> For the complete documentation index, see [llms.txt](https://boinc-ai.gitbook.io/datasets/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://boinc-ai.gitbook.io/datasets/how-to-guides/vision/load-image-data.md). # Load image data ## 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](https://huggingface.co/docs/datasets/installation#vision) guide to learn how to install it. When you load an image dataset and call the `image` column, the [Image](https://huggingface.co/docs/datasets/v2.14.5/en/package_reference/main_classes#datasets.Image) feature automatically decodes the PIL object into an image: Copied ``` >>> 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](https://huggingface.co/docs/datasets/loading). ### Local files You can load a dataset from the image path. Use the [cast\_column()](https://huggingface.co/docs/datasets/v2.14.5/en/package_reference/main_classes#datasets.Dataset.cast_column) function to accept a column of image file paths, and decode it into a PIL image with the [Image](https://huggingface.co/docs/datasets/v2.14.5/en/package_reference/main_classes#datasets.Image) feature: Copied ``` >>> 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"] ] ``` If you only want to load the underlying path to the image dataset without decoding the image object, set `decode=False` in the [Image](https://huggingface.co/docs/datasets/v2.14.5/en/package_reference/main_classes#datasets.Image) feature: Copied ``` >>> 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: Copied ``` 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`: Copied ``` >>> from datasets import load_dataset >>> dataset = load_dataset("imagefolder", data_dir="/path/to/folder") >>> dataset["train"][0] {"image": , "label": 0} >>> dataset["train"][-1] {"image": , "label": 1} ``` Load remote datasets from their URLs with the `data_files` parameter: Copied ``` >>> 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()](https://huggingface.co/docs/datasets/v2.14.5/en/package_reference/loading_methods#datasets.load_dataset) and specify `imagefolder`. To ignore the information in the metadata file, set `drop_labels=False` in [load\_dataset()](https://huggingface.co/docs/datasets/v2.14.5/en/package_reference/loading_methods#datasets.load_dataset), and allow `ImageFolder` to automatically infer the label name from the directory name: Copied ``` >>> from datasets import load_dataset >>> dataset = load_dataset("imagefolder", data_dir="/path/to/folder", drop_labels=False) ``` For more information about creating your own `ImageFolder` dataset, take a look at the [Create an image dataset](https://huggingface.co/docs/datasets/image_dataset) guide. --- # Agent Instructions This documentation is published with GitBook. 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