YOLOv10 improves both performance and efficiency, the best practice of object detection algorithm!

This tutorial is a gradio application tutorial for YOLOv10.

Introduction

The YOLO (You Only Look Once) series is currently the most mainstream edge-side object detection algorithm. It was first proposed by Joseph Redmon and others. It has become a benchmark in the field of real-time object detection because it achieves an effective balance between computing cost and detection performance. Over time, the YOLO series of algorithms has been continuously developed and improved, and multiple versions have been released. Each version has made significant progress in architecture design, optimization objectives, data enhancement strategies, and other aspects.

YOLOv10 is a real-time object detection method developed by researchers from Tsinghua University based on the Ultralytics Python package. It aims to address the deficiencies of previous YOLO versions in post-processing and model architecture. By eliminating non-maximum suppression (NMS) and optimizing various model components, YOLOv10 achieves state-of-the-art performance while significantly reducing computational overhead. The research team published a paper 「YOLOv10: Real-time End-to-End Object Detection」The study framework is explained in detail.

The main features of YOLOv10 include:

• NMS-free training: Leverage consistent dual assignment to eliminate the need for NMS, thereby reducing inference latency.
• Holistic model design: Various components are comprehensively optimized from the perspectives of efficiency and accuracy, including lightweight classification head, spatial channel decoupling down-sampling, and rank-guided block design.
• Enhanced model capabilities: Incorporation of large kernel convolution and partial self-attention modules improves performance without adding significant computational cost.

Effect display

How to run

1. After cloning the container, wait for the system to allocate resources

2. Perform image detection