MobileNet V1

MobileNet V1 is a family of efficient convolutional neural networks optimized for on-device or embedded vision tasks. It achieves this efficiency by using depth-wise separable convolutions instead of standard convolutions. The architecture allows for easy trade-offs between latency and accuracy using two main hyperparameters, a width multiplier (alpha) and an image resolution multiplier.

You can all the original MobileNet checkpoints under the Google organization.

Click on the MobileNet V1 models in the right sidebar for more examples of how to apply MobileNet to different vision tasks.

The example below demonstrates how to classify an image with Pipeline or the AutoModel class.

Pipeline

AutoModel

Notes

Checkpoint names follow the pattern mobilenet_v1_{depth_multiplier}_{resolution}, like mobilenet_v1_1.0_224. 1.0 is the depth multiplier and 224 is the image resolution.
While trained on images of a specific sizes, the model architecture works with images of different sizes (minimum 32x32). The MobileNetV1ImageProcessor handles the necessary preprocessing.
MobileNet is pretrained on ImageNet-1k, a dataset with 1000 classes. However, the model actually predicts 1001 classes. The additional class is an extra “background” class (index 0).
The original TensorFlow checkpoints determines the padding amount at inference because it depends on the input image size. To use the native PyTorch padding behavior, set tf_padding=False in MobileNetV1Config.
```
from transformers import MobileNetV1Config

config = MobileNetV1Config.from_pretrained("google/mobilenet_v1_1.0_224", tf_padding=True)
```
The Transformers implementation does not support the following features.
- Uses global average pooling instead of the optional 7x7 average pooling with stride 2. For larger inputs, this gives a pooled output that is larger than a 1x1 pixel.
- Does not support other output_stride values (fixed at 32). For smaller output_strides, the original implementation uses dilated convolution to prevent spatial resolution from being reduced further. (which would require dilated convolutions).
- output_hidden_states=True returns all intermediate hidden states. It is not possible to extract the output from specific layers for other downstream purposes.
- Does not include the quantized models from the original checkpoints because they include “FakeQuantization” operations to unquantize the weights.

MobileNetV1Config

class transformers.MobileNetV1Config

< source >

( num_channels = 3 image_size = 224 depth_multiplier = 1.0 min_depth = 8 hidden_act = 'relu6' tf_padding = True classifier_dropout_prob = 0.999 initializer_range = 0.02 layer_norm_eps = 0.001 **kwargs )

Parameters

num_channels (int, optional, defaults to 3) — The number of input channels.
image_size (int, optional, defaults to 224) — The size (resolution) of each image.
depth_multiplier (float, optional, defaults to 1.0) — Shrinks or expands the number of channels in each layer. Default is 1.0, which starts the network with 32 channels. This is sometimes also called “alpha” or “width multiplier”.
min_depth (int, optional, defaults to 8) — All layers will have at least this many channels.
hidden_act (str or function, optional, defaults to "relu6") — The non-linear activation function (function or string) in the Transformer encoder and convolution layers.
tf_padding (bool, optional, defaults to True) — Whether to use TensorFlow padding rules on the convolution layers.
classifier_dropout_prob (float, optional, defaults to 0.999) — The dropout ratio for attached classifiers.
initializer_range (float, optional, defaults to 0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (float, optional, defaults to 0.001) — The epsilon used by the layer normalization layers.

This is the configuration class to store the configuration of a MobileNetV1Model. It is used to instantiate a MobileNetV1 model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the MobileNetV1 google/mobilenet_v1_1.0_224 architecture.

Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.

Example:

>>> from transformers import MobileNetV1Config, MobileNetV1Model

>>> # Initializing a "mobilenet_v1_1.0_224" style configuration
>>> configuration = MobileNetV1Config()

>>> # Initializing a model from the "mobilenet_v1_1.0_224" style configuration
>>> model = MobileNetV1Model(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config

MobileNetV1FeatureExtractor

class transformers.MobileNetV1FeatureExtractor

< source >

( *args **kwargs )

preprocess

< source >

( images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']] do_resize: typing.Optional[bool] = None size: typing.Optional[typing.Dict[str, int]] = None resample: Resampling = None do_center_crop: typing.Optional[bool] = None crop_size: typing.Optional[typing.Dict[str, int]] = None do_rescale: typing.Optional[bool] = None rescale_factor: typing.Optional[float] = None do_normalize: typing.Optional[bool] = None image_mean: typing.Union[float, typing.List[float], NoneType] = None image_std: typing.Union[float, typing.List[float], NoneType] = None return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None data_format: typing.Union[str, transformers.image_utils.ChannelDimension] = <ChannelDimension.FIRST: 'channels_first'> input_data_format: typing.Union[str, transformers.image_utils.ChannelDimension, NoneType] = None )

