退购1.1定位算法

docs/datasets/pose/coco.md

@@ -0,0 +1,7 @@
+---
+comments: true
+---
+
+# 🚧 Page Under Construction ⚒
+
+This page is currently under construction!️ 👷Please check back later for updates. 😃🔜

docs/datasets/pose/coco8-pose.md

@@ -0,0 +1,79 @@
+---
+comments: true
+description: Test and debug object detection models with Ultralytics COCO8-Pose Dataset - a small, versatile pose detection dataset with 8 images.
+---
+
+# COCO8-Pose Dataset
+
+## Introduction
+
+[Ultralytics](https://ultralytics.com) COCO8-Pose is a small, but versatile pose detection dataset composed of the first
+8 images of the COCO train 2017 set, 4 for training and 4 for validation. This dataset is ideal for testing and
+debugging object detection models, or for experimenting with new detection approaches. With 8 images, it is small enough
+to be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before
+training larger datasets.
+
+This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com)
+and [YOLOv8](https://github.com/ultralytics/ultralytics).
+
+## Dataset YAML
+
+A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the COCO8-Pose dataset, the `coco8-pose.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/coco8-pose.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/coco8-pose.yaml).
+
+!!! example "ultralytics/datasets/coco8-pose.yaml"
+
+    ```yaml
+    --8<-- "ultralytics/datasets/coco8-pose.yaml"
+    ```
+
+## Usage
+
+To train a YOLOv8n model on the COCO8-Pose dataset for 100 epochs 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.
+
+!!! example "Train Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+        
+        # Load a model
+        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
+        
+        # Train the model
+        model.train(data='coco8-pose.yaml', epochs=100, imgsz=640)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo detect train data=coco8-pose.yaml model=yolov8n.pt epochs=100 imgsz=640
+        ```
+
+## Sample Images and Annotations
+
+Here are some examples of images from the COCO8-Pose dataset, along with their corresponding annotations:
+
+<img src="https://user-images.githubusercontent.com/26833433/236818283-52eecb96-fc6a-420d-8a26-d488b352dd4c.jpg" alt="Dataset sample image" width="800">
+
+- **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.
+
+The example showcases the variety and complexity of the images in the COCO8-Pose dataset and the benefits of using mosaicing during the training process.
+
+## Citations and Acknowledgments
+
+If you use the COCO dataset in your research or development work, please cite the following paper:
+
+```bibtex
+@misc{lin2015microsoft,
+      title={Microsoft COCO: Common Objects in Context}, 
+      author={Tsung-Yi Lin and Michael Maire and Serge Belongie and Lubomir Bourdev and Ross Girshick and James Hays and Pietro Perona and Deva Ramanan and C. Lawrence Zitnick and Piotr Dollár},
+      year={2015},
+      eprint={1405.0312},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```
+
+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).

docs/datasets/pose/index.md

@@ -0,0 +1,124 @@
+---
+comments: true
+description: Learn how to format your dataset for training YOLO models with Ultralytics YOLO format using our concise tutorial and example YAML files.
+---
+
+# Pose Estimation Datasets Overview
+
+## Supported Dataset Formats
+
+### Ultralytics YOLO format
+
+** Label Format **
+
+The dataset format used for training YOLO segmentation models is as follows:
+
+1. One text file per image: Each image in the dataset has a corresponding text file with the same name as the image file and the ".txt" extension.
+2. One row per object: Each row in the text file corresponds to one object instance in the image.
+3. Object information per row: Each row contains the following information about the object instance:
+    - Object class index: An integer representing the class of the object (e.g., 0 for person, 1 for car, etc.).
+    - Object center coordinates: The x and y coordinates of the center of the object, normalized to be between 0 and 1.
+    - Object width and height: The width and height of the object, normalized to be between 0 and 1.
+    - Object keypoint coordinates: The keypoints of the object, normalized to be between 0 and 1.
+
+Here is an example of the label format for pose estimation task:
+
+Format with Dim = 2
+
+```
+<class-index> <x> <y> <width> <height> <px1> <py1> <px2> <py2> ... <pxn> <pyn>
+```
+
+Format with Dim = 3
+
+```
+<class-index> <x> <y> <width> <height> <px1> <py1> <p1-visibility> <px2> <py2> <p2-visibility> <pxn> <pyn> <p2-visibility>
+```
+
+In this format, `<class-index>` is the index of the class for the object,`<x> <y> <width> <height>` are coordinates of boudning box, and `<px1> <py1> <px2> <py2> ... <pxn> <pyn>` are the pixel coordinates of the keypoints. The coordinates are separated by spaces.
+
+** Dataset file format **
+
+The Ultralytics framework uses a YAML file format to define the dataset and model configuration for training Detection Models. Here is an example of the YAML format used for defining a detection dataset:
+
+```yaml
+train: <path-to-training-images>
+val: <path-to-validation-images>
+
+nc: <number-of-classes>
+names: [<class-1>, <class-2>, ..., <class-n>]
+
+# Keypoints
+kpt_shape: [num_kpts, dim]  # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
+flip_idx: [n1, n2 ... , n(num_kpts)]
+
+```
+
+The `train` and `val` fields specify the paths to the directories containing the training and validation images, respectively.
+
+The `nc` field specifies the number of object classes in the dataset.
+
+The `names` field is a list of the names of the object classes. The order of the names should match the order of the object class indices in the YOLO dataset files.
+
+NOTE: Either `nc` or `names` must be defined. Defining both are not mandatory
+
+Alternatively, you can directly define class names like this:
+
+```
+names:
+  0: person
+  1: bicycle
+```
+
+(Optional) if the points are symmetric then need flip_idx, like left-right side of human or face.
+For example let's say there're five keypoints of facial landmark: [left eye, right eye, nose, left point of mouth, right point of mouse], and the original index is [0, 1, 2, 3, 4], then flip_idx is [1, 0, 2, 4, 3].(just exchange the left-right index, i.e 0-1 and 3-4, and do not modify others like nose in this example)
+
+** Example **
+
+```yaml
+train: data/train/
+val: data/val/
+
+nc: 2
+names: ['person', 'car']
+
+# Keypoints
+kpt_shape: [17, 3]  # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
+flip_idx: [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]
+```
+
+## Usage
+
+!!! example ""
+
+    === "Python"
+    
+        ```python
+        from ultralytics import YOLO
+        
+        # Load a model
+        model = YOLO('yolov8n-pose.pt')  # load a pretrained model (recommended for training)
+
+        # Train the model
+        model.train(data='coco128-pose.yaml', epochs=100, imgsz=640)
+        ```
+    === "CLI"
+    
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo detect train data=coco128-pose.yaml model=yolov8n-pose.pt epochs=100 imgsz=640
+        ```
+
+## Supported Datasets
+
+TODO
+
+## Port or Convert label formats
+
+### COCO dataset format to YOLO format
+
+```
+from ultralytics.yolo.data.converter import convert_coco
+
+convert_coco(labels_dir='../coco/annotations/', use_keypoints=True)
+```