description: Explore image classification datasets supported by Ultralytics, learn the standard dataset format, and set up your own dataset for training models.
For [Ultralytics](https://ultralytics.com) YOLO classification tasks, the dataset must be organized in a specific split-direcotry structure under the `root` directory to facilitate proper training, testing, and optional validation processes. This structure includes separate directories for training (`train`) and testing (`test`) phases, with an optional directory for validation (`val`).
Each of these directories should contain one subdirectory for each class in the dataset. The subdirectories are named after the corresponding class and contain all the images for that class. Ensure that each image file is named uniquely and stored in a common format such as JPEG or PNG.
This structured approach ensures that the model can effectively learn from well-organized classes during the training phase and accurately evaluate performance during testing and validation phases.
If you have your own dataset and would like to use it for training classification models with Ultralytics, ensure that it follows the format specified above under "Dataset format" and then point your `data` argument to the dataset directory.