Parameters

images (ImageInput) — Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If passing in images with pixel values between 0 and 1, set do_rescale=False.
do_resize (bool, optional, defaults to self.do_resize) — Whether to resize the image.
size (Dict[str, int], optional, defaults to self.size) — Size of the image after resizing. Shortest edge of the image is resized to size[“shortest_edge”], with the longest edge resized to keep the input aspect ratio.
resample (PILImageResampling filter, optional, defaults to self.resample) — PILImageResampling filter to use if resizing the image e.g. PILImageResampling.BILINEAR. Only has an effect if do_resize is set to True.
do_center_crop (bool, optional, defaults to self.do_center_crop) — Whether to center crop the image.
crop_size (Dict[str, int], optional, defaults to self.crop_size) — Size of the center crop. Only has an effect if do_center_crop is set to True.
do_rescale (bool, optional, defaults to self.do_rescale) — Whether to rescale the image values between [0 - 1].
rescale_factor (float, optional, defaults to self.rescale_factor) — Rescale factor to rescale the image by if do_rescale is set to True.
do_normalize (bool, optional, defaults to self.do_normalize) — Whether to normalize the image.
image_mean (float or List[float], optional, defaults to self.image_mean) — Image mean to use if do_normalize is set to True.
image_std (float or List[float], optional, defaults to self.image_std) — Image standard deviation to use if do_normalize is set to True.
return_tensors (str or TensorType, optional) — The type of tensors to return. Can be one of:
- Unset: Return a list of np.ndarray.
- TensorType.TENSORFLOW or 'tf': Return a batch of type tf.Tensor.
- TensorType.PYTORCH or 'pt': Return a batch of type torch.Tensor.
- TensorType.NUMPY or 'np': Return a batch of type np.ndarray.
- TensorType.JAX or 'jax': Return a batch of type jax.numpy.ndarray.
data_format (ChannelDimension or str, optional, defaults to ChannelDimension.FIRST) — The channel dimension format for the output image. Can be one of:
- "channels_first" or ChannelDimension.FIRST: image in (num_channels, height, width) format.
- "channels_last" or ChannelDimension.LAST: image in (height, width, num_channels) format.
- Unset: Use the channel dimension format of the input image.
input_data_format (ChannelDimension or str, optional) — The channel dimension format for the input image. If unset, the channel dimension format is inferred from the input image. Can be one of:
- "channels_first" or ChannelDimension.FIRST: image in (num_channels, height, width) format.
- "channels_last" or ChannelDimension.LAST: image in (height, width, num_channels) format.
- "none" or ChannelDimension.NONE: image in (height, width) format.

Preprocess an image or batch of images.

MobileNetV1ImageProcessor

class transformers.MobileNetV1ImageProcessor

< source >

( do_resize: bool = True size: typing.Optional[typing.Dict[str, int]] = None resample: Resampling = <Resampling.BILINEAR: 2> do_center_crop: bool = True crop_size: typing.Optional[typing.Dict[str, int]] = None do_rescale: bool = True rescale_factor: typing.Union[int, float] = 0.00392156862745098 do_normalize: bool = True image_mean: typing.Union[float, typing.List[float], NoneType] = None image_std: typing.Union[float, typing.List[float], NoneType] = None **kwargs )

Parameters

do_resize (bool, optional, defaults to True) — Whether to resize the image’s (height, width) dimensions to the specified size. Can be overridden by do_resize in the preprocess method.
size (Dict[str, int] optional, defaults to {"shortest_edge" -- 256}): Size of the image after resizing. The shortest edge of the image is resized to size[“shortest_edge”], with the longest edge resized to keep the input aspect ratio. Can be overridden by size in the preprocess method.
resample (PILImageResampling, optional, defaults to PILImageResampling.BILINEAR) — Resampling filter to use if resizing the image. Can be overridden by the resample parameter in the preprocess method.
do_center_crop (bool, optional, defaults to True) — Whether to center crop the image. If the input size is smaller than crop_size along any edge, the image is padded with 0’s and then center cropped. Can be overridden by the do_center_crop parameter in the preprocess method.
crop_size (Dict[str, int], optional, defaults to {"height" -- 224, "width": 224}): Desired output size when applying center-cropping. Only has an effect if do_center_crop is set to True. Can be overridden by the crop_size parameter in the preprocess method.
do_rescale (bool, optional, defaults to True) — Whether to rescale the image by the specified scale rescale_factor. Can be overridden by the do_rescale parameter in the preprocess method.
rescale_factor (int or float, optional, defaults to 1/255) — Scale factor to use if rescaling the image. Can be overridden by the rescale_factor parameter in the preprocess method.
do_normalize — Whether to normalize the image. Can be overridden by the do_normalize parameter in the preprocess method.
image_mean (float or List[float], optional, defaults to IMAGENET_STANDARD_MEAN) — Mean to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the image_mean parameter in the preprocess method.
image_std (float or List[float], optional, defaults to IMAGENET_STANDARD_STD) — Standard deviation to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the image_std parameter in the preprocess method.

