单目3D初始代码
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docs/en/datasets/pose/dog-pose.md
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docs/en/datasets/pose/dog-pose.md
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---
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comments: true
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description: Discover the Dog-Pose dataset for pose detection. Featuring 6,773 training and 1,703 test images, it is a robust dataset for training YOLO26 models.
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keywords: Dog-Pose, Ultralytics, pose detection dataset, YOLO26, machine learning, computer vision, training data
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---
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# Dog-Pose Dataset
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## Introduction
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The [Ultralytics](https://www.ultralytics.com/) Dog-Pose dataset is a high-quality and extensive dataset specifically curated for dog keypoint estimation. With 6,773 training images and 1,703 test images, this dataset provides a solid foundation for training robust pose estimation models.
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<p align="center">
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<br>
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<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/ZhjO32tZUek"
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title="YouTube video player" frameborder="0"
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allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
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allowfullscreen>
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</iframe>
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<br>
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<strong>Watch:</strong> How to Train Ultralytics YOLO26 on the Stanford Dog Pose Estimation Dataset | Step-by-Step Tutorial
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</p>
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Each annotated image includes 24 keypoints with 3 dimensions per keypoint (x, y, visibility), making it a valuable resource for advanced research and development in computer vision.
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<img src="https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/ultralytics-dogs.avif" alt="Ultralytics Dog-pose display image" width="800">
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This dataset is intended for use with [Ultralytics Platform](https://platform.ultralytics.com/) and [YOLO26](https://github.com/ultralytics/ultralytics).
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## Dataset Structure
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- **Split**: 6,773 train / 1,703 test images with matching YOLO-format label files.
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- **Keypoints**: 24 per dog with `(x, y, visibility)` triplets.
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- **Layout**:
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```
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datasets/dog-pose/
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├── images/{train,test}
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└── labels/{train,test}
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```
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## Dataset YAML
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A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It includes paths, keypoint details, and other relevant information. In the case of the Dog-pose dataset, The `dog-pose.yaml` is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/dog-pose.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/dog-pose.yaml).
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!!! example "ultralytics/cfg/datasets/dog-pose.yaml"
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```yaml
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--8<-- "ultralytics/cfg/datasets/dog-pose.yaml"
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```
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## Usage
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To train a YOLO26n-pose model on the Dog-pose dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
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!!! example "Train Example"
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=== "Python"
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolo26n-pose.pt") # load a pretrained model (recommended for training)
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# Train the model
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results = model.train(data="dog-pose.yaml", epochs=100, imgsz=640)
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```
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=== "CLI"
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```bash
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# Start training from a pretrained *.pt model
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yolo pose train data=dog-pose.yaml model=yolo26n-pose.pt epochs=100 imgsz=640
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```
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## Sample Images and Annotations
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Here are some examples of images from the Dog-pose dataset, along with their corresponding annotations:
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<img src="https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/mosaiced-training-batch-2-dog-pose.avif" alt="Dog pose estimation dataset mosaic training batch" width="800">
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- **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts.
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The example showcases the variety and complexity of the images in the Dog-pose dataset and the benefits of using mosaicing during the training process.
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## Citations and Acknowledgments
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If you use the Dog-pose dataset in your research or development work, please cite the following paper:
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!!! quote ""
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=== "BibTeX"
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```bibtex
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@inproceedings{khosla2011fgvc,
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title={Novel dataset for Fine-Grained Image Categorization},
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author={Aditya Khosla and Nityananda Jayadevaprakash and Bangpeng Yao and Li Fei-Fei},
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booktitle={First Workshop on Fine-Grained Visual Categorization (FGVC), IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
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year={2011}
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}
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@inproceedings{deng2009imagenet,
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title={ImageNet: A Large-Scale Hierarchical Image Database},
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author={Jia Deng and Wei Dong and Richard Socher and Li-Jia Li and Kai Li and Li Fei-Fei},
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booktitle={IEEE Computer Vision and Pattern Recognition (CVPR)},
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year={2009}
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}
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```
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We would like to acknowledge the Stanford team for creating and maintaining this valuable resource for the [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) community. For more information about the Dog-pose dataset and its creators, visit the [Stanford Dogs Dataset website](http://vision.stanford.edu/aditya86/ImageNetDogs/).
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## FAQ
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### What is the Dog-pose dataset, and how is it used with Ultralytics YOLO26?
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The Dog-Pose dataset features 6,773 training and 1,703 test images annotated with 24 keypoints for dog pose estimation. It's designed for training and validating models with [Ultralytics YOLO26](../../models/yolo26.md), supporting applications like animal behavior analysis, pet monitoring, and veterinary studies. The dataset's comprehensive annotations make it ideal for developing accurate pose estimation models for canines.
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### How do I train a YOLO26 model using the Dog-pose dataset in Ultralytics?
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To train a YOLO26n-pose model on the Dog-pose dataset for 100 epochs with an image size of 640, follow these examples:
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!!! example "Train Example"
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=== "Python"
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolo26n-pose.pt")
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# Train the model
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results = model.train(data="dog-pose.yaml", epochs=100, imgsz=640)
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```
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=== "CLI"
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```bash
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yolo pose train data=dog-pose.yaml model=yolo26n-pose.pt epochs=100 imgsz=640
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```
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For a comprehensive list of training arguments, refer to the model [Training](../../modes/train.md) page.
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### What are the benefits of using the Dog-pose dataset?
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The Dog-pose dataset offers several benefits:
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**Large and Diverse Dataset**: With over 8,400 images, it provides substantial data covering a wide range of dog poses, breeds, and contexts, enabling robust model training and evaluation.
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**Detailed Keypoint Annotations**: Each image includes 24 keypoints with 3 dimensions per keypoint (x, y, visibility), offering precise annotations for training accurate pose detection models.
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**Real-World Scenarios**: Includes images from varied environments, enhancing the model's ability to generalize to real-world applications like [pet monitoring](https://www.ultralytics.com/blog/custom-training-ultralytics-yolo11-for-dog-pose-estimation) and behavior analysis.
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**Transfer Learning Advantage**: The dataset works well with [transfer learning](https://www.ultralytics.com/blog/understanding-few-shot-zero-shot-and-transfer-learning) techniques, allowing models pretrained on human pose datasets to adapt to dog-specific features.
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For more about its features and usage, see the [Dataset Introduction](#introduction) section.
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### How does mosaicing benefit the YOLO26 training process using the Dog-pose dataset?
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Mosaicing, as illustrated in the sample images from the Dog-pose dataset, merges multiple images into a single composite, enriching the diversity of objects and scenes in each training batch. This technique offers several benefits:
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- Increases the variety of dog poses, sizes, and backgrounds in each batch
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- Improves the model's ability to detect dogs in different contexts and scales
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- Enhances generalization by exposing the model to more diverse visual patterns
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- Reduces overfitting by creating novel combinations of training examples
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This approach leads to more robust models that perform better in real-world scenarios. For example images, refer to the [Sample Images and Annotations](#sample-images-and-annotations) section.
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### Where can I find the Dog-pose dataset YAML file and how do I use it?
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The Dog-pose dataset YAML file can be found at <https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/dog-pose.yaml>. This file defines the dataset configuration, including paths, classes, keypoint details, and other relevant information. The YAML specifies 24 keypoints with 3 dimensions per keypoint, making it suitable for detailed pose estimation tasks.
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To use this file with YOLO26 training scripts, simply reference it in your training command as shown in the [Usage](#usage) section. The dataset will be automatically downloaded when first used, making setup straightforward.
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For more FAQs and detailed documentation, visit the [Ultralytics Documentation](https://docs.ultralytics.com/).
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