testbed/huggingface__datasets/docs/source/image_load.mdx

# Load image data

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

<Tip>

To work with image datasets, you need to have the `vision` dependency installed. Check out the [installation](./installation#vision) guide to learn how to install it.

</Tip>

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

```py
>>> from datasets import load_dataset, Image

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

<Tip warning={true}>

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.

</Tip>

For a guide on how to load any type of dataset, take a look at the <a class="underline decoration-sky-400 decoration-2 font-semibold" href="./loading">general loading guide</a>.

## Local files

You can load a dataset from the image path. Use the [`~Dataset.cast_column`] function to accept a column of image file paths, and decode it into a PIL image with the [`Image`] feature:
```py
>>> from datasets import 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:

```py
>>> 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 an `ImageFolder` dataset builder which does not require writing a custom dataloader. This makes `ImageFolder` ideal for quickly creating and loading image datasets with several thousand images for different 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`:

```py
>>> 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:

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

Some datasets have a metadata file (`metadata.csv`/`metadata.jsonl`) associated with it, containing other information about the data like bounding boxes, text captions, and labels. The metadata is automatically loaded when you call [`load_dataset`] and specify `imagefolder`. 

To ignore the information in the metadata file, set `drop_labels=False` in [`load_dataset`], and allow `ImageFolder` to automatically infer the label name from the directory name:

```py
>>> from datasets import load_dataset

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

<Tip>

For more information about creating your own `ImageFolder` dataset, take a look at the [Create an image dataset](./image_dataset) guide.

</Tip>