Constructs a MobileNetV1 image processor.

preprocess

< source >

Parameters

images (ImageInput) — Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If passing in images with pixel values between 0 and 1, set do_rescale=False.
do_resize (bool, optional, defaults to self.do_resize) — Whether to resize the image.
size (Dict[str, int], optional, defaults to self.size) — Size of the image after resizing. Shortest edge of the image is resized to size[“shortest_edge”], with the longest edge resized to keep the input aspect ratio.
resample (PILImageResampling filter, optional, defaults to self.resample) — PILImageResampling filter to use if resizing the image e.g. PILImageResampling.BILINEAR. Only has an effect if do_resize is set to True.
do_center_crop (bool, optional, defaults to self.do_center_crop) — Whether to center crop the image.
crop_size (Dict[str, int], optional, defaults to self.crop_size) — Size of the center crop. Only has an effect if do_center_crop is set to True.
do_rescale (bool, optional, defaults to self.do_rescale) — Whether to rescale the image values between [0 - 1].
rescale_factor (float, optional, defaults to self.rescale_factor) — Rescale factor to rescale the image by if do_rescale is set to True.
do_normalize (bool, optional, defaults to self.do_normalize) — Whether to normalize the image.
image_mean (float or List[float], optional, defaults to self.image_mean) — Image mean to use if do_normalize is set to True.
image_std (float or List[float], optional, defaults to self.image_std) — Image standard deviation to use if do_normalize is set to True.
return_tensors (str or TensorType, optional) — The type of tensors to return. Can be one of:
- Unset: Return a list of np.ndarray.
- TensorType.TENSORFLOW or 'tf': Return a batch of type tf.Tensor.
- TensorType.PYTORCH or 'pt': Return a batch of type torch.Tensor.
- TensorType.NUMPY or 'np': Return a batch of type np.ndarray.
- TensorType.JAX or 'jax': Return a batch of type jax.numpy.ndarray.
data_format (ChannelDimension or str, optional, defaults to ChannelDimension.FIRST) — The channel dimension format for the output image. Can be one of:
- "channels_first" or ChannelDimension.FIRST: image in (num_channels, height, width) format.
- "channels_last" or ChannelDimension.LAST: image in (height, width, num_channels) format.
- Unset: Use the channel dimension format of the input image.
input_data_format (ChannelDimension or str, optional) — The channel dimension format for the input image. If unset, the channel dimension format is inferred from the input image. Can be one of:
- "channels_first" or ChannelDimension.FIRST: image in (num_channels, height, width) format.
- "channels_last" or ChannelDimension.LAST: image in (height, width, num_channels) format.
- "none" or ChannelDimension.NONE: image in (height, width) format.

Preprocess an image or batch of images.

MobileNetV1ImageProcessorFast

class transformers.MobileNetV1ImageProcessorFast

< source >

( **kwargs: typing_extensions.Unpack[transformers.image_processing_utils_fast.DefaultFastImageProcessorKwargs] )

