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Image Classification Datasets Overview

Dataset Structure for YOLO Classification Tasks

For Ultralytics 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.

Folder Structure Example

Consider the CIFAR-10 dataset as an example. The folder structure should look like this:

cifar-10-/
|
|-- train/
|   |-- airplane/
|   |   |-- 10008_airplane.png
|   |   |-- 10009_airplane.png
|   |   |-- ...
|   |
|   |-- automobile/
|   |   |-- 1000_automobile.png
|   |   |-- 1001_automobile.png
|   |   |-- ...
|   |
|   |-- bird/
|   |   |-- 10014_bird.png
|   |   |-- 10015_bird.png
|   |   |-- ...
|   |
|   |-- ...
|
|-- test/
|   |-- airplane/
|   |   |-- 10_airplane.png
|   |   |-- 11_airplane.png
|   |   |-- ...
|   |
|   |-- automobile/
|   |   |-- 100_automobile.png
|   |   |-- 101_automobile.png
|   |   |-- ...
|   |
|   |-- bird/
|   |   |-- 1000_bird.png
|   |   |-- 1001_bird.png
|   |   |-- ...
|   |
|   |-- ...
|
|-- val/ (optional)
|   |-- airplane/
|   |   |-- 105_airplane.png
|   |   |-- 106_airplane.png
|   |   |-- ...
|   |
|   |-- automobile/
|   |   |-- 102_automobile.png
|   |   |-- 103_automobile.png
|   |   |-- ...
|   |
|   |-- bird/
|   |   |-- 1045_bird.png
|   |   |-- 1046_bird.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.

Usage

Example

from ultralytics import YOLO

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

# Train the model
results = model.train(data='path/to/dataset', epochs=100, imgsz=640)

# Start training from a pretrained *.pt model
yolo detect train data=path/to/data model=yolov8n-cls.pt epochs=100 imgsz=640

Supported Datasets

Ultralytics supports the following datasets with automatic download:

  • Caltech 101: A dataset containing images of 101 object categories for image classification tasks.
  • Caltech 256: An extended version of Caltech 101 with 256 object categories and more challenging images.
  • CIFAR-10: A dataset of 60K 32x32 color images in 10 classes, with 6K images per class.
  • CIFAR-100: An extended version of CIFAR-10 with 100 object categories and 600 images per class.
  • Fashion-MNIST: A dataset consisting of 70,000 grayscale images of 10 fashion categories for image classification tasks.
  • ImageNet: A large-scale dataset for object detection and image classification with over 14 million images and 20,000 categories.
  • ImageNet-10: A smaller subset of ImageNet with 10 categories for faster experimentation and testing.
  • Imagenette: A smaller subset of ImageNet that contains 10 easily distinguishable classes for quicker training and testing.
  • Imagewoof: A more challenging subset of ImageNet containing 10 dog breed categories for image classification tasks.
  • MNIST: A dataset of 70,000 grayscale images of handwritten digits for image classification tasks.

Adding your own dataset

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.



Created 2023-11-12, Updated 2024-04-10
Authors: stormsson (1), glenn-jocher (4), GreatV (1)

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