Shortcuts

Formatting Classification Targets

This guide details the different target formats supported by classification tasks in Flash. By default, the target format and any additional metadata (labels, num_classes, multi_label) will be inferred from your training data. You can override this behaviour by passing your own TargetFormatter using the target_formatter argument.

Single Label

Classification targets are described as single label (DataModule.multi_label = False) if each data sample corresponds to a single class.

Class Indexes

Targets formatted as class indexes are represented by a single number, e.g. train_targets = [0, 1, 0]. No labels will be inferred. The inferred num_classes is the maximum index plus one (we assume that class indexes are zero-based). Here’s an example:

>>> from flash.image import ImageClassificationData
>>> datamodule = ImageClassificationData.from_files(
...     train_files=["image_1.png", "image_2.png", "image_3.png"],
...     train_targets=[0, 1, 0],
...     transform_kwargs=dict(image_size=(128, 128)),
...     batch_size=2,
... )
>>> datamodule.num_classes
2
>>> datamodule.labels is None
True
>>> datamodule.multi_label
False

Alternatively, you can provide a SingleNumericTargetFormatter to override the behaviour. Here’s an example:

>>> from flash.image import ImageClassificationData
>>> from flash.core.data.utilities.classification import SingleNumericTargetFormatter
>>> datamodule = ImageClassificationData.from_files(
...     train_files=["image_1.png", "image_2.png", "image_3.png"],
...     train_targets=[0, 1, 0],
...     target_formatter=SingleNumericTargetFormatter(labels=["dog", "cat", "rabbit"]),
...     transform_kwargs=dict(image_size=(128, 128)),
...     batch_size=2,
... )
>>> datamodule.num_classes
3
>>> datamodule.labels
['dog', 'cat', 'rabbit']
>>> datamodule.multi_label
False

Labels

Targets formatted as labels are represented by a single string, e.g. train_targets = ["cat", "dog", "cat"]. The inferred labels will be the unique labels in the train targets sorted alphanumerically. The inferred num_classes is the number of labels. Here’s an example:

>>> from flash.image import ImageClassificationData
>>> datamodule = ImageClassificationData.from_files(
...     train_files=["image_1.png", "image_2.png", "image_3.png"],
...     train_targets=["cat", "dog", "cat"],
...     transform_kwargs=dict(image_size=(128, 128)),
...     batch_size=2,
... )
>>> datamodule.num_classes
2
>>> datamodule.labels
['cat', 'dog']
>>> datamodule.multi_label
False

Alternatively, you can provide a SingleLabelTargetFormatter to override the behaviour. Here’s an example:

>>> from flash.image import ImageClassificationData
>>> from flash.core.data.utilities.classification import SingleLabelTargetFormatter
>>> datamodule = ImageClassificationData.from_files(
...     train_files=["image_1.png", "image_2.png", "image_3.png"],
...     train_targets=["cat", "dog", "cat"],
...     target_formatter=SingleLabelTargetFormatter(labels=["dog", "cat", "rabbit"]),
...     transform_kwargs=dict(image_size=(128, 128)),
...     batch_size=2,
... )
>>> datamodule.num_classes
3
>>> datamodule.labels
['dog', 'cat', 'rabbit']
>>> datamodule.multi_label
False

One-hot Binaries

Targets formatted as one-hot binaries are represented by a binary list with a single index (the target class index) set to 1, e.g. train_targets = [[1, 0], [0, 1], [1, 0]]. No labels will be inferred. The inferred num_classes is the length of the binary list. Here’s an example:

>>> from flash.image import ImageClassificationData
>>> datamodule = ImageClassificationData.from_files(
...     train_files=["image_1.png", "image_2.png", "image_3.png"],
...     train_targets=[[1, 0], [0, 1], [1, 0]],
...     transform_kwargs=dict(image_size=(128, 128)),
...     batch_size=2,
... )
>>> datamodule.num_classes
2
>>> datamodule.labels is None
True
>>> datamodule.multi_label
False

Alternatively, you can provide a SingleBinaryTargetFormatter to override the behaviour. Here’s an example:

>>> from flash.image import ImageClassificationData
>>> from flash.core.data.utilities.classification import SingleBinaryTargetFormatter
>>> datamodule = ImageClassificationData.from_files(
...     train_files=["image_1.png", "image_2.png", "image_3.png"],
...     train_targets=[[1, 0], [0, 1], [1, 0]],
...     target_formatter=SingleLabelTargetFormatter(labels=["dog", "cat"]),
...     transform_kwargs=dict(image_size=(128, 128)),
...     batch_size=2,
... )
>>> datamodule.num_classes
2
>>> datamodule.labels
['dog', 'cat']
>>> datamodule.multi_label
False

Multi Label

Classification targets are described as multi label (DataModule.multi_label = True) if each data sample corresponds to zero or more (and perhaps many) classes.

