Object Detection Models for Raspberry Pis

Explore our list of recommended object detection model architectures for use on Raspberry Pi devices.

Deploy select models (i.e. YOLOv8, CLIP) using the Roboflow Hosted API, or your own hardware using Roboflow Inference.

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YOLOv8

YOLOv8 is a state-of-the-art object detection and image segmentation model created by Ultralytics, the developers of YOLOv5.

Object Detection

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YOLOv9

YOLOv9 is an object detection model architecture released on February 21st, 2024.

Object Detection

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YOLOv5

A very fast and easy to use PyTorch model that achieves state of the art (or near state of the art) results.

Object Detection

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YOLO11

YOLO11 is a computer vision model that you can use for object detection, segmentation, and classification.

Object Detection

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YOLOv8 Oriented Bounding Boxes

You can retrieve bounding boxes whose edges match an angled object by training an oriented bounding boxes object detection model, such as YOLOv8's Oriented Bounding Boxes model.

Object Detection

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YOLOv7

YOLOv7 is a state of the art object detection model.

Object Detection

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YOLOv10

YOLOv10 is a real-time object detection model introduced in the paper "YOLOv10: Real-Time End-to-End Object Detection".

Object Detection

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Moondream 2

Moondream 2 is the latest model in the Moondream series of “tiny vision language models”.

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MT-YOLOv6

MT-YOLOv6 is a YOLO based model released in 2022.

Object Detection

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RT-DETR

Object Detection

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Visual Question Answering

Image Similarity

Image Captioning

Zero-shot Detection

Real-Time Vision

Image Embedding

LLMS with Vision Capabilities

Multimodal Vision

Foundation Vision