Add FAQs to Docs Datasets and Help sections (#14211)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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@ -104,3 +104,56 @@ If you use the COCO-Pose dataset in your research or development work, please ci
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```
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We would like to acknowledge the COCO Consortium for creating and maintaining this valuable resource for the computer vision community. For more information about the COCO-Pose dataset and its creators, visit the [COCO dataset website](https://cocodataset.org/#home).
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## FAQ
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### What is the COCO-Pose dataset and how is it used with Ultralytics YOLO for pose estimation?
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The [COCO-Pose](https://cocodataset.org/#keypoints-2017) dataset is a specialized version of the COCO (Common Objects in Context) dataset designed for pose estimation tasks. It builds upon the COCO Keypoints 2017 images and annotations, allowing for the training of models like Ultralytics YOLO for detailed pose estimation. For instance, you can use the COCO-Pose dataset to train a YOLOv8n-pose model by loading a pretrained model and training it with a YAML configuration. For training examples, refer to the [Training](../../modes/train.md) documentation.
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### How can I train a YOLOv8 model on the COCO-Pose dataset?
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Training a YOLOv8 model on the COCO-Pose dataset can be accomplished using either Python or CLI commands. For example, to train a YOLOv8n-pose model for 100 epochs with an image size of 640, you can follow the steps below:
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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("yolov8n-pose.pt") # load a pretrained model (recommended for training)
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# Train the model
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results = model.train(data="coco-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 detect train data=coco-pose.yaml model=yolov8n.pt epochs=100 imgsz=640
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```
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For more details on the training process and available arguments, check the [training page](../../modes/train.md).
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### What are the different metrics provided by the COCO-Pose dataset for evaluating model performance?
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The COCO-Pose dataset provides several standardized evaluation metrics for pose estimation tasks, similar to the original COCO dataset. Key metrics include the Object Keypoint Similarity (OKS), which evaluates the accuracy of predicted keypoints against ground truth annotations. These metrics allow for thorough performance comparisons between different models. For instance, the COCO-Pose pretrained models such as YOLOv8n-pose, YOLOv8s-pose, and others have specific performance metrics listed in the documentation, like mAP<sup>pose</sup>50-95 and mAP<sup>pose</sup>50.
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### How is the dataset structured and split for the COCO-Pose dataset?
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The COCO-Pose dataset is split into three subsets:
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1. **Train2017**: Contains a portion of the 118K COCO images, annotated for training pose estimation models.
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2. **Val2017**: Selected images for validation purposes during model training.
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3. **Test2017**: Images used for testing and benchmarking trained models. Ground truth annotations for this subset are not publicly available; results are submitted to the [COCO evaluation server](https://codalab.lisn.upsaclay.fr/competitions/7384) for performance evaluation.
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These subsets help organize the training, validation, and testing phases effectively. For configuration details, explore the `coco-pose.yaml` file available on [GitHub](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco-pose.yaml).
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### What are the key features and applications of the COCO-Pose dataset?
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The COCO-Pose dataset extends the COCO Keypoints 2017 annotations to include 17 keypoints for human figures, enabling detailed pose estimation. Standardized evaluation metrics (e.g., OKS) facilitate comparisons across different models. Applications of the COCO-Pose dataset span various domains, such as sports analytics, healthcare, and human-computer interaction, wherever detailed pose estimation of human figures is required. For practical use, leveraging pretrained models like those provided in the documentation (e.g., YOLOv8n-pose) can significantly streamline the process ([Key Features](#key-features)).
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If you use the COCO-Pose dataset in your research or development work, please cite the paper with the following [BibTeX entry](#citations-and-acknowledgments).
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@ -77,3 +77,55 @@ If you use the COCO dataset in your research or development work, please cite th
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```
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We would like to acknowledge the COCO Consortium for creating and maintaining this valuable resource for the computer vision community. For more information about the COCO dataset and its creators, visit the [COCO dataset website](https://cocodataset.org/#home).
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## FAQ
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### What is the COCO8-Pose dataset, and how is it used with Ultralytics YOLOv8?
