ultralytics/docs/en/datasets/detect/index.md

comments	description	keywords
true	Navigate through supported dataset formats, methods to utilize them and how to add your own datasets. Get insights on porting or converting label formats.	Ultralytics, YOLO, datasets, object detection, dataset formats, label formats, data conversion

Object Detection Datasets Overview

Training a robust and accurate object detection model requires a comprehensive dataset. This guide introduces various formats of datasets that are compatible with the Ultralytics YOLO model and provides insights into their structure, usage, and how to convert between different formats.

Supported Dataset Formats

Ultralytics YOLO format

The Ultralytics YOLO format is a dataset configuration format that allows you to define the dataset root directory, the relative paths to training/validation/testing image directories or *.txt files containing image paths, and a dictionary of class names. Here is an example:

# 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, ..]
path: ../datasets/coco8  # dataset root dir
train: images/train  # train images (relative to 'path') 4 images
val: images/val  # val images (relative to 'path') 4 images
test:  # test images (optional)

# Classes (80 COCO classes)
names:
  0: person
  1: bicycle
  2: car
  # ...
  77: teddy bear
  78: hair drier
  79: toothbrush

Labels for this format should be exported to YOLO format with one *.txt file per image. If there are no objects in an image, no *.txt file is required. The *.txt file should be formatted with one row per object in class x_center y_center width height format. Box coordinates must be in normalized xywh format (from 0 to 1). If your boxes are in pixels, you should divide x_center and width by image width, and y_center and height by image height. Class numbers should be zero-indexed (start with 0).

The label file corresponding to the above image contains 2 persons (class 0) and a tie (class 27):

When using the Ultralytics YOLO format, organize your training and validation images and labels as shown in the COCO8 dataset example below.

Usage

Here's how you can use these formats to train your model:

!!! Example

=== "Python"

    ```python
    from ultralytics import YOLO

    # Load a model
    model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)

    # Train the model
    results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
    ```
=== "CLI"

    ```bash
    # Start training from a pretrained *.pt model
    yolo detect train data=coco8.yaml model=yolov8n.pt epochs=100 imgsz=640
    ```

Supported Datasets

Here is a list of the supported datasets and a brief description for each:

• Argoverse: A dataset containing 3D tracking and motion forecasting data from urban environments with rich annotations.
• COCO: Common Objects in Context (COCO) is a large-scale object detection, segmentation, and captioning dataset with 80 object categories.
• LVIS: A large-scale object detection, segmentation, and captioning dataset with 1203 object categories.
• COCO8: A smaller subset of the first 4 images from COCO train and COCO val, suitable for quick tests.
• Global Wheat 2020: A dataset containing images of wheat heads for the Global Wheat Challenge 2020.
• Objects365: A high-quality, large-scale dataset for object detection with 365 object categories and over 600K annotated images.
• OpenImagesV7: A comprehensive dataset by Google with 1.7M train images and 42k validation images.
• SKU-110K: A dataset featuring dense object detection in retail environments with over 11K images and 1.7 million bounding boxes.
• VisDrone: A dataset containing object detection and multi-object tracking data from drone-captured imagery with over 10K images and video sequences.
• VOC: The Pascal Visual Object Classes (VOC) dataset for object detection and segmentation with 20 object classes and over 11K images.
• xView: A dataset for object detection in overhead imagery with 60 object categories and over 1 million annotated objects.
• Roboflow 100: A diverse object detection benchmark with 100 datasets spanning seven imagery domains for comprehensive model evaluation.
• Brain-tumor: A dataset for detecting brain tumors includes MRI or CT scan images with details on tumor presence, location, and characteristics.
• African-wildlife: A dataset featuring images of African wildlife, including buffalo, elephant, rhino, and zebras.
• Signature: A dataset featuring images of various documents with annotated signatures, supporting document verification and fraud detection research.

Adding your own dataset

If you have your own dataset and would like to use it for training detection 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.

Port or Convert Label Formats

COCO Dataset Format to YOLO Format

You can easily convert labels from the popular COCO dataset format to the YOLO format using the following code snippet:

!!! Example

=== "Python"

    ```python
    from ultralytics.data.converter import convert_coco

    convert_coco(labels_dir="path/to/coco/annotations/")
    ```

This conversion tool can be used to convert the COCO dataset or any dataset in the COCO format to the Ultralytics YOLO format.

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.