YOLOv5-Lite

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YOLOv5-Lite is an open-source object detection model that is lighter, faster, and easier to deploy than its predecessor, YOLOv5. It offers models as small as 900kb (int8) and can achieve 15 FPS on a Raspberry Pi 4B.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is YOLOv5-Lite?

YOLOv5-Lite is an optimized object detection model evolved from YOLOv5, designed for enhanced lightness, speed, and deployment ease. It significantly reduces model size, with versions as compact as 900kb (int8) and 1.7M (fp16), making it highly efficient for resource-constrained environments. The tool boasts impressive performance, reaching 15 FPS on a Raspberry Pi 4B, making it suitable for edge computing and embedded systems. Key optimizations include the removal of the Focus layer and four slice operations, along with the addition of shuffle channels and a YOLOv5 head for channel reduction. YOLOv5-Lite supports various frameworks and backends like PyTorch, ncnn, mnn, OpenVINO, TensorRT, and TFLite, and provides models tailored for different platforms and precision levels. It includes comprehensive documentation for installation, inference, training, and deployment on diverse hardware.

Best used for

Ideal for developers and data scientists who need to implement efficient object detection on embedded systems, optimize models for mobile applications, and conduct research on efficient AI models. Especially valuable for projects requiring high performance on low-power hardware like the Raspberry Pi 4B.

Common actions

optimize object detection

deploy AI models

train custom models

run inference

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Capabilities

Key features

Lighter model size
Faster inference speed
Raspberry Pi 4B compatible
Multiple framework support
Model quantization options
Easy deployment

Target Audience

developerdata scientiststudent

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What are the key performance improvements of YOLOv5-Lite compared to YOLOv5?

YOLOv5-Lite is designed to be lighter, faster, and easier to deploy. It achieves this through optimizations like removing the Focus layer and slice operations, adding shuffle channels, and reducing model quantization accuracy to an acceptable range, resulting in smaller model sizes and higher FPS on devices like the Raspberry Pi 4B.

Which hardware platforms and frameworks does YOLOv5-Lite support for deployment?

YOLOv5-Lite supports deployment on various platforms including Raspberry Pi 4B, Android devices (Snapdragon processors), and AXera-Pi. It is compatible with multiple frameworks and backends such as PyTorch, ncnn, mnn, OpenVINO, TensorRT, TFLite, and ONNX Runtime, offering flexibility for different deployment scenarios.

Can YOLOv5-Lite models be trained on custom datasets?

Yes, YOLOv5-Lite provides training scripts that allow users to train models on custom datasets. The documentation outlines the required Python environment setup and commands for training, including support for multi-GPU training to accelerate the process.

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