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Load image data

Image datasets are loaded from the image column, which contains a PIL object.

To work with image datasets, you need to have the vision dependency installed. Check out the installation guide to learn how to install it.

When you load an image dataset and call the image column, the Image feature automatically decodes the PIL object into an image:

>>> from datasets import load_dataset, Image

>>> dataset = load_dataset("beans", split="train")
>>> dataset[0]["image"]

Index into an image dataset using the row index first and then the image column - dataset[0]["image"] - to avoid decoding and resampling all the image objects in the dataset. Otherwise, this can be a slow and time-consuming process if you have a large dataset.

For a guide on how to load any type of dataset, take a look at the general loading guide.

Local files

You can load a dataset from the image path. Use the cast_column() function to accept a column of image file paths, and decode it into a PIL image with the Image feature:

>>> from datasets import load_dataset, Image

>>> dataset = Dataset.from_dict({"image": ["path/to/image_1", "path/to/image_2", ..., "path/to/image_n"]}).cast_column("image", Image())
>>> dataset[0]["image"]
<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=1200x215 at 0x15E6D7160>]

If you only want to load the underlying path to the image dataset without decoding the image object, set decode=False in the Image feature:

>>> dataset = load_dataset("beans", split="train").cast_column("image", Image(decode=False))
>>> dataset[0]["image"]
{'bytes': None,
 'path': '/root/.cache/huggingface/datasets/downloads/extracted/b0a21163f78769a2cf11f58dfc767fb458fc7cea5c05dccc0144a2c0f0bc1292/train/bean_rust/bean_rust_train.29.jpg'}

ImageFolder

You can also load a dataset with a ImageFolder dataset builder. It does not require writing a custom dataloader, making it useful for quickly loading a dataset for certain vision tasks. Your image dataset structure should look like this:

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

Load your dataset by specifying imagefolder and the directory of your dataset in data_dir:

>>> from datasets import load_dataset

>>> dataset = load_dataset("imagefolder", data_dir="/path/to/folder")
>>> dataset["train"][0]
{"image": <PIL.PngImagePlugin.PngImageFile image mode=RGBA size=1200x215 at 0x15E6D7160>, "label": 0}

>>> dataset["train"][-1]
{"image": <PIL.PngImagePlugin.PngImageFile image mode=RGBA size=1200x215 at 0x15E8DAD30>, "label": 1}

Load remote datasets from their URLs with the data_files parameter:

>>> dataset = load_dataset("imagefolder", data_files="https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip", split="train")

ImageFolder creates a label column, and the label name is based on the directory name. To ignore the label column, set drop_labels=False as defined in ImageFolderConfig.

ImageFolder with metadata

Metadata associated with your dataset can also be loaded, extending the utility of ImageFolder to additional vision tasks like image captioning and object detection. Make sure your dataset has a metadata.jsonl file:

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

Your metadata.jsonl file must have a file_name column which links image files with their metadata:

{"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"}

If metadata files are present, the inferred labels based on the directory name are dropped by default. To include those labels, set drop_labels=False in load_dataset.

Image captioning

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

{"file_name": "0001.png", "text": "This is a golden retriever playing with a ball"}
{"file_name": "0002.png", "text": "A german shepherd"}
{"file_name": "0003.png", "text": "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]}