Add Chinese Modes and Tasks Docs (#6274)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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94
docs/en/datasets/segment/coco.md
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docs/en/datasets/segment/coco.md
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---
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comments: true
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description: Explore the possibilities of the COCO-Seg dataset, designed for object instance segmentation and YOLO model training. Discover key features, dataset structure, applications, and usage.
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keywords: Ultralytics, YOLO, COCO-Seg, dataset, instance segmentation, model training, deep learning, computer vision
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---
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# COCO-Seg Dataset
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The [COCO-Seg](https://cocodataset.org/#home) dataset, an extension of the COCO (Common Objects in Context) dataset, is specially designed to aid research in object instance segmentation. It uses the same images as COCO but introduces more detailed segmentation annotations. This dataset is a crucial resource for researchers and developers working on instance segmentation tasks, especially for training YOLO models.
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## Key Features
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- COCO-Seg retains the original 330K images from COCO.
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- The dataset consists of the same 80 object categories found in the original COCO dataset.
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- Annotations now include more detailed instance segmentation masks for each object in the images.
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- COCO-Seg provides standardized evaluation metrics like mean Average Precision (mAP) for object detection, and mean Average Recall (mAR) for instance segmentation tasks, enabling effective comparison of model performance.
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## Dataset Structure
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The COCO-Seg dataset is partitioned into three subsets:
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1. **Train2017**: This subset contains 118K images for training instance segmentation models.
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2. **Val2017**: This subset includes 5K images used for validation purposes during model training.
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3. **Test2017**: This subset encompasses 20K images used for testing and benchmarking the trained models. Ground truth annotations for this subset are not publicly available, and the results are submitted to the [COCO evaluation server](https://codalab.lisn.upsaclay.fr/competitions/7383) for performance evaluation.
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## Applications
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COCO-Seg is widely used for training and evaluating deep learning models in instance segmentation, such as the YOLO models. The large number of annotated images, the diversity of object categories, and the standardized evaluation metrics make it an indispensable resource for computer vision researchers and practitioners.
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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 contains information about the dataset's paths, classes, and other relevant information. In the case of the COCO-Seg dataset, the `coco.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco.yaml).
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!!! example "ultralytics/cfg/datasets/coco.yaml"
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```yaml
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--8<-- "ultralytics/cfg/datasets/coco.yaml"
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```
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## Usage
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To train a YOLOv8n-seg model on the COCO-Seg 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.
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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-seg.pt') # load a pretrained model (recommended for training)
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# Train the model
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results = model.train(data='coco-seg.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-seg.yaml model=yolov8n.pt epochs=100 imgsz=640
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```
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## Sample Images and Annotations
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COCO-Seg, like its predecessor COCO, contains a diverse set of images with various object categories and complex scenes. However, COCO-Seg introduces more detailed instance segmentation masks for each object in the images. Here are some examples of images from the dataset, along with their corresponding instance segmentation masks:
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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 aids 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 COCO-Seg 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 COCO-Seg dataset in your research or development work, please cite the original COCO paper and acknowledge the extension to COCO-Seg:
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!!! note ""
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=== "BibTeX"
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```bibtex
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@misc{lin2015microsoft,
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title={Microsoft COCO: Common Objects in Context},
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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},
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year={2015},
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eprint={1405.0312},
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archivePrefix={arXiv},
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primaryClass={cs.CV}
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}
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```
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We extend our thanks to the COCO Consortium for creating and maintaining this invaluable 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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docs/en/datasets/segment/coco8-seg.md
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docs/en/datasets/segment/coco8-seg.md
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---
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comments: true
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description: 'Discover the COCO8-Seg: a compact but versatile instance segmentation dataset ideal for testing Ultralytics YOLOv8 detection approaches. Complete usage guide included.'
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keywords: COCO8-Seg dataset, Ultralytics, YOLOv8, instance segmentation, dataset configuration, YAML, YOLOv8n-seg model, mosaiced dataset images
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---
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# COCO8-Seg Dataset
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## Introduction
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[Ultralytics](https://ultralytics.com) COCO8-Seg is a small, but versatile instance segmentation 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 segmentation 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.
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This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com)
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and [YOLOv8](https://github.com/ultralytics/ultralytics).
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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 contains information about the dataset's paths, classes, and other relevant information. In the case of the COCO8-Seg dataset, the `coco8-seg.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8-seg.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8-seg.yaml).
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!!! example "ultralytics/cfg/datasets/coco8-seg.yaml"
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```yaml
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--8<-- "ultralytics/cfg/datasets/coco8-seg.yaml"
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```
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## Usage
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To train a YOLOv8n-seg model on the COCO8-Seg 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.
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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-seg.pt') # load a pretrained model (recommended for training)
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# Train the model
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results = model.train(data='coco8-seg.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=coco8-seg.yaml model=yolov8n.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 COCO8-Seg dataset, along with their corresponding annotations:
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<img src="https://user-images.githubusercontent.com/26833433/236818387-f7bde7df-caaa-46d1-8341-1f7504cd11a1.jpg" alt="Dataset sample image" 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 COCO8-Seg 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 COCO dataset in your research or development work, please cite the following paper:
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!!! note ""
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=== "BibTeX"
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```bibtex
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@misc{lin2015microsoft,
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title={Microsoft COCO: Common Objects in Context},
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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},
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year={2015},
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eprint={1405.0312},
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archivePrefix={arXiv},
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primaryClass={cs.CV}
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}
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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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docs/en/datasets/segment/index.md
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---
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comments: true
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description: Learn how Ultralytics YOLO supports various dataset formats for instance segmentation. This guide includes information on data conversions, auto-annotations, and dataset usage.
