👋 Hello @${{ github.actor }}, thank you for your interest in Ultralytics 🚀! We recommend a visit to the [Docs](https://docs.ultralytics.com) for new users where you can find many [Python](https://docs.ultralytics.com/usage/python/) and [CLI](https://docs.ultralytics.com/usage/cli/) usage examples and where many of the most common questions may already be answered.
@ -135,7 +135,7 @@ Congratulations! You have successfully set up a Conda environment, installed the
Setting up a Conda environment for Ultralytics projects is straightforward and ensures smooth package management. First, create a new Conda environment using the following command:
@ -106,7 +106,7 @@ When deploying [machine learning](https://www.ultralytics.com/glossary/machine-l
- **Profile the Inference Pipeline:** Identifying bottlenecks in the inference pipeline can help pinpoint the source of delays. Use profiling tools to analyze each step of the inference process, identifying and addressing any stages that cause significant delays, such as inefficient layers or data transfer issues.
- **Use Appropriate Precision:** Using higher precision than necessary can slow down inference times. Experiment with using lower precision, such as FP16 (half-precision), instead of FP32 (full-precision). While FP16 can reduce inference time, also keep in mind that it can impact model accuracy.
If you are facing this issue while deploying YOLO11, consider that YOLO11 offers [various model sizes](../models/yolov8.md), such as YOLO11n (nano) for devices with lower memory capacity and YOLOv8x (extra-large) for more powerful GPUs. Choosing the right model variant for your hardware can help balance memory usage and processing time.
If you are facing this issue while deploying YOLO11, consider that YOLO11 offers [various model sizes](../models/yolo11.md), such as YOLO11n (nano) for devices with lower memory capacity and YOLO11x (extra-large) for more powerful GPUs. Choosing the right model variant for your hardware can help balance memory usage and processing time.
Also keep in mind that the size of the input images directly impacts memory usage and processing time. Lower resolutions reduce memory usage and speed up inference, while higher resolutions improve accuracy but require more memory and processing power.
description: Learn how to deploy Ultralytics YOLOv8 on Raspberry Pi with our comprehensive guide. Get performance benchmarks, setup instructions, and best practices.
description: Learn how to deploy Ultralytics YOLO11 on Raspberry Pi with our comprehensive guide. Get performance benchmarks, setup instructions, and best practices.
# Quick Start Guide: Raspberry Pi with Ultralytics YOLOv8
# Quick Start Guide: Raspberry Pi with Ultralytics YOLO11
This comprehensive guide provides a detailed walkthrough for deploying Ultralytics YOLOv8 on [Raspberry Pi](https://www.raspberrypi.com/) devices. Additionally, it showcases performance benchmarks to demonstrate the capabilities of YOLOv8 on these small and powerful devices.
This comprehensive guide provides a detailed walkthrough for deploying Ultralytics YOLO11 on [Raspberry Pi](https://www.raspberrypi.com/) devices. Additionally, it showcases performance benchmarks to demonstrate the capabilities of YOLO11 on these small and powerful devices.
<palign="center">
<br>
@ -56,7 +56,7 @@ There are two ways of setting up Ultralytics package on Raspberry Pi to build yo
### Start with Docker
The fastest way to get started with Ultralytics YOLOv8 on Raspberry Pi is to run with pre-built docker image for Raspberry Pi.
The fastest way to get started with Ultralytics YOLO11 on Raspberry Pi is to run with pre-built docker image for Raspberry Pi.
Execute the below command to pull the Docker container and run on Raspberry Pi. This is based on [arm64v8/debian](https://hub.docker.com/r/arm64v8/debian) docker image which contains Debian 12 (Bookworm) in a Python3 environment.
@ -98,7 +98,7 @@ Out of all the model export formats supported by Ultralytics, [NCNN](https://doc
## Convert Model to NCNN and Run Inference
The YOLOv8n model in PyTorch format is converted to NCNN to run inference with the exported model.
The YOLO11n model in PyTorch format is converted to NCNN to run inference with the exported model.
!!! example
@ -107,14 +107,14 @@ The YOLOv8n model in PyTorch format is converted to NCNN to run inference with t
For more details about supported export options, visit the [Ultralytics documentation page on deployment options](https://docs.ultralytics.com/guides/model-deployment-options/).
