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Instantiates the ConvNeXtSmall architecture.
tf.keras.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'
)
References:
- A ConvNet for the 2020s (CVPR 2022)
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.
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.
Args | |
---|---|
include_top
|
Whether to include the fully-connected
layer at the top of the network. Defaults to True .
|
weights
|
One of None (random initialization),
"imagenet" (pre-training on ImageNet-1k), or the path to the weights
file to be loaded. Defaults to "imagenet" .
|
input_tensor
|
Optional Keras tensor
(i.e. output of layers.Input() )
to use as image input for the model.
|
input_shape
|
Optional shape tuple, only to be specified
if include_top is False .
It should have exactly 3 inputs channels.
|
pooling
|
Optional pooling mode for feature extraction
when include_top is False . Defaults to None.
|
classes
|
Optional number of classes to classify images
into, only to be specified if include_top is True , and
if no weights argument is specified. Defaults to 1000 (number of
ImageNet classes).
|
classifier_activation
|
A 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. |