ultralytics-ascend/docs/en/models/yolov6.md

---
comments: true
description: Explore Meituan YOLOv6, a top-tier object detector balancing speed and accuracy. Learn about its unique features and performance metrics on Ultralytics Docs.
keywords: Meituan YOLOv6, object detection, real-time applications, BiC module, Anchor-Aided Training, COCO dataset, high-performance models, Ultralytics Docs
---

# Meituan YOLOv6

## Overview

[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.

![Meituan YOLOv6](https://user-images.githubusercontent.com/26833433/240750495-4da954ce-8b3b-41c4-8afd-ddb74361d3c2.png)
![Model example image](https://user-images.githubusercontent.com/26833433/240750557-3e9ec4f0-0598-49a8-83ea-f33c91eb6d68.png) **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 structure of a BiC module. (c) A SimCSPSPPF block. ([source](https://arxiv.org/pdf/2301.05586.pdf)).

### Key Features

- **Bidirectional 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.
- **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.
- **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.
- **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.

## Performance Metrics

YOLOv6 provides various pre-trained models with different scales:

- YOLOv6-N: 37.5% AP on COCO val2017 at 1187 FPS with NVIDIA Tesla T4 GPU.
- YOLOv6-S: 45.0% AP at 484 FPS.
- YOLOv6-M: 50.0% AP at 226 FPS.
- YOLOv6-L: 52.8% AP at 116 FPS.
- YOLOv6-L6: State-of-the-art accuracy in real-time.

YOLOv6 also provides quantized models for different precisions and models optimized for mobile platforms.

## Usage Examples

This example provides simple YOLOv6 training and inference examples. For full documentation on these and other [modes](../modes/index.md) see the [Predict](../modes/predict.md), [Train](../modes/train.md), [Val](../modes/val.md) and [Export](../modes/export.md) docs pages.

!!! Example

    === "Python"

        PyTorch pretrained `*.pt` models as well as configuration `*.yaml` files can be passed to the `YOLO()` class to create a model instance in python:

        ```python
        from ultralytics import YOLO

        # Build a YOLOv6n model from scratch
        model = YOLO("yolov6n.yaml")

        # Display model information (optional)
        model.info()

        # Train the model on the COCO8 example dataset for 100 epochs
        results = model.train(data="coco8.yaml", epochs=100, imgsz=640)

        # Run inference with the YOLOv6n model on the 'bus.jpg' image
        results = model("path/to/bus.jpg")
        ```

    === "CLI"

        CLI commands are available to directly run the models:

        ```bash
        # Build a YOLOv6n model from scratch and train it on the COCO8 example dataset for 100 epochs
        yolo train model=yolov6n.yaml data=coco8.yaml epochs=100 imgsz=640

        # Build a YOLOv6n model from scratch and run inference on the 'bus.jpg' image
        yolo predict model=yolov6n.yaml source=path/to/bus.jpg
        ```

## Supported Tasks and Modes

The YOLOv6 series offers a range of models, each optimized for high-performance [Object Detection](../tasks/detect.md). These models cater to varying computational needs and accuracy requirements, making them versatile for a wide array of applications.

| Model Type | Pre-trained Weights | Tasks Supported                        | Inference | Validation | Training | Export |
| ---------- | ------------------- | -------------------------------------- | --------- | ---------- | -------- | ------ |
| YOLOv6-N   | `yolov6-n.pt`       | [Object Detection](../tasks/detect.md) | ✅        | ✅         | ✅       | ✅     |
| YOLOv6-S   | `yolov6-s.pt`       | [Object Detection](../tasks/detect.md) | ✅        | ✅         | ✅       | ✅     |
| YOLOv6-M   | `yolov6-m.pt`       | [Object Detection](../tasks/detect.md) | ✅        | ✅         | ✅       | ✅     |
| YOLOv6-L   | `yolov6-l.pt`       | [Object Detection](../tasks/detect.md) | ✅        | ✅         | ✅       | ✅     |
| YOLOv6-L6  | `yolov6-l6.pt`      | [Object Detection](../tasks/detect.md) | ✅        | ✅         | ✅       | ✅     |

This table provides a detailed overview of the YOLOv6 model variants, highlighting their capabilities in object detection tasks and their compatibility with various operational modes such as [Inference](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md). This comprehensive support ensures that users can fully leverage the capabilities of YOLOv6 models in a broad range of object detection scenarios.