## Raspberry Pi 5 vs Raspberry Pi 4 YOLOv8 Benchmarks
## Raspberry Pi 5 YOLO11 Benchmarks
YOLOv8 benchmarks were run by the Ultralytics team on nine different model formats measuring speed and [accuracy](https://www.ultralytics.com/glossary/accuracy): PyTorch, TorchScript, ONNX, OpenVINO, TF SavedModel, TF GraphDef, TF Lite, PaddlePaddle, NCNN. Benchmarks were run on both Raspberry Pi 5 and Raspberry Pi 4 at FP32 [precision](https://www.ultralytics.com/glossary/precision) with default input image size of 640.
!!! note
We have only included benchmarks for YOLOv8n and YOLOv8s models because other models sizes are too big to run on the Raspberry Pis and does not offer decent performance.
YOLO11 benchmarks were run by the Ultralytics team on nine different model formats measuring speed and [accuracy](https://www.ultralytics.com/glossary/accuracy): PyTorch, TorchScript, ONNX, OpenVINO, TF SavedModel, TF GraphDef, TF Lite, PaddlePaddle, NCNN. Benchmarks were run on a Raspberry Pi 5 at FP32 [precision](https://www.ultralytics.com/glossary/precision) with default input image size of 640.
### Comparison Chart
!!! tip "Performance"
=== "YOLOv8n"
We have only included benchmarks for YOLO11n and YOLO11s models because other models sizes are too big to run on the Raspberry Pis and does not offer decent performance.
<imgwidth="800"src="https://github.com/ultralytics/docs/releases/download/0/rpi-yolo11-benchmarks.avif"alt="YOLO11 benchmarks on RPi 5">
</div>
### Detailed Comparison Table
The below table represents the benchmark results for two different models (YOLOv8n, YOLOv8s) across nine different formats (PyTorch, TorchScript, ONNX, OpenVINO, TF SavedModel, TF GraphDef, TF Lite, PaddlePaddle, NCNN), running on both Raspberry Pi 4 and Raspberry Pi 5, giving us the status, size, mAP50-95(B) metric, and inference time for each combination.
The below table represents the benchmark results for two different models (YOLO11n, YOLO11s) across nine different formats (PyTorch, TorchScript, ONNX, OpenVINO, TF SavedModel, TF GraphDef, TF Lite, PaddlePaddle, NCNN), running on a Raspberry Pi 5, giving us the status, size, mAP50-95(B) metric, and inference time for each combination.
!!! tip "Performance"
=== "YOLOv8n on RPi5"
| Format | Status | Size on disk (MB) | mAP50-95(B) | Inference time (ms/im) |
Note that benchmarking results might vary based on the exact hardware and software configuration of a system, as well as the current workload of the system at the time the benchmarks are run. For the most reliable results use a dataset with a large number of images, i.e. `data='coco8.yaml' (4 val images), or `data='coco.yaml'` (5000 val images).
## Use Raspberry Pi Camera
When using Raspberry Pi for Computer Vision projects, it can be essentially to grab real-time video feeds to perform inference. The onboard MIPI CSI connector on the Raspberry Pi allows you to connect official Raspberry PI camera modules. In this guide, we have used a [Raspberry Pi Camera Module 3](https://www.raspberrypi.com/products/camera-module-3/) to grab the video feeds and perform inference using YOLOv8 models.
When using Raspberry Pi for Computer Vision projects, it can be essentially to grab real-time video feeds to perform inference. The onboard MIPI CSI connector on the Raspberry Pi allows you to connect official Raspberry PI camera modules. In this guide, we have used a [Raspberry Pi Camera Module 3](https://www.raspberrypi.com/products/camera-module-3/) to grab the video feeds and perform inference using YOLO11 models.
!!! tip
@ -273,13 +233,13 @@ rpicam-hello
### Inference with Camera
There are 2 methods of using the Raspberry Pi Camera to inference YOLOv8 models.
There are 2 methods of using the Raspberry Pi Camera to inference YOLO11 models.
!!! usage
=== "Method 1"
We can use `picamera2`which comes pre-installed with Raspberry Pi OS to access the camera and inference YOLOv8 models.
We can use `picamera2`which comes pre-installed with Raspberry Pi OS to access the camera and inference YOLO11 models.
