description: Navigate through supported dataset formats, methods to utilize them and how to add your own datasets. Get insights on porting or converting label formats.
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.
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:
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).
When using the Ultralytics YOLO format, organize your training and validation images and labels as shown in the [COCO8 dataset](coco8.md) example below.
- [**Argoverse**](argoverse.md): A collection of sensor data collected from autonomous vehicles. It contains 3D tracking annotations for car objects.
- [**COCO**](coco.md): Common Objects in Context (COCO) is a large-scale object detection, segmentation, and captioning dataset with 80 object categories.
- [**COCO8**](coco8.md): A smaller subset of the COCO dataset, COCO8 is more lightweight and faster to train.
- [**GlobalWheat2020**](globalwheat2020.md): A dataset containing images of wheat heads for the Global Wheat Challenge 2020.
- [**Objects365**](objects365.md): A large-scale object detection dataset with 365 object categories and 600k images, aimed at advancing object detection research.
- [**OpenImagesV7**](open-images-v7.md): A comprehensive dataset by Google with 1.7M train images and 42k validation images.
- [**SKU-110K**](sku-110k.md): A dataset containing images of densely packed retail products, intended for retail environment object detection.
- [**VisDrone**](visdrone.md): A dataset focusing on drone-based images, containing various object categories like cars, pedestrians, and cyclists.
- [**VOC**](voc.md): PASCAL VOC is a popular object detection dataset with 20 object categories including vehicles, animals, and furniture.
- [**xView**](xview.md): A dataset containing high-resolution satellite imagery, designed for the detection of various object classes in overhead views.
- [**Brain-tumor**](brain-tumor.md): This dataset comprises MRI or CT scan images containing information about brain tumor presence, location, and characteristics. It plays a crucial role in training computer vision models to automate tumor identification, facilitating early diagnosis and treatment planning.
- [**African-wildlife**](african-wildlife.md): Featuring images of African wildlife such as buffalo, elephant, rhino, and zebra, this dataset is instrumental in training computer vision models. It is indispensable for identifying animals across different habitats and contributes significantly to wildlife research endeavors.
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.
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.