> For the complete documentation index, see [llms.txt](https://docs.universaldatatool.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.universaldatatool.com/building-and-labeling-datasets/image-classification.md).

# Image Classification

## Setup the Dataset

Navigate to [udt.dev](https://udt.dev) and click "New File"

![Click "New File" on udt.dev](/files/-MI11H2GNT530d7n2r1Q)

Then select the Image Segmentation button from the `Setup > Data Type` page.

![](/files/-MIBB8b2peYagzSg1Cbq)

## Import Data

You can use any of the following methods to import image data. If you're just getting started, you can quickly create a dataset using the COCO Images method!

* [Import COCO Dialog](/importing-data/coco-images.md)
* [Import from Google Drive](/importing-data/import-from-google-drive.md)
* [Import from AWS S3 Bucket](/importing-data/import-from-aws-s3-bucket.md)
* [Import from List of URLs](/importing-data/import-file-urls.md)
* [Import from CSV or JSON](/importing-data/import-from-csv-or-json.md)
* [Upload or Open Directory](/importing-data/upload-or-open-directories.md)

## Label your Data (with friends!)

Use the `Label` tab to label your data. Look at the [Collaborative Labeling Guide](/collaborative-labeling.md) to label with others.

![An example Image Classification labeling task](/files/-MIFT4BKQXTIVczYsLNp)

## Export and Use

The image classification format is easy to use. If you'd like the data in a tabular format, download as CSV and consider taking a look at the [Pandas Usage Guide](/machine-learning/import-datasets-into-pandas.md) (which makes it easy to load into python). You can also download the images into folders using the [Fast.ai Image Classification Guide](/machine-learning/fastai/import-datasets-for-fast.ai-image-classification.md).


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