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>

docs/models/index.md

@@ -11,6 +11,7 @@ In this documentation, we provide information on four major models:
 
 1. [YOLOv3](./yolov3.md): The third iteration of the YOLO model family, known for its efficient real-time object detection capabilities.
 2. [YOLOv5](./yolov5.md): An improved version of the YOLO architecture, offering better performance and speed tradeoffs compared to previous versions.
+3. [YOLOv6](./yolov6.md): Released by [Meituan](https://about.meituan.com/) in 2022 and is in use in many of the company's autonomous delivery robots.
 3. [YOLOv8](./yolov8.md): The latest version of the YOLO family, featuring enhanced capabilities such as instance segmentation, pose/keypoints estimation, and classification.
 4. [Segment Anything Model (SAM)](./sam.md): Meta's Segment Anything Model (SAM).
 5. [Realtime Detection Transformers (RT-DETR)](./rtdetr.md): Baidu's RT-DETR model.

docs/models/sam.md

@@ -26,7 +26,7 @@ For more information about the Segment Anything Model and the SA-1B dataset, ple
 SAM can be used for a variety of downstream tasks involving object and image distributions beyond its training data. Examples include edge detection, object proposal generation, instance segmentation, and preliminary text-to-mask prediction. By employing prompt engineering, SAM can adapt to new tasks and data distributions in a zero-shot manner, making it a versatile and powerful tool for image segmentation tasks.
 
 ```python
-from ultralytics.vit import SAM
+from ultralytics import SAM
 
 model = SAM('sam_b.pt')
 model.info()  # display model information

docs/models/yolov6.md

@@ -0,0 +1,81 @@
+---
+comments: true
+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.
+---
+
+# 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
+
+- **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.
+- **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.
+
+## Pre-trained Models
+
+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
+
+### Python API
+
+```python
+from ultralytics import YOLO
+
+model = YOLO("yolov6n.yaml")  # build new model from scratch
+model.info()  # display model information
+model.predict("path/to/image.jpg")  # predict
+```
+
+### Supported Tasks
+
+| Model Type | Pre-trained Weights | Tasks Supported  |
+|------------|---------------------|------------------|
+| YOLOv6-N   | `yolov6-n.pt`       | Object Detection |
+| YOLOv6-S   | `yolov6-s.pt`       | Object Detection |
+| YOLOv6-M   | `yolov6-m.pt`       | Object Detection |
+| YOLOv6-L   | `yolov6-l.pt`       | Object Detection |
+| YOLOv6-L6  | `yolov6-l6.pt`      | Object Detection |
+
+## Supported Modes
+
+| Mode       | Supported          |
+|------------|--------------------|
+| Inference  | :heavy_check_mark: |
+| Validation | :heavy_check_mark: |
+| Training   | :heavy_check_mark: |
+
+## Citations and Acknowledgements
+
+We would like to acknowledge the authors for their significant contributions in the field of real-time object detection:
+
+```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.