!!! example
@ -299,14 +259,14 @@ There are 2 methods of using the Raspberry Pi Camera to inference YOLOv8 models.
picam2.configure("preview")
picam2.start()
# Load the YOLOv8 model
model = YOLO("yolov8n.pt")
# Load the YOLO11 model
model = YOLO("yolo11n.pt")
while True:
# Capture frame-by-frame
frame = picam2.capture_array()
# Run YOLOv8 inference on the frame
# Run YOLO11 inference on the frame
results = model(frame)
# Visualize the results on the frame
@ -340,8 +300,8 @@ There are 2 methods of using the Raspberry Pi Camera to inference YOLOv8 models.
```python
from ultralytics import YOLO
# Load a YOLOv8n PyTorch model
model = YOLO("yolov8n.pt")
# Load a YOLO11n PyTorch model
model = YOLO("yolo11n.pt")
# Run inference
results = model("tcp://127.0.0.1:8888")
@ -350,7 +310,7 @@ There are 2 methods of using the Raspberry Pi Camera to inference YOLOv8 models.
@ -359,7 +319,7 @@ There are 2 methods of using the Raspberry Pi Camera to inference YOLOv8 models.
## Best Practices when using Raspberry Pi
There are a couple of best practices to follow in order to enable maximum performance on Raspberry Pis running YOLOv8.
There are a couple of best practices to follow in order to enable maximum performance on Raspberry Pis running YOLO11.
1. Use an SSD
@ -371,7 +331,7 @@ There are a couple of best practices to follow in order to enable maximum perfor
## Next Steps
Congratulations on successfully setting up YOLO on your Raspberry Pi! For further learning and support, visit [Ultralytics YOLOv8 Docs](../index.md) and [Kashmir World Foundation](https://www.kashmirworldfoundation.org/).
Congratulations on successfully setting up YOLO on your Raspberry Pi! For further learning and support, visit [Ultralytics YOLO11 Docs](../index.md) and [Kashmir World Foundation](https://www.kashmirworldfoundation.org/).
## Acknowledgements and Citations
@ -381,9 +341,9 @@ For more information about Kashmir World Foundation's activities, you can visit
## FAQ
### How do I set up Ultralytics YOLOv8 on a Raspberry Pi without using Docker?
### How do I set up Ultralytics YOLO11 on a Raspberry Pi without using Docker?
To set up Ultralytics YOLOv8 on a Raspberry Pi without Docker, follow these steps:
To set up Ultralytics YOLO11 on a Raspberry Pi without Docker, follow these steps:
1. Update the package list and install `pip`:
```bash
@ -402,13 +362,13 @@ To set up Ultralytics YOLOv8 on a Raspberry Pi without Docker, follow these step
For detailed instructions, refer to the [Start without Docker](#start-without-docker) section.
### Why should I use Ultralytics YOLOv8's NCNN format on Raspberry Pi for AI tasks?
### Why should I use Ultralytics YOLO11's NCNN format on Raspberry Pi for AI tasks?
Ultralytics YOLOv8's NCNN format is highly optimized for mobile and embedded platforms, making it ideal for running AI tasks on Raspberry Pi devices. NCNN maximizes inference performance by leveraging ARM architecture, providing faster and more efficient processing compared to other formats. For more details on supported export options, visit the [Ultralytics documentation page on deployment options](../modes/export.md).
Ultralytics YOLO11's NCNN format is highly optimized for mobile and embedded platforms, making it ideal for running AI tasks on Raspberry Pi devices. NCNN maximizes inference performance by leveraging ARM architecture, providing faster and more efficient processing compared to other formats. For more details on supported export options, visit the [Ultralytics documentation page on deployment options](../modes/export.md).
### How can I convert a YOLOv8 model to NCNN format for use on Raspberry Pi?
### How can I convert a YOLO11 model to NCNN format for use on Raspberry Pi?
You can convert a PyTorch YOLOv8 model to NCNN format using either Python or CLI commands:
You can convert a PyTorch YOLO11 model to NCNN format using either Python or CLI commands:
!!! example
@ -417,14 +377,14 @@ You can convert a PyTorch YOLOv8 model to NCNN format using either Python or CLI
For more details, see the [Use NCNN on Raspberry Pi](#use-ncnn-on-raspberry-pi) section.
