ConvNeXtTiny
functionkeras.applications.ConvNeXtTiny(
model_name="convnext_tiny",
include_top=True,
include_preprocessing=True,
weights="imagenet",
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
classifier_activation="softmax",
)
Instantiates the ConvNeXtTiny architecture.
References
For image classification use cases, see this page for detailed examples. For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.
The base
, large
, and xlarge
models were first pre-trained on the
ImageNet-21k dataset and then fine-tuned on the ImageNet-1k dataset. The
pre-trained parameters of the models were assembled from the
official repository. To get a
sense of how these parameters were converted to Keras compatible parameters,
please refer to
this repository.
Note: Each Keras Application expects a specific kind of input preprocessing.
For ConvNeXt, preprocessing is included in the model using a Normalization
layer. ConvNeXt models expect their inputs to be float or uint8 tensors of
pixels with values in the [0-255] range.
When calling the summary()
method after instantiating a ConvNeXt model,
prefer setting the expand_nested
argument summary()
to True
to better
investigate the instantiated model.
Arguments
True
.None
(random initialization),
"imagenet"
(pre-training on ImageNet-1k), or the path to the weights
file to be loaded. Defaults to "imagenet"
.layers.Input()
)
to use as image input for the model.include_top
is False
.
It should have exactly 3 inputs channels.include_top
is False
. Defaults to None.
- None
means that the output of the model will be
the 4D tensor output of the last convolutional layer.
- avg
means that global average pooling
will be applied to the output of the
last convolutional layer, and thus
the output of the model will be a 2D tensor.
- max
means that global max pooling will
be applied.include_top
is True
, and
if no weights
argument is specified. Defaults to 1000 (number of
ImageNet classes).str
or callable. The activation function to use
on the "top" layer. Ignored unless include_top=True
. Set
classifier_activation=None
to return the logits of the "top" layer.
Defaults to "softmax"
.
When loading pretrained weights, classifier_activation
can only
be None
or "softmax"
.Returns
A model instance.
ConvNeXtSmall
functionkeras.applications.ConvNeXtSmall(
model_name="convnext_small",
include_top=True,
include_preprocessing=True,
weights="imagenet",
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
classifier_activation="softmax",
)
Instantiates the ConvNeXtSmall architecture.
References
For image classification use cases, see this page for detailed examples. For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.
The base
, large
, and xlarge
models were first pre-trained on the
ImageNet-21k dataset and then fine-tuned on the ImageNet-1k dataset. The
pre-trained parameters of the models were assembled from the
official repository. To get a
sense of how these parameters were converted to Keras compatible parameters,
please refer to
this repository.
Note: Each Keras Application expects a specific kind of input preprocessing.
For ConvNeXt, preprocessing is included in the model using a Normalization
layer. ConvNeXt models expect their inputs to be float or uint8 tensors of
pixels with values in the [0-255] range.
When calling the summary()
method after instantiating a ConvNeXt model,
prefer setting the expand_nested
argument summary()
to True
to better
investigate the instantiated model.
Arguments
True
.None
(random initialization),
"imagenet"
(pre-training on ImageNet-1k), or the path to the weights
file to be loaded. Defaults to "imagenet"
.layers.Input()
)
to use as image input for the model.include_top
is False
.
It should have exactly 3 inputs channels.include_top
is False
. Defaults to None.
- None
means that the output of the model will be
the 4D tensor output of the last convolutional layer.
- avg
means that global average pooling
will be applied to the output of the
last convolutional layer, and thus
the output of the model will be a 2D tensor.
- max
means that global max pooling will
be applied.include_top
is True
, and
if no weights
argument is specified. Defaults to 1000 (number of
ImageNet classes).str
or callable. The activation function to use
on the "top" layer. Ignored unless include_top=True
. Set
classifier_activation=None
to return the logits of the "top" layer.
Defaults to "softmax"
.
When loading pretrained weights, classifier_activation
can only
be None
or "softmax"
.Returns
A model instance.
ConvNeXtBase
functionkeras.applications.ConvNeXtBase(
model_name="convnext_base",
include_top=True,
include_preprocessing=True,
weights="imagenet",
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
classifier_activation="softmax",
)
Instantiates the ConvNeXtBase architecture.
References
For image classification use cases, see this page for detailed examples. For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.
The base
, large
, and xlarge
models were first pre-trained on the
ImageNet-21k dataset and then fine-tuned on the ImageNet-1k dataset. The
pre-trained parameters of the models were assembled from the
official repository. To get a
sense of how these parameters were converted to Keras compatible parameters,
please refer to
this repository.
Note: Each Keras Application expects a specific kind of input preprocessing.
For ConvNeXt, preprocessing is included in the model using a Normalization
layer. ConvNeXt models expect their inputs to be float or uint8 tensors of
pixels with values in the [0-255] range.
When calling the summary()
method after instantiating a ConvNeXt model,
prefer setting the expand_nested
argument summary()
to True
to better
investigate the instantiated model.