Parameters

do_resize (bool, optional, defaults to True) — Whether to resize the image.
size (dict[str, int], optional, defaults to {'shortest_edge' -- 256}): Describes the maximum input dimensions to the model.
default_to_square (bool, optional, defaults to False) — Whether to default to a square image when resizing, if size is an int.
resample (Union[PILImageResampling, F.InterpolationMode, NoneType], defaults to Resampling.BILINEAR) — Resampling filter to use if resizing the image. This can be one of the enum PILImageResampling. Only has an effect if do_resize is set to True.
do_center_crop (bool, optional, defaults to True) — Whether to center crop the image.
crop_size (dict[str, int], optional, defaults to {'height' -- 224, 'width': 224}): Size of the output image after applying center_crop.
do_rescale (bool, optional, defaults to True) — Whether to rescale the image.
rescale_factor (Union[int, float, NoneType], defaults to 0.00392156862745098) — Rescale factor to rescale the image by if do_rescale is set to True.
do_normalize (bool, optional, defaults to True) — Whether to normalize the image.
image_mean (Union[float, list[float], NoneType], defaults to [0.5, 0.5, 0.5]) — Image mean to use for normalization. Only has an effect if do_normalize is set to True.
image_std (Union[float, list[float], NoneType], defaults to [0.5, 0.5, 0.5]) — Image standard deviation to use for normalization. Only has an effect if do_normalize is set to True.
do_convert_rgb (bool, optional, defaults to None) — Whether to convert the image to RGB.
return_tensors (Union[str, ~utils.generic.TensorType, NoneType], defaults to None) — Returns stacked tensors if set to `pt, otherwise returns a list of tensors.
data_format (~image_utils.ChannelDimension, optional, defaults to ChannelDimension.FIRST) — Only ChannelDimension.FIRST is supported. Added for compatibility with slow processors.
input_data_format (Union[~image_utils.ChannelDimension, str, NoneType], defaults to None) — The channel dimension format for the input image. If unset, the channel dimension format is inferred from the input image. Can be one of:
- "channels_first" or ChannelDimension.FIRST: image in (num_channels, height, width) format.
- "channels_last" or ChannelDimension.LAST: image in (height, width, num_channels) format.
- "none" or ChannelDimension.NONE: image in (height, width) format.
device (torch.device, optional, defaults to None) — The device to process the images on. If unset, the device is inferred from the input images.

Constructs a fast Mobilenet V1 image processor.

preprocess

< source >

( images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']] *args **kwargs: typing_extensions.Unpack[transformers.image_processing_utils_fast.DefaultFastImageProcessorKwargs] ) → <class 'transformers.image_processing_base.BatchFeature'>

Parameters

images (Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']]) — Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If passing in images with pixel values between 0 and 1, set do_rescale=False.
do_resize (bool, optional) — Whether to resize the image.
size (dict[str, int], optional) — Describes the maximum input dimensions to the model.
default_to_square (bool, optional) — Whether to default to a square image when resizing, if size is an int.
resample (Union[PILImageResampling, F.InterpolationMode, NoneType]) — Resampling filter to use if resizing the image. This can be one of the enum PILImageResampling. Only has an effect if do_resize is set to True.
do_center_crop (bool, optional) — Whether to center crop the image.
crop_size (dict[str, int], optional) — Size of the output image after applying center_crop.
do_rescale (bool, optional) — Whether to rescale the image.
rescale_factor (Union[int, float, NoneType]) — Rescale factor to rescale the image by if do_rescale is set to True.
do_normalize (bool, optional) — Whether to normalize the image.
image_mean (Union[float, list[float], NoneType]) — Image mean to use for normalization. Only has an effect if do_normalize is set to True.
image_std (Union[float, list[float], NoneType]) — Image standard deviation to use for normalization. Only has an effect if do_normalize is set to True.
do_convert_rgb (bool, optional) — Whether to convert the image to RGB.
return_tensors (Union[str, ~utils.generic.TensorType, NoneType]) — Returns stacked tensors if set to `pt, otherwise returns a list of tensors.
data_format (~image_utils.ChannelDimension, optional) — Only ChannelDimension.FIRST is supported. Added for compatibility with slow processors.
input_data_format (Union[~image_utils.ChannelDimension, str, NoneType]) — The channel dimension format for the input image. If unset, the channel dimension format is inferred from the input image. Can be one of:
- "channels_first" or ChannelDimension.FIRST: image in (num_channels, height, width) format.
- "channels_last" or ChannelDimension.LAST: image in (height, width, num_channels) format.
- "none" or ChannelDimension.NONE: image in (height, width) format.
device (torch.device, optional) — The device to process the images on. If unset, the device is inferred from the input images.

Returns

<class 'transformers.image_processing_base.BatchFeature'>

data (dict) — Dictionary of lists/arrays/tensors returned by the call method (‘pixel_values’, etc.).
tensor_type (Union[None, str, TensorType], optional) — You can give a tensor_type here to convert the lists of integers in PyTorch/TensorFlow/Numpy Tensors at initialization.

MobileNetV1Model

class transformers.MobileNetV1Model

< source >

( config: MobileNetV1Config add_pooling_layer: bool = True )

Parameters

config (MobileNetV1Config) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.
add_pooling_layer (bool, optional, defaults to True) — Whether to add a pooling layer

The bare Mobilenet V1 Model outputting raw hidden-states without any specific head on top.