## Citations and Acknowledgements

We would like to acknowledge the authors for their significant contributions in the field of real-time object detection:

!!! Quote ""

    === "BibTeX"

        ```bibtex
        @misc{li2023yolov6,
              title={YOLOv6 v3.0: A Full-Scale Reloading},
              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},
              year={2023},
              eprint={2301.05586},
              archivePrefix={arXiv},
              primaryClass={cs.CV}
        }
        ```

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.

## FAQ

### What is Meituan YOLOv6 and how does it differ from other YOLO models?

Meituan YOLOv6 is a highly advanced object detection model that balances speed and accuracy, making it ideal for real-time applications. This model features unique enhancements such as the Bidirectional Concatenation (BiC) module, Anchor-Aided Training (AAT) strategy, and an improved backbone and neck design, providing state-of-the-art performance on the COCO dataset. Unlike prior YOLO models, YOLOv6 incorporates these innovative strategies to enhance both inference speed and detection accuracy.

### How do I use the YOLOv6 model in a Python script?

Using the YOLOv6 model in a Python script is straightforward. Here is a sample code snippet to get you started:

```python
from ultralytics import YOLO

# Build a YOLOv6n model from scratch
model = YOLO("yolov6n.yaml")

# Display model information (optional)
model.info()

# Train the model on the COCO8 example dataset for 100 epochs
results = model.train(data="coco8.yaml", epochs=100, imgsz=640)

# Run inference with the YOLOv6n model on the 'bus.jpg' image
results = model("path/to/bus.jpg")
```

For more detailed examples and documentation, visit the [Train](../modes/train.md) and [Predict](../modes/predict.md) pages.

### What are the performance metrics for different scales of YOLOv6 models?

YOLOv6 offers pretrained models in various scales with the following performance metrics on the COCO val2017 dataset:

- **YOLOv6-N**: 37.5% AP at 1187 FPS using an NVIDIA Tesla T4 GPU
- **YOLOv6-S**: 45.0% AP at 484 FPS
- **YOLOv6-M**: 50.0% AP at 226 FPS
- **YOLOv6-L**: 52.8% AP at 116 FPS
- **YOLOv6-L6**: State-of-the-art accuracy for real-time

These metrics make YOLOv6 a versatile choice for both high-accuracy and high-speed applications.

### What are the unique features of YOLOv6 that improve its performance?

YOLOv6 introduces several key features that enhance its performance:

- **Bidirectional Concatenation (BiC) Module**: Improves localization signals and offers performance gains with minimal speed degradation.
- **Anchor-Aided Training (AAT) Strategy**: Combines the benefits of anchor-based and anchor-free methods for better efficiency without sacrificing inference speed.
- **Enhanced Backbone and Neck Design**: Adds additional stages to the backbone and neck, achieving state-of-the-art results on high-resolution inputs.
- **Self-Distillation Strategy**: Boosts smaller model performance by refining the auxiliary regression branch during training and removing it during inference to maintain speed.

### How can YOLOv6 be used for mobile and embedded applications?

YOLOv6 supports quantized models for different precisions and models optimized for mobile platforms, making it suitable for applications requiring low-latency and energy-efficient computations. For deployment on mobile and edge devices, you can explore conversion to formats like TFLite and ONNX, as detailed in the [Export](../modes/export.md) documentation. Quantized models ensure high performance even on resource-constrained devices, enabling real-time object detection in mobile and IoT applications.