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 ~flash.core.data.utilities.classification.TargetFormatter using the target_formatter argument.
targets
import numpy as np from PIL import Image
rand_image = Image.fromarray(np.random.randint(0, 255, (64, 64, 3), dtype="uint8")) _ = [rand_image.save(f"image{i}.png") for i in range(1, 4)]
Classification targets are described as single label (DataModule.multi_label = False) if each data sample corresponds to a single class.
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:
targets
>>> from flash import Trainer >>> from flash.image import ImageClassifier, 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 ~flash.core.data.utilities.classification.SingleNumericTargetFormatter to override the behaviour. Here's an example:
targets
>>> from flash import Trainer >>> from flash.image import ImageClassifier, 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
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:
targets
>>> from flash import Trainer >>> from flash.image import ImageClassifier, 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 ~flash.core.data.utilities.classification.SingleLabelTargetFormatter to override the behaviour. Here's an example:
targets
>>> from flash import Trainer >>> from flash.image import ImageClassifier, 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
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:
targets
>>> from flash import Trainer >>> from flash.image import ImageClassifier, 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 ~flash.core.data.utilities.classification.SingleBinaryTargetFormatter to override the behaviour. Here's an example:
targets
>>> from flash import Trainer >>> from flash.image import ImageClassifier, 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
Classification targets are described as multi label (DataModule.multi_label = True) if each data sample corresponds to zero or more (and perhaps many) classes.
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:
targets
>>> from flash import Trainer >>> from flash.image import ImageClassifier, 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 ~flash.core.data.utilities.classification.MultiNumericTargetFormatter to override the behaviour. Here's an example:
targets
>>> from flash import Trainer >>> from flash.image import ImageClassifier, 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
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:
targets
>>> from flash import Trainer >>> from flash.image import ImageClassifier, 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 ~flash.core.data.utilities.classification.MultiLabelTargetFormatter to override the behaviour. Here's an example:
targets
>>> from flash import Trainer >>> from flash.image import ImageClassifier, 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
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:
targets
>>> from flash import Trainer >>> from flash.image import ImageClassifier, 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 ~flash.core.data.utilities.classification.CommaDelimitedMultiLabelTargetFormatter to override the behaviour. Here's an example:
targets
>>> from flash import Trainer >>> from flash.image import ImageClassifier, 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
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:
targets
>>> from flash import Trainer >>> from flash.image import ImageClassifier, 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 ~flash.core.data.utilities.classification.SpaceDelimitedTargetFormatter to override the behaviour. Here's an example:
targets
>>> from flash import Trainer >>> from flash.image import ImageClassifier, 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
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:
targets
>>> from flash import Trainer >>> from flash.image import ImageClassifier, 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 ~flash.core.data.utilities.classification.MultiBinaryTargetFormatter to override the behaviour. Here's an example:
targets
>>> from flash import Trainer >>> from flash.image import ImageClassifier, 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