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The COCO8-Pose dataset is a small, versatile pose detection dataset that includes the first 8 images from the COCO train 2017 set, with 4 images for training and 4 for validation. It's designed for testing and debugging object detection models and experimenting with new detection approaches. This dataset is ideal for quick experiments with [Ultralytics YOLOv8](https://docs.ultralytics.com/models/yolov8/). For more details on dataset configuration, check out the dataset YAML file [here](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8-pose.yaml).
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### How do I train a YOLOv8 model using the COCO8-Pose dataset in Ultralytics?
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To train a YOLOv8n-pose model on the COCO8-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("yolov8n-pose.pt")
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# Train the model
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results = model.train(data="coco8-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 detect train data=coco8-pose.yaml model=yolov8n.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 COCO8-Pose dataset?
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The COCO8-Pose dataset offers several benefits:
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- **Compact Size**: With only 8 images, it is easy to manage and perfect for quick experiments.
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- **Diverse Data**: Despite its small size, it includes a variety of scenes, useful for thorough pipeline testing.
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- **Error Debugging**: Ideal for identifying training errors and performing sanity checks before scaling up to larger datasets.
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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 YOLOv8 training process using the COCO8-Pose dataset?
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Mosaicing, demonstrated in the sample images of the COCO8-Pose dataset, combines multiple images into one, increasing the variety of objects and scenes within each training batch. This technique helps improve the model's ability to generalize across various object sizes, aspect ratios, and contexts, ultimately enhancing model performance. See the [Sample Images and Annotations](#sample-images-and-annotations) section for example images.
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### Where can I find the COCO8-Pose dataset YAML file and how do I use it?
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The COCO8-Pose dataset YAML file can be found [here](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8-pose.yaml). This file defines the dataset configuration, including paths, classes, and other relevant information. Use this file with the YOLOv8 training scripts as mentioned in the [Train Example](#how-do-i-train-a-yolov8-model-using-the-coco8-pose-dataset-in-ultralytics) section.
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For more FAQs and detailed documentation, visit the [Ultralytics Documentation](https://docs.ultralytics.com/).
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@ -77,6 +77,7 @@ The `train` and `val` fields specify the paths to the directories containing the
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# Train the model
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results = model.train(data="coco8-pose.yaml", epochs=100, imgsz=640)
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```
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=== "CLI"
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```bash
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@ -136,3 +137,74 @@ Ultralytics provides a convenient conversion tool to convert labels from the pop
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```
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This conversion tool can be used to convert the COCO dataset or any dataset in the COCO format to the Ultralytics YOLO format. The `use_keypoints` parameter specifies whether to include keypoints (for pose estimation) in the converted labels.
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## FAQ
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### What is the Ultralytics YOLO format for pose estimation?
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The Ultralytics YOLO format for pose estimation datasets involves labeling each image with a corresponding text file. Each row of the text file stores information about an object instance:
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- Object class index
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- Object center coordinates (normalized x and y)
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- Object width and height (normalized)
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- Object keypoint coordinates (normalized pxn and pyn)
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For 2D poses, keypoints include pixel coordinates. For 3D, each keypoint also has a visibility flag. For more details, see [Ultralytics YOLO format](#ultralytics-yolo-format).
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### How do I use the COCO-Pose dataset with Ultralytics YOLO?
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To use the COCO-Pose dataset with Ultralytics YOLO:
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1. Download the dataset and prepare your label files in the YOLO format.
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2. Create a YAML configuration file specifying paths to training and validation images, keypoint shape, and class names.
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3. Use the configuration file for training:
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```python
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from ultralytics import YOLO
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model = YOLO("yolov8n-pose.pt") # load pretrained model
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results = model.train(data="coco-pose.yaml", epochs=100, imgsz=640)
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```
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For more information, visit [COCO-Pose](coco.md) and [train](../../modes/train.md) sections.
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### How can I add my own dataset for pose estimation in Ultralytics YOLO?
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To add your dataset:
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1. Convert your annotations to the Ultralytics YOLO format.
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2. Create a YAML configuration file specifying the dataset paths, number of classes, and class names.