### What are the hardware differences between Raspberry Pi 4 and Raspberry Pi 5 relevant to running YOLOv8?
### What are the hardware differences between Raspberry Pi 4 and Raspberry Pi 5 relevant to running YOLO11?
Key differences include:
@ -450,11 +410,11 @@ Key differences include:
- **Max CPU Frequency**: Raspberry Pi 4 has a max frequency of 1.8GHz, whereas Raspberry Pi 5 reaches 2.4GHz.
- **Memory**: Raspberry Pi 4 offers up to 8GB of LPDDR4-3200 SDRAM, while Raspberry Pi 5 features LPDDR4X-4267 SDRAM, available in 4GB and 8GB variants.
These enhancements contribute to better performance benchmarks for YOLOv8 models on Raspberry Pi 5 compared to Raspberry Pi 4. Refer to the [Raspberry Pi Series Comparison](#raspberry-pi-series-comparison) table for more details.
These enhancements contribute to better performance benchmarks for YOLO11 models on Raspberry Pi 5 compared to Raspberry Pi 4. Refer to the [Raspberry Pi Series Comparison](#raspberry-pi-series-comparison) table for more details.
### How can I set up a Raspberry Pi Camera Module to work with Ultralytics YOLOv8?
### How can I set up a Raspberry Pi Camera Module to work with Ultralytics YOLO11?
There are two methods to set up a Raspberry Pi Camera for YOLOv8 inference:
There are two methods to set up a Raspberry Pi Camera for YOLO11 inference:
1. **Using `picamera2`**:
@ -471,7 +431,7 @@ There are two methods to set up a Raspberry Pi Camera for YOLOv8 inference:
picam2.configure("preview")
picam2.start()
model = YOLO("yolov8n.pt")
model = YOLO("yolo11n.pt")
while True:
frame = picam2.capture_array()
@ -494,7 +454,7 @@ There are two methods to set up a Raspberry Pi Camera for YOLOv8 inference:
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---
### What happens if Ultralytics does not use my contributions?
**Need More Help?**
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Ultralytics offers two licensing options for YOLO: AGPL-3.0 License and Enterprise License. Ultralytics is available on [GitHub](https://github.com/ultralytics/ultralytics)
@ -105,7 +163,7 @@ Ultralytics YOLO is the latest advancement in the acclaimed YOLO (You Only Look
Getting started with YOLO is quick and straightforward. You can install the Ultralytics package using [pip](https://pypi.org/project/ultralytics/) and get up and running in minutes. Here's a basic installation command:
!!! example
!!! example "Installation using pip"
=== "CLI"
@ -121,11 +179,11 @@ Training a custom YOLO model on your dataset involves a few detailed steps:
1. Prepare your annotated dataset.
2. Configure the training parameters in a YAML file.
3. Use the `yolo train` command to start training.
3. Use the `yolo TASK train` command to start training. (Each `TASK` has its own argument)
Here's example code:
Here's example code for the Object Detection Task:
!!! example
!!! example "Train Example for Object Detection Task"
For a detailed walkthrough, check out our [Train a Model](modes/train.md) guide, which includes examples and tips for optimizing your training process.
@ -161,7 +219,7 @@ For more details, visit our [Licensing](https://www.ultralytics.com/license) pag
Ultralytics YOLO supports efficient and customizable multi-object tracking. To utilize tracking capabilities, you can use the `yolo track` command as shown below:
!!! example
!!! example "Example for Object Tracking on a Video"
If you are using a Google Colab notebook, the code above will prompt you to enter your API key for initialization.
## Usage
Before diving into the usage instructions, be sure to check out the range of [YOLO11 models offered by Ultralytics](../models/index.md). This will help you choose the most appropriate model for your project requirements.
Before diving into the usage instructions, be sure to check out the range of [YOLO11 models offered by Ultralytics](../models/yolo11.md). This will help you choose the most appropriate model for your project requirements.
!!! example "Usage"
@ -75,7 +79,7 @@ Before diving into the usage instructions, be sure to check out the range of [YO
# Train the model
results = model.train(
data="coco8.yaml",
project="comet-example-yolov8-coco128",
project="comet-example-yolo11-coco128",
batch=32,
save_period=1,
save_json=True,
@ -200,7 +204,7 @@ To integrate Comet ML with Ultralytics YOLO11, follow these steps:
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