post-process-file /usr/share/rpi-camera-assets/imx500_posenet.json --viewfinder-width 1920 --viewfinder-height 1080 --framerate 30

You can configure the imx500_posenet stage in many ways.

For example, max_detections defines the maximum number of bodies that the pipeline will detect at any given time. threshold defines the minimum confidence value required for the pipeline to consider input as a body.

Picamera2

For examples of image classification, object detection, object segmentation, and pose estimation using Picamera2, see the picamera2 GitHub repository.

Most of the examples use OpenCV for some additional processing. To install the dependencies required to run OpenCV, run the following command:

sudo apt install python3-opencv python3-munkres

Now download the picamera2 repository to your Raspberry Pi to run the examples. You’ll find example files in the root directory, with additional information in the README.md file.

Run the following script from the repository to run YOLOv8 object detection:

python imx500_object_detection_demo.py --model /usr/share/imx500-models/imx500_network_yolov8n_pp.rpk --ignore-dash-labels -r

To try pose estimation in Picamera2, run the following script from the repository:

python imx500_pose_estimation_higherhrnet_demo.py

To explore further, including how things work under the hood and how to convert existing models to run on the Raspberry Pi AI Camera, see our documentation.

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In the latest issue of The MagPi, Raspberry Pi Documentation Lead Nate Contino shows you how to attach a Raspberry Pi Pico-series device and start development with the new VS Code extension.

The following tutorial assumes that you are using a Pico-series device; some details may differ if you use a different Raspberry Pi microcontroller-based board. Pico-series devices are built around microcontrollers designed by Raspberry Pi itself. Development on the boards is fully supported with both a C/C++ SDK, and an official MicroPython port. This article talks about how to get started with the SDK, and walks you through how to build, install, and work with the SDK toolchain.

To install Visual Studio Code (known as VS Code for short) on Raspberry Pi OS or Linux, run the following commands: 

$ sudo apt update
$ sudo apt install code

On macOS and Windows, you can install VS Code from magpi.cc/vscode. On macOS, you can also install VS Code with brew using the following command: 

$ brew install --cask visual-studio-code

The Raspberry Pi Pico VS Code extension helps you create, develop, run, and debug projects in Visual Studio Code. It includes a project generator with many templating options, automatic toolchain management, one-click project compilation, and offline documentation of the Pico SDK. The VS Code extension supports all Raspberry Pi Pico-series devices.

Install dependencies

On Raspberry Pi OS and Windows no dependencies are needed.

Most Linux distributions come preconfigured with all of the dependencies needed to run the extension. However, some distributions may require additional dependencies.

The extension requires the following: 

• Python 3.9 or later 
• Git 
• Tar 
• A native C and C++ compiler (the extension supports GCC) 

You can install these with: 

$ sudo apt install python3 git tar build-essential

On macOS 

To install all requirements for the extension on macOS, run the following command: 

$ xcode-select --install

This installs the following dependencies:

• Git
• Tar 
• A native C and C++ compiler (the extension supports GCC and Clang)

Install the extension 

You can find the extension in the VS Code Extensions Marketplace. Search for the Raspberry Pi Pico extension, published by Raspberry Pi. Click the Install button to add it to VS Code.

You can find the store entry at magpi.cc/vscodeext. You can find the extension source code and release downloads at magpi.cc/picovscodegit. When installation completes, check the Activity sidebar (by default, on the left side of VS Code). If installation was successful, a new sidebar section appears with a Raspberry Pi Pico icon, labelled “Raspberry Pi Pico Project”.

Load and debug a project 

The VS Code extension can create projects based on the examples provided by Pico Examples. For an example, we’ll walk you through how to create a project that blinks the LED on your Pico-series device: 

1. In the VS Code left sidebar, select the Raspberry Pi Pico icon, labelled Raspberry Pi Pico Project. 
2. Select New Project from Examples. 
3. In the Name field, select the blink example. 
4. Choose the board type that matches your device. 
5. Specify a folder where the extension can generate files. VS Code will create the new project in a sub-folder of the selected folder. 
6. Click Create to create the project. The extension will now download the SDK and the toolchain, install them locally, and generate the new project. The first project may take five to ten minutes to install the toolchain. VS Code will ask you whether you trust the authors because we’ve automatically generated the .vscode directory for you. Select yes.

The CMake Tools extension may display some notifications at this point. Ignore and close them. 

On the left Explorer sidebar in VS Code, you should now see a list of files. Open blink.c to view the blink example source code in the main window. The Raspberry Pi Pico extension adds some capabilities to the status bar at the bottom right of the screen:

• Compile. Compiles the sources and builds the target UF2 file. You can copy this binary onto your device to program it. 
• Run. Finds a connected device, flashes the code into it, and runs that code.

The extension sidebar also contains some quick access functions. Click on the Pico icon in the side menu and you’ll see Compile Project. Hit Compile Project and a terminal tab will open at the bottom of the screen displaying the compilation progress.

Compile and run blink 

To run the blink example: 

1. Hold down the BOOTSEL button on your Pico-series device while plugging it into your development device using a Micro USB cable to force it into USB Mass Storage Mode. 
2. Press the Run button in the status bar or the Run Project button in the sidebar. You should see the terminal tab at the bottom of the window open. It will display information concerning the upload of the code. Once the code uploads, the device will reboot, and you should see the following output:

The device was rebooted to start the application.

Your blink code is now running. If you look at your device, the LED should blink twice every second. 

Make a code change and re-run 

To check that everything is working correctly, click on the blink.c file in VS Code. Navigate to the definition of LED_DELAY_MS at the top of the code: 

#ifndef LED_DELAY_MS
#define LED_DELAY_MS 250
#endif LED_DELAY_MS

Change the 250 (in ms, a quarter of a second) to 100 (a tenth of a second): #ifndef LED_DELAY_MS #define LED_DELAY_MS 100 #endif LED_DELAY_MS 

1. Disconnect your device, then reconnect while holding the BOOTSEL button just as you did before. 
2. Press the Run button in the status bar or the Run Project button in the sidebar. You should see the terminal tab at the bottom of the window open. It will display information concerning the upload of the code. Once the code uploads, the device will reboot, and you should see the following output: 

The device was rebooted to start the application.

Your blink code is now running. If you look at your device, the LED should flash faster, five times every second.

Top tip

Read the online guide

This tutorial also features in the Raspberry Pi Datasheet: Getting started with Pico. It also features information on using Raspberry Pi’s Debug Probe.

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