YOLOv5-Lite

YOLOv5-Lite: A Convenient Solution for Lightweight Object Detection

YOLOv5-Lite is an innovative variation of the original YOLOv5, designed to be lighter, faster, and more accessible for deployment across various platforms. This project is particularly advantageous for environments with limited computational resources, such as mobile devices and edge computing systems.

Key Enhancements

YOLOv5-Lite introduces a series of improvements over the conventional YOLOv5 model:

1. Lighter Design:

   • The model is optimized with reduced Flops (Floating Point Operations), lower memory usage, and fewer parameters.
   • It is engineered to be smaller, making it more suitable for devices with limited storage capacity.

2. Faster Performance:

   • The model features channel shuffle techniques and a YOLOv5 head for channel reduction, significantly increasing its inference speed.
   • It demonstrates impressive performance, with capabilities of at least 10+ FPS on a Raspberry Pi 4B when processing a 320×320 image frame.

3. Ease of Deployment:

   • The Focus layer and several slice operations have been removed to simplify deployment and improve model quantization accuracy within acceptable ranges.

Experiment Results and Comparisons

YOLOv5-Lite's performance was evaluated against various models in different contexts:

• YOLOv5-Lite_e: This version is optimized for low-power devices.
• YOLOv5-Lite_s: This version is suitable for slightly more powerful devices, offering more accuracy with reasonable resource consumption.
• YOLOv5-Lite_c and _g: These versions provide even more accuracy and cater to more powerful setups with additional computational resources.

The following table demonstrates YOLOv5-Lite's efficiency compared to other models:

Platform Compatibility

YOLOv5-Lite exhibits adaptability across several platforms, including:

• Intel (i5-10210U) using OpenVINO on Windows
• Nvidia (RTX 2080Ti) with Torch on Linux
• Android devices like Redmi K30 and Xiaomi 10 with NCNN
• Raspberry Pi 4B, promising remarkable performance acceleration optimizations

Model Availability

YOLOv5-Lite offers various pre-trained models suitable for distinct frameworks and hardware:

• v5lite-e: Paired with shufflenetv2 backbone, designed for ARM CPUs.
• v5lite-s: Enhanced with shufflenetv2, tailored for both ARM CPU and GPU platforms.
• v5lite-c: Equipped with PPLcnet backbone, compatible with x86 CPUs.
• v5lite-g: Utilizes Repvgg for x86 GPUs and ARM NPUs.

How to Use YOLOv5-Lite

• Installation: Clone the repository, and install dependencies via Python’s package manager.
• Inference: Use detect.py for running inference on images, videos, and streams.
• Training: Train models using the provided configurations and datasets.
• Deployment: Detailed instructions for deploying YOLOv5-Lite on various platforms are available, with support for frameworks like NCNN, OpenVINO, TensorRT, etc.

With its versatile features and streamlined architecture, YOLOv5-Lite offers a robust solution for object detection tasks, particularly suitable for applications requiring efficient, quick, and flexible object recognition capabilities.