YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications
Indexed inarxivdatacite
Abstract
For years, the YOLO series has been the de facto industry-level standard for efficient object detection. The YOLO community has prospered overwhelmingly to enrich its use in a multitude of hardware platforms and abundant scenarios. In this technical report, we strive to push its limits to the next level, stepping forward with an unwavering mindset for industry application. Considering the diverse requirements for speed and accuracy in the real environment, we extensively examine the up-to-date object detection advancements either from industry or academia. Specifically, we heavily assimilate ideas from recent network design, training strategies, testing techniques, quantization, and optimization methods. On…
Citation impact
1,745
total citations
- FWCI
- —
- Percentile
- —
- References
- 0
Citations per year
Authors
18Topics & keywords
Topics
Keywords
- Computer science
- Mindset
- Mainstream
- Throughput
- Object detection
- Software deployment
- Quantization (signal processing)
- Artificial intelligence
UN Sustainable Development Goals
- Industry, innovation and infrastructure
No related works found for this paper.