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

forward

< source >

( pixel_values: typing.Optional[torch.Tensor] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) → transformers.modeling_outputs.BaseModelOutputWithPoolingAndNoAttention or tuple(torch.FloatTensor)

Parameters

pixel_values (torch.Tensor of shape (batch_size, num_channels, image_size, image_size), optional) — The tensors corresponding to the input images. Pixel values can be obtained using {image_processor_class}. See {image_processor_class}.__call__ for details ({processor_class} uses {image_processor_class} for processing images).
output_hidden_states (bool, optional) — Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail.
return_dict (bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple.

Returns

transformers.modeling_outputs.BaseModelOutputWithPoolingAndNoAttention or tuple(torch.FloatTensor)

A transformers.modeling_outputs.BaseModelOutputWithPoolingAndNoAttention or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (MobileNetV1Config) and inputs.

last_hidden_state (torch.FloatTensor of shape (batch_size, num_channels, height, width)) — Sequence of hidden-states at the output of the last layer of the model.
pooler_output (torch.FloatTensor of shape (batch_size, hidden_size)) — Last layer hidden-state after a pooling operation on the spatial dimensions.
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, num_channels, height, width).

Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

The MobileNetV1Model forward method, overrides the __call__ special method.

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

MobileNetV1ForImageClassification

class transformers.MobileNetV1ForImageClassification

< source >

( config: MobileNetV1Config )

Parameters

config (MobileNetV1Config) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for ImageNet.

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

forward

< source >

( pixel_values: typing.Optional[torch.Tensor] = None output_hidden_states: typing.Optional[bool] = None labels: typing.Optional[torch.Tensor] = None return_dict: typing.Optional[bool] = None ) → transformers.modeling_outputs.ImageClassifierOutputWithNoAttention or tuple(torch.FloatTensor)

Parameters

pixel_values (torch.Tensor of shape (batch_size, num_channels, image_size, image_size), optional) — The tensors corresponding to the input images. Pixel values can be obtained using {image_processor_class}. See {image_processor_class}.__call__ for details ({processor_class} uses {image_processor_class} for processing images).
output_hidden_states (bool, optional) — Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail.
labels (torch.Tensor of shape (batch_size,), optional) — Labels for computing the image classification/regression loss. Indices should be in [0, ..., config.num_labels - 1]. If config.num_labels == 1 a regression loss is computed (Mean-Square loss). If config.num_labels > 1 a classification loss is computed (Cross-Entropy).
return_dict (bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple.

Returns

transformers.modeling_outputs.ImageClassifierOutputWithNoAttention or tuple(torch.FloatTensor)

A transformers.modeling_outputs.ImageClassifierOutputWithNoAttention or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (MobileNetV1Config) and inputs.

loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Classification (or regression if config.num_labels==1) loss.
logits (torch.FloatTensor of shape (batch_size, config.num_labels)) — Classification (or regression if config.num_labels==1) scores (before SoftMax).
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each stage) of shape (batch_size, num_channels, height, width). Hidden-states (also called feature maps) of the model at the output of each stage.

The MobileNetV1ForImageClassification forward method, overrides the __call__ special method.

Example:

>>> from transformers import AutoImageProcessor, MobileNetV1ForImageClassification
>>> import torch
>>> from datasets import load_dataset

>>> dataset = load_dataset("huggingface/cats-image", trust_remote_code=True)
>>> image = dataset["test"]["image"][0]

>>> image_processor = AutoImageProcessor.from_pretrained("google/mobilenet_v1_1.0_224")
>>> model = MobileNetV1ForImageClassification.from_pretrained("google/mobilenet_v1_1.0_224")

>>> inputs = image_processor(image, return_tensors="pt")

>>> with torch.no_grad():
...     logits = model(**inputs).logits

>>> # model predicts one of the 1000 ImageNet classes
>>> predicted_label = logits.argmax(-1).item()
>>> print(model.config.id2label[predicted_label])
...

< > Update on GitHub

Transformers

MobileNet V1

Notes

MobileNetV1Config

class transformers.MobileNetV1Config

MobileNetV1FeatureExtractor

class transformers.MobileNetV1FeatureExtractor

preprocess

MobileNetV1ImageProcessor

class transformers.MobileNetV1ImageProcessor

preprocess

MobileNetV1ImageProcessorFast

class transformers.MobileNetV1ImageProcessorFast

preprocess

MobileNetV1Model

class transformers.MobileNetV1Model

forward

MobileNetV1ForImageClassification

class transformers.MobileNetV1ForImageClassification

forward