Create an image dataset

There are two methods for creating and sharing an image dataset. This guide will show you how to:

• Create an image dataset from local files in python with Dataset.push_to_hub(). This is an easy way that requires only a few steps in python.

• Create an image dataset with ImageFolder and some metadata. This is a no-code solution for quickly creating an image dataset with several thousand images.

ImageFolder

The ImageFolder is a dataset builder designed to quickly load an image dataset with several thousand images without requiring you to write any code.

ImageFolder automatically infers the class labels of your dataset based on the directory name. Store your dataset in a directory structure like:

folder/train/dog/golden_retriever.png
folder/train/dog/german_shepherd.png
folder/train/dog/chihuahua.png

folder/train/cat/maine_coon.png
folder/train/cat/bengal.png
folder/train/cat/birman.png

If the dataset follows the ImageFolder structure, then you can load it directly with load_dataset():

>>> from datasets import load_dataset

>>> dataset = load_dataset("path/to/folder")

This is equivalent to passing imagefolder manually in load_dataset() and the directory in data_dir:

>>> dataset = load_dataset("imagefolder", data_dir="/path/to/folder")

You can also use imagefolder to load datasets involving multiple splits. To do so, your dataset directory should have the following structure:

folder/train/dog/golden_retriever.png
folder/train/cat/maine_coon.png
folder/test/dog/german_shepherd.png
folder/test/cat/bengal.png

If there is additional information you’d like to include about your dataset, like text captions or bounding boxes, add it as a metadata.csv file in your folder. This lets you quickly create datasets for different computer vision tasks like text captioning or object detection. You can also use a JSONL file metadata.jsonl or a Parquet file metadata.parquet.

folder/train/metadata.csv
folder/train/0001.png
folder/train/0002.png
folder/train/0003.png

You can also zip your images, and in this case each zip should contain both the images and the metadata

folder/train.zip
folder/test.zip
folder/validation.zip

Your metadata.csv file must have a file_name or *_file_name field which links image files with their metadata:

file_name,additional_feature
0001.png,This is a first value of a text feature you added to your images
0002.png,This is a second value of a text feature you added to your images
0003.png,This is a third value of a text feature you added to your images

or using metadata.jsonl:

{"file_name": "0001.png", "additional_feature": "This is a first value of a text feature you added to your images"}
{"file_name": "0002.png", "additional_feature": "This is a second value of a text feature you added to your images"}
{"file_name": "0003.png", "additional_feature": "This is a third value of a text feature you added to your images"}

Here the file_name must be the name of the image file next to the metadata file. More generally, it must be the relative path from the directory containing the metadata to the image file.

It’s possible to point to more than one image in each row in your dataset, for example if both your input and output are images:

{"input_file_name": "0001.png", "output_file_name": "0001_output.png"}
{"input_file_name": "0002.png", "output_file_name": "0002_output.png"}
{"input_file_name": "0003.png", "output_file_name": "0003_output.png"}

You can also define lists of images. In that case you need to name the field file_names or *_file_names. Here is an example:

{"frames_file_names": ["0001_t0.png", "0001_t1.png"], label: "moving_up"}
{"frames_file_names": ["0002_t0.png", "0002_t1.png"], label: "moving_down"}
{"frames_file_names": ["0003_t0.png", "0003_t1.png"], label: "moving_right"}

Image captioning

Image captioning datasets have text describing an image. An example metadata.csv may look like:

file_name,text
0001.png,This is a golden retriever playing with a ball
0002.png,A german shepherd
0003.png,One chihuahua

Load the dataset with ImageFolder, and it will create a text column for the image captions:

>>> dataset = load_dataset("imagefolder", data_dir="/path/to/folder", split="train")
>>> dataset[0]["text"]
"This is a golden retriever playing with a ball"

Object detection

Object detection datasets have bounding boxes and categories identifying objects in an image. An example metadata.jsonl may look like:

{"file_name": "0001.png", "objects": {"bbox": [[302.0, 109.0, 73.0, 52.0]], "categories": [0]}}
{"file_name": "0002.png", "objects": {"bbox": [[810.0, 100.0, 57.0, 28.0]], "categories": [1]}}
{"file_name": "0003.png", "objects": {"bbox": [[160.0, 31.0, 248.0, 616.0], [741.0, 68.0, 202.0, 401.0]], "categories": [2, 2]}}

Load the dataset with ImageFolder, and it will create a objects column with the bounding boxes and the categories:

>>> dataset = load_dataset("imagefolder", data_dir="/path/to/folder", split="train")
>>> dataset[0]["objects"]
{"bbox": [[302.0, 109.0, 73.0, 52.0]], "categories": [0]}

Upload dataset to the Hub

Once you’ve created a dataset, you can share it to the Hub with the push_to_hub() method. Make sure you have the huggingface_hub library installed and you’re logged in to your Hugging Face account (see the Upload with Python tutorial for more details).

Upload your dataset with push_to_hub():

>>> from datasets import load_dataset

>>> dataset = load_dataset("imagefolder", data_dir="/path/to/folder", split="train")
>>> dataset.push_to_hub("stevhliu/my-image-captioning-dataset")

WebDataset

The WebDataset format is based on TAR archives and is suitable for big image datasets. Indeed you can group your images in TAR archives (e.g. 1GB of images per TAR archive) and have thousands of TAR archives:

folder/train/00000.tar
folder/train/00001.tar
folder/train/00002.tar
...

In the archives, each example is made of files sharing the same prefix:

e39871fd9fd74f55.jpg
e39871fd9fd74f55.json
f18b91585c4d3f3e.jpg
f18b91585c4d3f3e.json
ede6e66b2fb59aab.jpg
ede6e66b2fb59aab.json
ed600d57fcee4f94.jpg
ed600d57fcee4f94.json
...

You can put your images labels/captions/bounding boxes using JSON or text files for example.

Load your WebDataset and it will create on column per file suffix (here “jpg” and “json”):

>>> from datasets import load_dataset

>>> dataset = load_dataset("webdataset", data_dir="/path/to/folder", split="train")
>>> dataset[0]["json"]
{"bbox": [[302.0, 109.0, 73.0, 52.0]], "categories": [0]}

It’s also possible to have several images per example like this:

e39871fd9fd74f55.input.jpg
e39871fd9fd74f55.output.jpg
e39871fd9fd74f55.json
f18b91585c4d3f3e.input.jpg
f18b91585c4d3f3e.output.jpg
f18b91585c4d3f3e.json
...

For more details on the WebDataset format and the python library, please check the WebDataset documentation.