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81 lines
4.2 KiB
81 lines
4.2 KiB
--- |
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comments: true |
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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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