YOLOv6 v3.0: A Full-Scale Reloading

By Chuyin Li, Lu Li, Yifei Geng, Hongliang Jiang, Meng Cheng, Bo Zhang, Zaidan Ke, Xiaoming Xu, Xiangxiang Chu. arXiv.org, 2023

373 citations recorded by the source corpus.

The YOLO community has been in high spirits since our first two releases! By the advent of Chinese New Year 2023, which sees the Year of the Rabbit, we refurnish YOLOv6 with numerous novel enhancements on the network architecture and the training scheme. This release is identified as YOLOv6 v3.0. For a glimpse of performance, our YOLOv6-N hits 37.5% AP on the COCO dataset at a throughput of 1187 FPS tested with an NVIDIA Tesla T4 GPU. YOLOv6-S strikes 45.0% AP at 484 FPS, outperforming other mainstream detectors at the same scale (YOLOv5-S, YOLOv8-S, YOLOX-S and PPYOLOE-S). Whereas, YOLOv6-M/L also achieve better accuracy performance (50.0%/52.8% respectively) than other detectors at a similar inference speed. Additionally, with an extended backbone and neck design, our YOLOv6-L6 achieves the state-of-the-art accuracy in real-time. Extensive experiments are carefully conducted to validate the effectiveness of each improving component. Our code is made available at https://github.com/meituan/YOLOv6.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/e5148d28223b8764d4676d89b4f291aefb28899a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Jungong Han
2. Yunhe Wang

In Computer Vision and Pattern Recognition

1. Hongpeng Liao
2. Chien-Yao Wang
3. I-Hau Yeh
4. Yunhe Wang
5. Kai Han
6. Guiguang Ding
7. Zijia Lin
8. Jianyuan Guo

In Radiology, Nuclear Medicine and Imaging

1. Hongpeng Liao
2. Chien-Yao Wang
3. I-Hau Yeh
4. Yunhe Wang
5. Kai Han
6. Rongrong Ji
7. Qingqing Dang
8. Jianyuan Guo

Citations in the corpus

• Gold-YOLO: Efficient Object Detector via Gather-and-Distribute Mechanism (2023, methodology): “To further enhance the performance, we drew inspiration from the PAFPN module in YOLOv6 [19] and introduced an Inject-LAF module.”
• YOLOv10: Real-Time End-to-End Object Detection (2024, methodology): “YOLOv6 [29] presents BiC and SimCSPSPPF for neck and backbone, respectively, with anchor-aided training and self-distillation strategy.”
• Hyper-YOLO: When Visual Object Detection Meets Hypergraph Computation (2024, positive): “In the realm of neck architecture, leading-edge iterations such as YOLOv6 [5], YOLOv7 [7], and YOLOv8 [8] have consistently incorporated the PANet [16] (Path Aggregation Network) structure.”
• DETRs Beat YOLOs on Real-time Object Detection (2023, methodology): “We utilize the benchmark to test the end-to-end speed of anchor-based detectors YOLOv5 [10] and YOLOv7 [35], as well as anchor-free detectors PP-YOLOE [37], YOLOv6 [15] and YOLOv8 [11] on T4 GPU with TensorRT FP16.”
• YOLOv12: Attention-Centric Real-Time Object Detectors (2025, neutral): “Many attention-centric vision transformers are designed with the plain-style architectures [1, 18, 19, 21, 25, 51], while we retain the hierarchical design of the previous YOLO systems [3, 24, 28, 29, 32, 45–47, 53, 57, 58] and will demonstrate the necessity of this.”
• YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information (2024, methodology): “The current mainstream real-time object detectors are the YOLO series [2, 7, 13–15, 25, 30, 31, 47–49, 61–63, 74, 75], and most of these models use CSPNet [64] or ELAN [65] and their variants as the main computing units.”