DeiTImageClassifier classkeras_hub.models.DeiTImageClassifier(
backbone,
num_classes,
preprocessor=None,
pooling="token",
activation=None,
dropout=0.0,
head_dtype=None,
**kwargs
)
DeiT image classification task.
DeiTImageClassifier tasks wrap a keras_hub.models.DeiTBackbone and
a keras_hub.models.Preprocessor to create a model that can be used for
image classification. DeiTImageClassifier tasks take an additional
num_classes argument, controlling the number of predicted output classes.
To fine-tune with fit(), pass a dataset containing tuples of (x, y)
labels where x is a string and y is a integer from [0, num_classes).
Not that unlike keras_hub.model.ImageClassifier, the DeiTImageClassifier
we pluck out cls_token which is first seqence from the backbone.
Arguments
keras_hub.models.DeiTBackbone instance or a keras.Model.None, a keras_hub.models.Preprocessor instance,
a keras.Layer instance, or a callable. If None no preprocessing
will be applied to the inputs."token": A single vector (class token) representing the
overall image features.
"gap": A single vector representing the average features
across the spatial dimensions.None, str, or callable. The activation function to use on
the Dense layer. Set activation=None to return the output
logits. Defaults to None.None, str, or keras.mixed_precision.DTypePolicy. The
dtype to use for the classification head's computations and weights.Examples
Call predict() to run inference.
# Load preset and train
images = np.random.randint(0, 256, size=(2, 384, 384, 3))
classifier = keras_hub.models.DeiTImageClassifier.from_preset(
"hf://facebook/deit-base-distilled-patch16-384"
)
classifier.predict(images)
Call fit() on a single batch.
# Load preset and train
images = np.random.randint(0, 256, size=(2, 384, 384, 3))
labels = [0, 3]
classifier = keras_hub.models.DeiTImageClassifier.from_preset(
"hf://facebook/deit-base-distilled-patch16-384"
)
classifier.fit(x=images, y=labels, batch_size=2)
Call fit() with custom loss, optimizer and backbone.
classifier = keras_hub.models.DeiTImageClassifier.from_preset(
"hf://facebook/deit-base-distilled-patch16-384"
)
classifier.compile(
loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
optimizer=keras.optimizers.Adam(5e-5),
)
classifier.backbone.trainable = False
classifier.fit(x=images, y=labels, batch_size=2)
Custom backbone.
images = np.random.randint(0, 256, size=(2, 384, 384, 3))
labels = [0, 3]
backbone = keras_hub.models.DeiTBackbone(
image_shape = (384, 384, 3),
patch_size=16,
num_layers=6,
num_heads=3,
hidden_dim=768,
intermediate_dim=2048
)
classifier = keras_hub.models.DeiTImageClassifier(
backbone=backbone,
num_classes=4,
)
classifier.fit(x=images, y=labels, batch_size=2)
from_preset methodDeiTImageClassifier.from_preset(preset, load_weights=True, **kwargs)
Instantiate a keras_hub.models.Task from a model preset.
A preset is a directory of configs, weights and other file assets used
to save and load a pre-trained model. The preset can be passed as
one of:
'bert_base_en''kaggle://user/bert/keras/bert_base_en''hf://user/bert_base_en''./bert_base_en'For any Task subclass, you can run cls.presets.keys() to list all
built-in presets available on the class.
This constructor can be called in one of two ways. Either from a task
specific base class like keras_hub.models.CausalLM.from_preset(), or
from a model class like
keras_hub.models.BertTextClassifier.from_preset().
If calling from the a base class, the subclass of the returning object
will be inferred from the config in the preset directory.
Arguments
True, saved weights will be loaded into
the model architecture. If False, all weights will be
randomly initialized.Examples
# Load a Gemma generative task.
causal_lm = keras_hub.models.CausalLM.from_preset(
"gemma_2b_en",
)
# Load a Bert classification task.
model = keras_hub.models.TextClassifier.from_preset(
"bert_base_en",
num_classes=2,
)
| Preset | Parameters | Description |
|---|---|---|
| deit_tiny_distilled_patch16_224_imagenet | 5.52M | DeiT-T16 model pre-trained on the ImageNet 1k dataset with image resolution of 224x224 |
| deit_small_distilled_patch16_224_imagenet | 21.67M | DeiT-S16 model pre-trained on the ImageNet 1k dataset with image resolution of 224x224 |
| deit_base_distilled_patch16_224_imagenet | 85.80M | DeiT-B16 model pre-trained on the ImageNet 1k dataset with image resolution of 224x224 |
| deit_base_distilled_patch16_384_imagenet | 86.09M | DeiT-B16 model pre-trained on the ImageNet 1k dataset with image resolution of 384x384 |
backbone propertykeras_hub.models.DeiTImageClassifier.backbone
A keras_hub.models.Backbone model with the core architecture.
preprocessor propertykeras_hub.models.DeiTImageClassifier.preprocessor
A keras_hub.models.Preprocessor layer used to preprocess input.