`ultralytics 8.0.108` add Meituan YOLOv6 models (#2811)
Co-authored-by: Michael Currie <mcurrie@gmail.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Hicham Talaoubrid <98521878+HichTala@users.noreply.github.com> Co-authored-by: Zlobin Vladimir <vladimir.zlobin@intel.com> Co-authored-by: Szymon Mikler <sjmikler@gmail.com>single_channel
Glenn Jocher
2 years ago
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description: Discover Meituan YOLOv6, a robust real-time object detector. Learn how to utilize pre-trained models with Ultralytics Python API for a variety of tasks.
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---
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# Meituan YOLOv6
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## Overview
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[Meituan](https://about.meituan.com/) YOLOv6 is a cutting-edge object detector that offers remarkable balance between speed and accuracy, making it a popular choice for real-time applications. This model introduces several notable enhancements on its architecture and training scheme, including the implementation of a Bi-directional Concatenation (BiC) module, an anchor-aided training (AAT) strategy, and an improved backbone and neck design for state-of-the-art accuracy on the COCO dataset.
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**Overview of YOLOv6.** Model architecture diagram showing the redesigned network components and training strategies that have led to significant performance improvements. (a) The neck of YOLOv6 (N and S are shown). Note for M/L, RepBlocks is replaced with CSPStackRep. (b) The
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structure of a BiC module. (c) A SimCSPSPPF block. ([source](https://arxiv.org/pdf/2301.05586.pdf)).
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### Key Features
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- **Bi-directional Concatenation (BiC) Module:** YOLOv6 introduces a BiC module in the neck of the detector, enhancing localization signals and delivering performance gains with negligible speed degradation.
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- **Anchor-Aided Training (AAT) Strategy:** This model proposes AAT to enjoy the benefits of both anchor-based and anchor-free paradigms without compromising inference efficiency.
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- **Enhanced Backbone and Neck Design:** By deepening YOLOv6 to include another stage in the backbone and neck, this model achieves state-of-the-art performance on the COCO dataset at high-resolution input.
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- **Self-Distillation Strategy:** A new self-distillation strategy is implemented to boost the performance of smaller models of YOLOv6, enhancing the auxiliary regression branch during training and removing it at inference to avoid a marked speed decline.
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## Pre-trained Models
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YOLOv6 provides various pre-trained models with different scales:
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- YOLOv6-N: 37.5% AP on COCO val2017 at 1187 FPS with NVIDIA Tesla T4 GPU.
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- YOLOv6-S: 45.0% AP at 484 FPS.
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- YOLOv6-M: 50.0% AP at 226 FPS.
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- YOLOv6-L: 52.8% AP at 116 FPS.
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- YOLOv6-L6: State-of-the-art accuracy in real-time.
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YOLOv6 also provides quantized models for different precisions and models optimized for mobile platforms.
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## Usage
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### Python API
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```python
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from ultralytics import YOLO
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model = YOLO("yolov6n.yaml") # build new model from scratch
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model.info() # display model information
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model.predict("path/to/image.jpg") # predict
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```
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### Supported Tasks
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| Model Type | Pre-trained Weights | Tasks Supported |
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|------------|---------------------|------------------|
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| YOLOv6-N | `yolov6-n.pt` | Object Detection |
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| YOLOv6-S | `yolov6-s.pt` | Object Detection |
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| YOLOv6-M | `yolov6-m.pt` | Object Detection |
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| YOLOv6-L | `yolov6-l.pt` | Object Detection |
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| YOLOv6-L6 | `yolov6-l6.pt` | Object Detection |
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## Supported Modes
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| Mode | Supported |
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|------------|--------------------|
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| Inference | :heavy_check_mark: |
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| Validation | :heavy_check_mark: |
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| Training | :heavy_check_mark: |
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## Citations and Acknowledgements
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We would like to acknowledge the authors for their significant contributions in the field of real-time object detection:
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```bibtex
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@misc{li2023yolov6,
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title={YOLOv6 v3.0: A Full-Scale Reloading},
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author={Chuyi Li and Lulu Li and Yifei Geng and Hongliang Jiang and Meng Cheng and Bo Zhang and Zaidan Ke and Xiaoming Xu and Xiangxiang Chu},
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year={2023},
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eprint={2301.05586},
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archivePrefix={arXiv},
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primaryClass={cs.CV}
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}
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```
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The original YOLOv6 paper can be found on [arXiv](https://arxiv.org/abs/2301.05586). The authors have made their work publicly available, and the codebase can be accessed on [GitHub](https://github.com/meituan/YOLOv6). We appreciate their efforts in advancing the field and making their work accessible to the broader community.
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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# YOLOv6 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
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# Parameters
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act: nn.ReLU()
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nc: 80 # number of classes
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scales: # model compound scaling constants, i.e. 'model=yolov6n.yaml' will call yolov8.yaml with scale 'n'
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# [depth, width, max_channels]
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n: [ 0.33, 0.25, 1024 ]
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s: [ 0.33, 0.50, 1024 ]
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m: [ 0.67, 0.75, 768 ]
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l: [ 1.00, 1.00, 512 ]
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x: [ 1.00, 1.25, 512 ]
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# YOLOv6-3.0s backbone
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backbone:
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# [from, repeats, module, args]
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- [ -1, 1, Conv, [ 64, 3, 2 ] ] # 0-P1/2
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- [ -1, 1, Conv, [ 128, 3, 2 ] ] # 1-P2/4
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- [ -1, 6, Conv, [ 128, 3, 1 ] ]
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- [ -1, 1, Conv, [ 256, 3, 2 ] ] # 3-P3/8
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- [ -1, 12, Conv, [ 256, 3, 1 ] ]
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- [ -1, 1, Conv, [ 512, 3, 2 ] ] # 5-P4/16
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- [ -1, 18, Conv, [ 512, 3, 1 ] ]
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- [ -1, 1, Conv, [ 1024, 3, 2 ] ] # 7-P5/32
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- [ -1, 9, Conv, [ 1024, 3, 1 ] ]
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- [ -1, 1, SPPF, [ 1024, 5 ] ] # 9
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# YOLOv6-3.0s head
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head:
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- [ -1, 1, nn.ConvTranspose2d, [ 256, 2, 2, 0 ] ]
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- [ [ -1, 6 ], 1, Concat, [ 1 ] ] # cat backbone P4
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- [ -1, 1, Conv, [ 256, 3, 1 ] ]
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- [ -1, 9, Conv, [ 256, 3, 1 ] ] # 13
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- [ -1, 1, nn.ConvTranspose2d, [ 128, 2, 2, 0 ] ]
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- [ [ -1, 4 ], 1, Concat, [ 1 ] ] # cat backbone P3
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- [ -1, 1, Conv, [ 128, 3, 1 ] ]
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- [ -1, 9, Conv, [ 128, 3, 1 ] ] # 17
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- [ -1, 1, Conv, [ 128, 3, 2 ] ]
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- [ [ -1, 12 ], 1, Concat, [ 1 ] ] # cat head P4
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- [ -1, 1, Conv, [ 256, 3, 1 ] ]
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- [ -1, 9, Conv, [ 256, 3, 1 ] ] # 21
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- [ -1, 1, Conv, [ 256, 3, 2 ] ]
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- [ [ -1, 9 ], 1, Concat, [ 1 ] ] # cat head P5
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- [ -1, 1, Conv, [ 512, 3, 1 ] ]
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- [ -1, 9, Conv, [ 512, 3, 1 ] ] # 25
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- [ [ 17, 21, 25 ], 1, Detect, [ nc ] ] # Detect(P3, P4, P5)
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