Reference for ultralytics/data/dataset.py
Note
Full source code for this file is available at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/dataset.py. Help us fix any issues you see by submitting a Pull Request 🛠️. Thank you 🙏!
ultralytics.data.dataset.YOLODataset
Bases: BaseDataset
Dataset class for loading object detection and/or segmentation labels in YOLO format.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
data |
dict
|
A dataset YAML dictionary. Defaults to None. |
None
|
use_segments |
bool
|
If True, segmentation masks are used as labels. Defaults to False. |
False
|
use_keypoints |
bool
|
If True, keypoints are used as labels. Defaults to False. |
False
|
Returns:
Type | Description |
---|---|
Dataset
|
A PyTorch dataset object that can be used for training an object detection model. |
Source code in ultralytics/data/dataset.py
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|
build_transforms(hyp=None)
Builds and appends transforms to the list.
Source code in ultralytics/data/dataset.py
cache_labels(path=Path('./labels.cache'))
Cache dataset labels, check images and read shapes. Args: path (Path): path where to save the cache file (default: Path('./labels.cache')). Returns: (dict): labels.
Source code in ultralytics/data/dataset.py
close_mosaic(hyp)
Sets mosaic, copy_paste and mixup options to 0.0 and builds transformations.
Source code in ultralytics/data/dataset.py
collate_fn(batch)
staticmethod
Collates data samples into batches.
Source code in ultralytics/data/dataset.py
get_labels()
Returns dictionary of labels for YOLO training.
Source code in ultralytics/data/dataset.py
update_labels_info(label)
custom your label format here.
Source code in ultralytics/data/dataset.py
ultralytics.data.dataset.ClassificationDataset
Bases: ImageFolder
YOLO Classification Dataset.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
root |
str
|
Dataset path. |
required |
Attributes:
Name | Type | Description |
---|---|---|
cache_ram |
bool
|
True if images should be cached in RAM, False otherwise. |
cache_disk |
bool
|
True if images should be cached on disk, False otherwise. |
samples |
list
|
List of samples containing file, index, npy, and im. |
torch_transforms |
callable
|
torchvision transforms applied to the dataset. |
album_transforms |
callable
|
Albumentations transforms applied to the dataset if augment is True. |
Source code in ultralytics/data/dataset.py
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|
__getitem__(i)
Returns subset of data and targets corresponding to given indices.
Source code in ultralytics/data/dataset.py
__init__(root, args, augment=False, cache=False, prefix='')
Initialize YOLO object with root, image size, augmentations, and cache settings.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
root |
str
|
Dataset path. |
required |
args |
Namespace
|
Argument parser containing dataset related settings. |
required |
augment |
bool
|
True if dataset should be augmented, False otherwise. Defaults to False. |
False
|
cache |
bool | str | optional
|
Cache setting, can be True, False, 'ram' or 'disk'. Defaults to False. |
False
|
Source code in ultralytics/data/dataset.py
verify_images()
Verify all images in dataset.
Source code in ultralytics/data/dataset.py
ultralytics.data.dataset.SemanticDataset
Bases: BaseDataset
Source code in ultralytics/data/dataset.py
ultralytics.data.dataset.load_dataset_cache_file(path)
Load an Ultralytics *.cache dictionary from path.
Source code in ultralytics/data/dataset.py
ultralytics.data.dataset.save_dataset_cache_file(prefix, path, x)
Save an Ultralytics dataset *.cache dictionary x to path.