burn_core::data::dataset::vision

Struct ImageFolderDataset

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pub struct ImageFolderDataset { /* private fields */ }
Available on crate features std and dataset only.
Expand description

A generic dataset to load images from disk.

Implementations§

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impl ImageFolderDataset

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pub fn new_classification<P>( root: P, ) -> Result<ImageFolderDataset, ImageLoaderError>
where P: AsRef<Path>,

Create an image classification dataset from the root folder.

§Arguments
  • root - Dataset root folder.
§Returns

A new dataset instance.

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pub fn new_classification_with<P, S>( root: P, extensions: &[S], ) -> Result<ImageFolderDataset, ImageLoaderError>
where P: AsRef<Path>, S: AsRef<str>,

Create an image classification dataset from the root folder. The included images are filtered based on the provided extensions.

§Arguments
  • root - Dataset root folder.
  • extensions - List of allowed extensions.
§Returns

A new dataset instance.

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pub fn new_classification_with_items<P, S>( items: Vec<(P, String)>, classes: &[S], ) -> Result<ImageFolderDataset, ImageLoaderError>
where P: AsRef<Path>, S: AsRef<str>,

Create an image classification dataset with the specified items.

§Arguments
  • items - List of dataset items, each item represented by a tuple (image path, label).
  • classes - Dataset class names.
§Returns

A new dataset instance.

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pub fn new_multilabel_classification_with_items<P, S>( items: Vec<(P, Vec<String>)>, classes: &[S], ) -> Result<ImageFolderDataset, ImageLoaderError>
where P: AsRef<Path>, S: AsRef<str>,

Create a multi-label image classification dataset with the specified items.

§Arguments
  • items - List of dataset items, each item represented by a tuple (image path, labels).
  • classes - Dataset class names.
§Returns

A new dataset instance.

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pub fn new_segmentation_with_items<P, S>( items: Vec<(P, P)>, classes: &[S], ) -> Result<ImageFolderDataset, ImageLoaderError>
where P: AsRef<Path>, S: AsRef<str>,

Create an image segmentation dataset with the specified items.

§Arguments
  • items - List of dataset items, each item represented by a tuple (image path, annotation path).
  • classes - Dataset class names.
§Returns

A new dataset instance.

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pub fn new_coco_detection<A, I>( annotations_json: A, images_path: I, ) -> Result<ImageFolderDataset, ImageLoaderError>
where A: AsRef<Path>, I: AsRef<Path>,

Create a COCO detection dataset based on the annotations JSON and image directory.

§Arguments

  • annotations_json - Path to the JSON file containing annotations in COCO format (for example instances_train2017.json).

  • images_path - Path containing the images matching the annotations JSON.

§Returns

A new dataset instance.

Trait Implementations§

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impl Dataset<ImageDatasetItem> for ImageFolderDataset

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fn get(&self, index: usize) -> Option<ImageDatasetItem>

Gets the item at the given index.
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fn len(&self) -> usize

Gets the number of items in the dataset.
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fn is_empty(&self) -> bool

Checks if the dataset is empty.
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fn iter(&self) -> DatasetIterator<'_, I> ⓘ
where Self: Sized,

Returns an iterator over the dataset.

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fn windows(&self, size: usize) -> WindowsIterator<'_, I> ⓘ

Is empty if the Dataset is shorter than size.

§Panics

Panics if size is 0.

§Examples
use crate::burn_dataset::{
   transform::{Windows, WindowsDataset},
   Dataset, InMemDataset,
};

let items = [1, 2, 3, 4].to_vec();
let dataset = InMemDataset::new(items.clone());

for window in dataset.windows(2) {
 // do sth with window
}
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