Class Indexes

Targets formatted as multi label class indexes are represented by a list of class indexes, e.g. train_targets = [[0], [0, 1], [1, 2]]. No labels will be inferred. The inferred num_classes is the maximum target value plus one (we assume that targets are zero-based). Here’s an example:

>>> from flash.image import ImageClassificationData
>>> datamodule = ImageClassificationData.from_files(
...     train_files=["image_1.png", "image_2.png", "image_3.png"],
...     train_targets=[[0], [0, 1], [1, 2]],
...     transform_kwargs=dict(image_size=(128, 128)),
...     batch_size=2,
... )
>>> datamodule.num_classes
3
>>> datamodule.labels is None
True
>>> datamodule.multi_label
True

Alternatively, you can provide a MultiNumericTargetFormatter to override the behaviour. Here’s an example:

>>> from flash.image import ImageClassificationData
>>> from flash.core.data.utilities.classification import MultiNumericTargetFormatter
>>> datamodule = ImageClassificationData.from_files(
...     train_files=["image_1.png", "image_2.png", "image_3.png"],
...     train_targets=[[0], [0, 1], [1, 2]],
...     target_formatter=MultiNumericTargetFormatter(labels=["dog", "cat", "rabbit"]),
...     transform_kwargs=dict(image_size=(128, 128)),
...     batch_size=2,
... )
>>> datamodule.num_classes
3
>>> datamodule.labels
['dog', 'cat', 'rabbit']
>>> datamodule.multi_label
True

Labels

Targets formatted as multi label are represented by a list of strings, e.g. train_targets = [["cat"], ["cat", "dog"], ["dog", "rabbit"]]. The inferred labels will be the unique labels in the train targets sorted alphanumerically. The inferred num_classes is the number of labels. Here’s an example:

>>> from flash.image import ImageClassificationData
>>> datamodule = ImageClassificationData.from_files(
...     train_files=["image_1.png", "image_2.png", "image_3.png"],
...     train_targets=[["cat"], ["cat", "dog"], ["dog", "rabbit"]],
...     transform_kwargs=dict(image_size=(128, 128)),
...     batch_size=2,
... )
>>> datamodule.num_classes
3
>>> datamodule.labels
['cat', 'dog', 'rabbit']
>>> datamodule.multi_label
True

Alternatively, you can provide a MultiLabelTargetFormatter to override the behaviour. Here’s an example:

>>> from flash.image import ImageClassificationData
>>> from flash.core.data.utilities.classification import MultiLabelTargetFormatter
>>> datamodule = ImageClassificationData.from_files(
...     train_files=["image_1.png", "image_2.png", "image_3.png"],
...     train_targets=[["cat"], ["cat", "dog"], ["dog", "rabbit"]],
...     target_formatter=MultiLabelTargetFormatter(labels=["dog", "cat", "rabbit"]),
...     transform_kwargs=dict(image_size=(128, 128)),
...     batch_size=2,
... )
>>> datamodule.num_classes
3
>>> datamodule.labels
['dog', 'cat', 'rabbit']
>>> datamodule.multi_label
True

Comma Delimited

Targets formatted as comma delimited mutli label are given as comma delimited strings, e.g. train_targets = ["cat", "cat,dog", "dog,rabbit"]. The inferred labels will be the unique labels in the train targets sorted alphanumerically. The inferred num_classes is the number of labels. Here’s an example:

>>> from flash.image import ImageClassificationData
>>> datamodule = ImageClassificationData.from_files(
...     train_files=["image_1.png", "image_2.png", "image_3.png"],
...     train_targets=["cat", "cat,dog", "dog,rabbit"],
...     transform_kwargs=dict(image_size=(128, 128)),
...     batch_size=2,
... )
>>> datamodule.num_classes
3
>>> datamodule.labels
['cat', 'dog', 'rabbit']
>>> datamodule.multi_label
True

Alternatively, you can provide a CommaDelimitedMultiLabelTargetFormatter to override the behaviour. Here’s an example:

>>> from flash.image import ImageClassificationData
>>> from flash.core.data.utilities.classification import CommaDelimitedMultiLabelTargetFormatter
>>> datamodule = ImageClassificationData.from_files(
...     train_files=["image_1.png", "image_2.png", "image_3.png"],
...     train_targets=["cat", "cat,dog", "dog,rabbit"],
...     target_formatter=CommaDelimitedMultiLabelTargetFormatter(labels=["dog", "cat", "rabbit"]),
...     transform_kwargs=dict(image_size=(128, 128)),
...     batch_size=2,
... )
>>> datamodule.num_classes
3
>>> datamodule.labels
['dog', 'cat', 'rabbit']
>>> datamodule.multi_label
True

Space Delimited

Targets formatted as space delimited mutli label are given as space delimited strings, e.g. train_targets = ["cat", "cat dog", "dog rabbit"]. The inferred labels will be the unique labels in the train targets sorted alphanumerically. The inferred num_classes is the number of labels. Here’s an example:

