MAITE Datasets

MAITE Datasets are a collection of public datasets wrapped in a MAITE compliant format.

Installation

To install and use maite-datasets you can use pip:

pip install maite-datasets

Extras

Optional features are installed as extras, e.g. pip install maite-datasets[tqdm].

Available Downloadable Datasets

The paired RGB-infrared datasets (DroneVehicle, M3FD) stack the infrared channel onto the RGB image. MilitaryAircraft and MilitaryVehicles expose a hierarchy attribute describing their class ontology. Every dataset accepts lazy=True to defer image decoding until the image is actually read, which keeps metadata-only passes over large datasets cheap.

Usage

Here is an example of how to import MNIST for usage with your workflow.

>>> from maite_datasets.image_classification import MNIST

>>> mnist = MNIST(root="data", download=True)
>>> print(mnist)
MNIST Dataset
-------------
    Corruption: None
    Transforms: []
    Image_set: train
    Metadata: {'id': 'MNIST_train', 'index2label': {0: 'zero', 1: 'one', 2: 'two', 3: 'three', 4: 'four', 5: 'five', 6: 'six', 7: 'seven', 8: 'eight', 9: 'nine'}, 'split': 'train'}
    Path: /home/user/maite-datasets/data/mnist
    Size: 60000

>>> print("tuple("+", ".join([str(type(t)) for t in mnist[0]])+")")
tuple(<class 'numpy.ndarray'>, <class 'numpy.ndarray'>, <class 'dict'>)

Dataset Wrappers

Wrappers provide a way to convert datasets to allow usage of tools within specific backend frameworks.

Torchvision

TorchvisionWrapper is a convenience class that wraps any of the datasets and provides the capability to apply torchvision transforms to the dataset.

NOTE: TorchvisionWrapper requires torch and torchvision to be installed.

>>> from maite_datasets.object_detection import MILCO

>>> milco = MILCO(root="data", download=True)
>>> print(milco)
MILCO Dataset
-------------
    Transforms: []
    Image Set: train
    Metadata: {'id': 'MILCO_train', 'index2label': {0: 'MILCO', 1: 'NOMBO'}, 'split': 'train'}
    Path: /home/user/maite-datasets/data/milco
    Size: 261

>>> print(f"type={milco[0][0].__class__.__name__}, shape={milco[0][0].shape}")
type=ndarray, shape=(3, 1024, 1024)

>>> print(milco[0][1].boxes[0])
[ 75. 217. 130. 247.]

>>> from maite_datasets.wrappers import TorchvisionWrapper
>>> from torchvision.transforms.v2 import Resize

>>> milco_torch = TorchvisionWrapper(milco, transforms=Resize(224))
>>> print(milco_torch)
Torchvision Wrapped MILCO Dataset
---------------------------
    Transforms: Resize(size=[224], interpolation=InterpolationMode.BILINEAR, antialias=True)

MILCO Dataset
-------------
    Transforms: []
    Image Set: train
    Metadata: {'id': 'MILCO_train', 'index2label': {0: 'MILCO', 1: 'NOMBO'}, 'split': 'train'}
    Path: /home/user/maite-datasets/data/milco
    Size: 261

>>> print(f"type={milco_torch[0][0].__class__.__name__}, shape={milco_torch[0][0].shape}")
type=Image, shape=torch.Size([3, 224, 224])

>>> print(milco_torch[0][1].boxes[0])
tensor([16.4062, 47.4688, 28.4375, 54.0312], dtype=torch.float64)

Dataset Adapters

Adapters provide a way to read in datasets from other popular formats.

Huggingface

Hugging face datasets can be adapted into MAITE compliant format using the from_huggingface adapter.

>>> from datasets import load_dataset
>>> from maite_datasets.adapters import from_huggingface

>>> cppe5 = load_dataset("cppe-5")
>>> m_cppe5 = from_huggingface(cppe5["train"])
>>> print(m_cppe5)
HFObjectDetection Dataset
-------------------------
    Source: Dataset({
    features: ['image_id', 'image', 'width', 'height', 'objects'],
    num_rows: 1000
})
    Metadata: {'id': 'cppe-5', 'index2label': {0: 'Coverall', 1: 'Face_Shield', 2: 'Gloves', 3: 'Goggles', 4: 'Mask'}, 'description': '', 'citation': '', 'homepage': '', 'license': '', 'features': {'image_id': Value('int64'), 'image': Image(mode=None, decode=True), 'width': Value('int32'), 'height': Value('int32'), 'objects': {'id': List(Value('int64')), 'area': List(Value('int64')), 'bbox': List(List(Value('float32'), length=4)), 'category': List(ClassLabel(names=['Coverall', 'Face_Shield', 'Gloves', 'Goggles', 'Mask']))}}, 'post_processed': None, 'supervised_keys': None, 'builder_name': 'parquet', 'dataset_name': 'cppe-5', 'config_name': 'default', 'version': 0.0.0, 'splits': {'train': SplitInfo(name='train', num_bytes=240478590, num_examples=1000, shard_lengths=None, dataset_name='cppe-5'), 'test': SplitInfo(name='test', num_bytes=4172706, num_examples=29, shard_lengths=None, dataset_name='cppe-5')}, 'download_checksums': {'hf://datasets/cppe-5@66f6a5efd474e35bd7cb94bf15dea27d4c6ad3f8/data/train-00000-of-00001.parquet': {'num_bytes': 237015519, 'checksum': None}, 'hf://datasets/cppe-5@66f6a5efd474e35bd7cb94bf15dea27d4c6ad3f8/data/test-00000-of-00001.parquet': {'num_bytes': 4137134, 'checksum': None}}, 'download_size': 241152653, 'post_processing_size': None, 'dataset_size': 244651296, 'size_in_bytes': 485803949}

>>> image = m_cppe5[0][0]
>>> print(f"type={image.__class__.__name__}, shape={image.shape}")
type=ndarray, shape=(3, 663, 943)

>>> target = m_cppe5[0][1]
>>> print(f"box={target.boxes[0]}, label={target.labels[0]}")
box=[302.0, 109.0, 73.0, 52.0], label=4

>>> print(m_cppe5[0][2])
{'id': [114, 115, 116, 117], 'image_id': 15, 'width': 943, 'height': 663, 'area': [3796, 1596, 152768, 81002]}

Additional Information

For more information on the MAITE protocol, check out their documentation.

Acknowledgement

CDAO Funding Acknowledgement

This material is based upon work supported by the Chief Digital and Artificial Intelligence Office under Contract No. W519TC-23-9-2033. The views and conclusions contained herein are those of the author(s) and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the U.S. Government.