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3. Use the configuration file to train your model:
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```python
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from ultralytics import YOLO
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model = YOLO("yolov8n-pose.pt")
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results = model.train(data="your-dataset.yaml", epochs=100, imgsz=640)
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```
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For complete steps, check the [Adding your own dataset](#adding-your-own-dataset) section.
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### What is the purpose of the dataset YAML file in Ultralytics YOLO?
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The dataset YAML file in Ultralytics YOLO defines the dataset and model configuration for training. It specifies paths to training, validation, and test images, keypoint shapes, class names, and other configuration options. This structured format helps streamline dataset management and model training. Here is an example YAML format:
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```yaml
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path: ../datasets/coco8-pose
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train: images/train
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val: images/val
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names:
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0: person
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```
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Read more about creating YAML configuration files in [Dataset YAML format](#dataset-yaml-format).
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### How can I convert COCO dataset labels to Ultralytics YOLO format for pose estimation?
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Ultralytics provides a conversion tool to convert COCO dataset labels to the YOLO format, including keypoint information:
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```python
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from ultralytics.data.converter import convert_coco
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convert_coco(labels_dir="path/to/coco/annotations/", use_keypoints=True)
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```
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This tool helps seamlessly integrate COCO datasets into YOLO projects. For details, refer to the [Conversion Tool](#conversion-tool) section.
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@ -96,3 +96,69 @@ The example showcases the variety and complexity of the images in the Tiger-Pose
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## Citations and Acknowledgments
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The dataset has been released available under the [AGPL-3.0 License](https://github.com/ultralytics/ultralytics/blob/main/LICENSE).
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## FAQ
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### What is the Ultralytics Tiger-Pose dataset used for?
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The Ultralytics Tiger-Pose dataset is designed for pose estimation tasks, consisting of 263 images sourced from a [YouTube video](https://www.youtube.com/watch?v=MIBAT6BGE6U&pp=ygUbVGlnZXIgd2Fsa2luZyByZWZlcmVuY2UubXA0). The dataset is divided into 210 training images and 53 validation images. It is particularly useful for testing, training, and refining pose estimation algorithms using [Ultralytics HUB](https://hub.ultralytics.com) and [YOLOv8](https://github.com/ultralytics/ultralytics).
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### How do I train a YOLOv8 model on the Tiger-Pose dataset?
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To train a YOLOv8n-pose model on the Tiger-Pose dataset for 100 epochs with an image size of 640, use the following code snippets. For more details, visit the [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("yolov8n-pose.pt") # load a pretrained model (recommended for training)
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# Train the model
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results = model.train(data="tiger-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 task=pose mode=train data=tiger-pose.yaml model=yolov8n.pt epochs=100 imgsz=640
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```
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### What configurations does the `tiger-pose.yaml` file include?
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The `tiger-pose.yaml` file is used to specify the configuration details of the Tiger-Pose dataset. It includes crucial data such as file paths and class definitions. To see the exact configuration, you can check out the [Ultralytics Tiger-Pose Dataset Configuration File](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/tiger-pose.yaml).
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### How can I run inference using a YOLOv8 model trained on the Tiger-Pose dataset?
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To perform inference using a YOLOv8 model trained on the Tiger-Pose dataset, you can use the following code snippets. For a detailed guide, visit the [Prediction](../../modes/predict.md) page:
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!!! Example "Inference 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("path/to/best.pt") # load a tiger-pose trained model
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# Run inference
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results = model.predict(source="https://youtu.be/MIBAT6BGE6U", show=True)
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```
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=== "CLI"
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```bash
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# Run inference using a tiger-pose trained model
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yolo task=pose mode=predict source="https://youtu.be/MIBAT6BGE6U" show=True model="path/to/best.pt"
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```
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### What are the benefits of using the Tiger-Pose dataset for pose estimation?
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The Tiger-Pose dataset, despite its manageable size of 210 images for training, provides a diverse collection of images that are ideal for testing pose estimation pipelines. The dataset helps identify potential errors and acts as a preliminary step before working with larger datasets. Additionally, the dataset supports the training and refinement of pose estimation algorithms using advanced tools like [Ultralytics HUB](https://hub.ultralytics.com) and [YOLOv8](https://github.com/ultralytics/ultralytics), enhancing model performance and accuracy.
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