For [Ultralytics](https://ultralytics.com) YOLO classification tasks, the dataset must be organized in a specific split-directory 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.
### How do I structure my dataset for YOLO classification tasks?
To structure your dataset for Ultralytics YOLO classification tasks, you should follow a specific split-directory format. Organize your dataset into separate directories for `train`, `test`, and optionally `val`. Each of these directories should contain subdirectories named after each class, with the corresponding images inside. This facilitates smooth training and evaluation processes. For an example, consider the CIFAR-10 dataset format:
```
cifar-10-/
|-- train/
| |-- airplane/
| |-- automobile/
| |-- bird/
| ...
|-- test/
| |-- airplane/
| |-- automobile/
| |-- bird/
| ...
|-- val/ (optional)
| |-- airplane/
| |-- automobile/
| |-- bird/
| ...
```
For more details, visit [Dataset Structure for YOLO Classification Tasks](#dataset-structure-for-yolo-classification-tasks).
### What datasets are supported by Ultralytics YOLO for image classification?
Ultralytics YOLO supports automatic downloading of several datasets for image classification, including:
- [Caltech 101](caltech101.md)
- [Caltech 256](caltech256.md)
- [CIFAR-10](cifar10.md)
- [CIFAR-100](cifar100.md)
- [Fashion-MNIST](fashion-mnist.md)
- [ImageNet](imagenet.md)
- [ImageNet-10](imagenet10.md)
- [Imagenette](imagenette.md)
- [Imagewoof](imagewoof.md)
- [MNIST](mnist.md)
These datasets are structured in a way that makes them easy to use with YOLO. Each dataset's page provides further details about its structure and applications.
### How do I add my own dataset for YOLO image classification?
To use your own dataset with Ultralytics YOLO, ensure it follows the specified directory format required for the classification task, with separate `train`, `test`, and optionally `val` directories, and subdirectories for each class containing the respective images. Once your dataset is structured correctly, point the `data` argument to your dataset's root directory when initializing the training script. Here's an example in Python:
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n-cls.pt") # load a pretrained model (recommended for training)
These examples demonstrate the straightforward process of training a YOLO model using either approach. For more information, visit the [Usage](#usage) section.