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keywords: Ultralytics, YOLO, Instance Segmentation, Dataset, YAML, COCO, Auto-Annotation, Image Segmentation
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---
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# Instance Segmentation Datasets Overview
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## Supported Dataset Formats
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### Ultralytics YOLO format
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The dataset label format used for training YOLO segmentation models is as follows:
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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.
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2. One row per object: Each row in the text file corresponds to one object instance in the image.
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3. Object information per row: Each row contains the following information about the object instance:
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- Object class index: An integer representing the class of the object (e.g., 0 for person, 1 for car, etc.).
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- Object bounding coordinates: The bounding coordinates around the mask area, normalized to be between 0 and 1.
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The format for a single row in the segmentation dataset file is as follows:
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```
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<class-index> <x1> <y1> <x2> <y2> ... <xn> <yn>
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```
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In this format, `<class-index>` is the index of the class for the object, and `<x1> <y1> <x2> <y2> ... <xn> <yn>` are the bounding coordinates of the object's segmentation mask. The coordinates are separated by spaces.
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Here is an example of the YOLO dataset format for a single image with two objects made up of a 3-point segment and a 5-point segment.
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```
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0 0.681 0.485 0.670 0.487 0.676 0.487
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1 0.504 0.000 0.501 0.004 0.498 0.004 0.493 0.010 0.492 0.0104
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```
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!!! tip "Tip"
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- The length of each row does **not** have to be equal.
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- Each segmentation label must have a **minimum of 3 xy points**: `<class-index> <x1> <y1> <x2> <y2> <x3> <y3>`
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### Dataset YAML format
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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:
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```yaml
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# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
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path: ../datasets/coco8-seg # dataset root dir
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train: images/train # train images (relative to 'path') 4 images
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val: images/val # val images (relative to 'path') 4 images
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test: # test images (optional)
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# Classes (80 COCO classes)
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names:
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0: person
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1: bicycle
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2: car
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...
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77: teddy bear
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78: hair drier
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79: toothbrush
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```
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The `train` and `val` fields specify the paths to the directories containing the training and validation images, respectively.
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`names` is a dictionary of class names. The order of the names should match the order of the object class indices in the YOLO dataset files.
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## Usage
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!!! 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-seg.pt') # load a pretrained model (recommended for training)
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# Train the model
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results = model.train(data='coco128-seg.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=coco128-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640
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```
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## Supported Datasets
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* [COCO](coco.md): A large-scale dataset designed for object detection, segmentation, and captioning tasks with over 200K labeled images.
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* [COCO8-seg](coco8-seg.md): A smaller dataset for instance segmentation tasks, containing a subset of 8 COCO images with segmentation annotations.
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### Adding your own dataset
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If you have your own dataset and would like to use it for training segmentation models with Ultralytics YOLO format, ensure that it follows the format specified above under "Ultralytics YOLO format". Convert your annotations to the required format and specify the paths, number of classes, and class names in the YAML configuration file.
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## Port or Convert Label Formats
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### COCO Dataset Format to YOLO Format
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You can easily convert labels from the popular COCO dataset format to the YOLO format using the following code snippet:
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!!! example ""
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=== "Python"
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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_segments=True)
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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.
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Remember to double-check if the dataset you want to use is compatible with your model and follows the necessary format conventions. Properly formatted datasets are crucial for training successful object detection models.
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## Auto-Annotation
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Auto-annotation is an essential feature that allows you to generate a segmentation dataset using a pre-trained detection model. It enables you to quickly and accurately annotate a large number of images without the need for manual labeling, saving time and effort.
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### Generate Segmentation Dataset Using a Detection Model
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To auto-annotate your dataset using the Ultralytics framework, you can use the `auto_annotate` function as shown below:
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!!! example ""
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=== "Python"
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```python
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from ultralytics.data.annotator import auto_annotate
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auto_annotate(data="path/to/images", det_model="yolov8x.pt", sam_model='sam_b.pt')
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```
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Certainly, here is the table updated with code snippets:
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| Argument | Type | Description | Default |
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|--------------|-------------------------|-------------------------------------------------------------------------------------------------------------|----------------|
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| `data` | `str` | Path to a folder containing images to be annotated. | `None` |
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| `det_model` | `str, optional` | Pre-trained YOLO detection model. Defaults to `'yolov8x.pt'`. | `'yolov8x.pt'` |
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| `sam_model` | `str, optional` | Pre-trained SAM segmentation model. Defaults to `'sam_b.pt'`. | `'sam_b.pt'` |
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| `device` | `str, optional` | Device to run the models on. Defaults to an empty string (CPU or GPU, if available). | `''` |
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| `output_dir` | `str or None, optional` | Directory to save the annotated results. Defaults to a `'labels'` folder in the same directory as `'data'`. | `None` |
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The `auto_annotate` function takes the path to your images, along with optional arguments for specifying the pre-trained detection and [SAM segmentation models](https://docs.ultralytics.com/models/sam), the device to run the models on, and the output directory for saving the annotated results.
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By leveraging the power of pre-trained models, auto-annotation can significantly reduce the time and effort required for creating high-quality segmentation datasets. This feature is particularly useful for researchers and developers working with large image collections, as it allows them to focus on model development and evaluation rather than manual annotation.
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