>>> from flash.image import ImageClassificationData
>>> datamodule = ImageClassificationData.from_files(
...     train_files=["image_1.png", "image_2.png", "image_3.png"],
...     train_targets=["cat", "cat dog", "dog rabbit"],
...     transform_kwargs=dict(image_size=(128, 128)),
...     batch_size=2,
... )
>>> datamodule.num_classes
3
>>> datamodule.labels
['cat', 'dog', 'rabbit']
>>> datamodule.multi_label
True

Alternatively, you can provide a SpaceDelimitedTargetFormatter to override the behaviour. Here’s an example:

>>> from flash.image import ImageClassificationData
>>> from flash.core.data.utilities.classification import SpaceDelimitedTargetFormatter
>>> datamodule = ImageClassificationData.from_files(
...     train_files=["image_1.png", "image_2.png", "image_3.png"],
...     train_targets=["cat", "cat dog", "dog rabbit"],
...     target_formatter=SpaceDelimitedTargetFormatter(labels=["dog", "cat", "rabbit"]),
...     transform_kwargs=dict(image_size=(128, 128)),
...     batch_size=2,
... )
>>> datamodule.num_classes
3
>>> datamodule.labels
['dog', 'cat', 'rabbit']
>>> datamodule.multi_label
True

Multi-hot Binaries

Targets formatted as one-hot binaries are represented by a binary list with a zero or more indices (the target class indices) set to 1, e.g. train_targets = [[1, 0, 0], [1, 1, 0], [0, 1, 1]]. No labels will be inferred. The inferred num_classes is the length of the binary list. Here’s an example:

>>> from flash.image import ImageClassificationData
>>> datamodule = ImageClassificationData.from_files(
...     train_files=["image_1.png", "image_2.png", "image_3.png"],
...     train_targets=[[1, 0, 0], [1, 1, 0], [0, 1, 1]],
...     transform_kwargs=dict(image_size=(128, 128)),
...     batch_size=2,
... )
>>> datamodule.num_classes
3
>>> datamodule.labels is None
True
>>> datamodule.multi_label
True

Alternatively, you can provide a MultiBinaryTargetFormatter to override the behaviour. Here’s an example:

>>> from flash.image import ImageClassificationData
>>> from flash.core.data.utilities.classification import MultiBinaryTargetFormatter
>>> datamodule = ImageClassificationData.from_files(
...     train_files=["image_1.png", "image_2.png", "image_3.png"],
...     train_targets=[[1, 0, 0], [1, 1, 0], [0, 1, 1]],
...     target_formatter=MultiBinaryTargetFormatter(labels=["dog", "cat", "rabbit"]),
...     transform_kwargs=dict(image_size=(128, 128)),
...     batch_size=2,
... )
>>> datamodule.num_classes
3
>>> datamodule.labels
['dog', 'cat', 'rabbit']
>>> datamodule.multi_label
True

Multi-label Soft Targets

Multi-label soft targets are represented by a list of floats, e.g. train_targets = [[0.1, 0, 0], [0.9, 0.7, 0], [0, 0.5, 0.6]]. No labels will be inferred. The inferred num_classes is the length of the list. Here’s an example:

>>> from flash.image import ImageClassificationData
>>> datamodule = ImageClassificationData.from_files(
...     train_files=["image_1.png", "image_2.png", "image_3.png"],
...     train_targets=[[0.1, 0, 0], [0.9, 0.7, 0], [0, 0.5, 0.6]],
...     transform_kwargs=dict(image_size=(128, 128)),
...     batch_size=2,
... )
>>> datamodule.num_classes
3
>>> datamodule.labels is None
True
>>> datamodule.multi_label
True

Alternatively, you can provide a MultiSoftTargetFormatter to override the behaviour. Here’s an example:

>>> from flash.image import ImageClassificationData
>>> from flash.core.data.utilities.classification import MultiSoftTargetFormatter
>>> datamodule = ImageClassificationData.from_files(
...     train_files=["image_1.png", "image_2.png", "image_3.png"],
...     train_targets=[[0.1, 0, 0], [0.9, 0.7, 0], [0, 0.5, 0.6]],
...     target_formatter=MultiSoftTargetFormatter(labels=["dog", "cat", "rabbit"]),
...     transform_kwargs=dict(image_size=(128, 128)),
...     batch_size=2,
... )
>>> datamodule.num_classes
3
>>> datamodule.labels
['dog', 'cat', 'rabbit']
>>> datamodule.multi_label
True
Read the Docs v: latest
Versions
latest
stable
0.8.2
0.8.1.post0
0.8.1
0.8.0
0.7.5
0.7.4
0.7.3
0.7.2
0.7.1
0.7.0
0.6.0
0.5.2
0.5.1
0.5.0
0.4.0
0.3.2
0.3.1
0.3.0
0.2.3
0.2.2
0.2.1
0.2.0
0.1.0post1
Downloads
html
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.