Arguments
True
.None
(random initialization),
"imagenet"
(pre-training on ImageNet-1k), or the path to the weights
file to be loaded. Defaults to "imagenet"
.layers.Input()
)
to use as image input for the model.include_top
is False
.
It should have exactly 3 inputs channels.include_top
is False
. Defaults to None.
- None
means that the output of the model will be
the 4D tensor output of the last convolutional layer.
- avg
means that global average pooling
will be applied to the output of the
last convolutional layer, and thus
the output of the model will be a 2D tensor.
- max
means that global max pooling will
be applied.include_top
is True
, and
if no weights
argument is specified. Defaults to 1000 (number of
ImageNet classes).str
or callable. The activation function to use
on the "top" layer. Ignored unless include_top=True
. Set
classifier_activation=None
to return the logits of the "top" layer.
Defaults to "softmax"
.
When loading pretrained weights, classifier_activation
can only
be None
or "softmax"
.Returns
A model instance.
ConvNeXtLarge
functionkeras.applications.ConvNeXtLarge(
model_name="convnext_large",
include_top=True,
include_preprocessing=True,
weights="imagenet",
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
classifier_activation="softmax",
)
Instantiates the ConvNeXtLarge architecture.
References
For image classification use cases, see this page for detailed examples. For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.
The base
, large
, and xlarge
models were first pre-trained on the
ImageNet-21k dataset and then fine-tuned on the ImageNet-1k dataset. The
pre-trained parameters of the models were assembled from the
official repository. To get a
sense of how these parameters were converted to Keras compatible parameters,
please refer to
this repository.
Note: Each Keras Application expects a specific kind of input preprocessing.
For ConvNeXt, preprocessing is included in the model using a Normalization
layer. ConvNeXt models expect their inputs to be float or uint8 tensors of
pixels with values in the [0-255] range.
When calling the summary()
method after instantiating a ConvNeXt model,
prefer setting the expand_nested
argument summary()
to True
to better
investigate the instantiated model.
Arguments
True
.None
(random initialization),
"imagenet"
(pre-training on ImageNet-1k), or the path to the weights
file to be loaded. Defaults to "imagenet"
.layers.Input()
)
to use as image input for the model.include_top
is False
.
It should have exactly 3 inputs channels.include_top
is False
. Defaults to None.
- None
means that the output of the model will be
the 4D tensor output of the last convolutional layer.
- avg
means that global average pooling
will be applied to the output of the
last convolutional layer, and thus
the output of the model will be a 2D tensor.
- max
means that global max pooling will
be applied.include_top
is True
, and
if no weights
argument is specified. Defaults to 1000 (number of
ImageNet classes).str
or callable. The activation function to use
on the "top" layer. Ignored unless include_top=True
. Set
classifier_activation=None
to return the logits of the "top" layer.
Defaults to "softmax"
.
When loading pretrained weights, classifier_activation
can only
be None
or "softmax"
.Returns
A model instance.
ConvNeXtXLarge
functionkeras.applications.ConvNeXtXLarge(
model_name="convnext_xlarge",
include_top=True,
include_preprocessing=True,
weights="imagenet",
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
classifier_activation="softmax",
)
Instantiates the ConvNeXtXLarge architecture.
References
For image classification use cases, see this page for detailed examples. For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.
The base
, large
, and xlarge
models were first pre-trained on the
ImageNet-21k dataset and then fine-tuned on the ImageNet-1k dataset. The
pre-trained parameters of the models were assembled from the
official repository. To get a
sense of how these parameters were converted to Keras compatible parameters,
please refer to
this repository.
Note: Each Keras Application expects a specific kind of input preprocessing.
For ConvNeXt, preprocessing is included in the model using a Normalization
layer. ConvNeXt models expect their inputs to be float or uint8 tensors of
pixels with values in the [0-255] range.
When calling the summary()
method after instantiating a ConvNeXt model,
prefer setting the expand_nested
argument summary()
to True
to better
investigate the instantiated model.
Arguments
True
.None
(random initialization),
"imagenet"
(pre-training on ImageNet-1k), or the path to the weights
file to be loaded. Defaults to "imagenet"
.layers.Input()
)
to use as image input for the model.include_top
is False
.
It should have exactly 3 inputs channels.include_top
is False
. Defaults to None.
- None
means that the output of the model will be
the 4D tensor output of the last convolutional layer.
- avg
means that global average pooling
will be applied to the output of the
last convolutional layer, and thus
the output of the model will be a 2D tensor.
- max
means that global max pooling will
be applied.include_top
is True
, and
if no weights
argument is specified. Defaults to 1000 (number of
ImageNet classes).str
or callable. The activation function to use
on the "top" layer. Ignored unless include_top=True
. Set
classifier_activation=None
to return the logits of the "top" layer.
Defaults to "softmax"
.
When loading pretrained weights, classifier_activation
can only
be None
or "softmax"
.Returns
A model instance.