This section provides detailed documentation for all datasets available in stable-datasets.
stable-datasets provides easy access to a wide variety of datasets for machine learning research, with a focus on stability and reproducibility. Each dataset page includes:
- Example Samples: Visual examples or data snippets from the dataset
- Dataset Details: Number of classes, target types, and data specifications
- Data Structure: Keys and data types returned when accessing the dataset
- Usage Examples: Code snippets showing how to load and use the dataset
- Related Datasets: Links to similar or derived datasets
- Citation: The original paper to cite when using the dataset
All datasets can be loaded using the same consistent API:
from stable_datasets.images.<dataset_module> import <DatasetClass>
# First run will download + prepare cache, then return the split as a HF Dataset
ds = <DatasetClass>(split="train")
# If you omit the split (split=None), you get a DatasetDict with all available splits
ds_all = <DatasetClass>(split=None)
# Access individual examples
sample = ds[0]
print(sample.keys()) # e.g., {"image", "label"}
# Optional: make it PyTorch-friendly
ds_torch = ds.with_format("torch").. toctree:: :maxdepth: 1 :caption: Image Classification Datasets arabic_characters arabic_digits cifar10 cifar100 cifar10_c cifar100_c cars196 clevr dtd fashion_mnist k_mnist medmnist awa2 beans stl10 tiny_imagenet tiny_imagenet_c e_mnist fgvc_aircraft flowers102 food101 cub200 country211 galaxy10 hasy_v2 face_pointing rock_paper_scissor linnaeus5
Note
Documentation is being added progressively, as datasets are ready for usage. Please only use datasets found in the documentation.