EfficientFormer

## [](#overview)Overview

The EfficientFormer model was proposed in [EfficientFormer: Vision Transformers at MobileNet Speed](https://arxiv.org/abs/2206.01191) by Yanyu Li, Geng Yuan, Yang Wen, Eric Hu, Georgios Evangelidis, Sergey Tulyakov, Yanzhi Wang, Jian Ren. EfficientFormer proposes a dimension-consistent pure transformer that can be run on mobile devices for dense prediction tasks like image classification, object detection and semantic segmentation.

The abstract from the paper is the following:

_Vision Transformers (ViT) have shown rapid progress in computer vision tasks, achieving promising results on various benchmarks. However, due to the massive number of parameters and model design, e.g., attention mechanism, ViT-based models are generally times slower than lightweight convolutional networks. Therefore, the deployment of ViT for real-time applications is particularly challenging, especially on resource-constrained hardware such as mobile devices. Recent efforts try to reduce the computation complexity of ViT through network architecture search or hybrid design with MobileNet block, yet the inference speed is still unsatisfactory. This leads to an important question: can transformers run as fast as MobileNet while obtaining high performance? To answer this, we first revisit the network architecture and operators used in ViT-based models and identify inefficient designs. Then we introduce a dimension-consistent pure transformer (without MobileNet blocks) as a design paradigm. Finally, we perform latency-driven slimming to get a series of final models dubbed EfficientFormer. Extensive experiments show the superiority of EfficientFormer in performance and speed on mobile devices. Our fastest model, EfficientFormer-L1, achieves 79.2% top-1 accuracy on ImageNet-1K with only 1.6 ms inference latency on iPhone 12 (compiled with CoreML), which { runs as fast as MobileNetV2×1.4 (1.6 ms, 74.7% top-1),} and our largest model, EfficientFormer-L7, obtains 83.3% accuracy with only 7.0 ms latency. Our work proves that properly designed transformers can reach extremely low latency on mobile devices while maintaining high performance._

This model was contributed by [novice03](https://huggingface.co/novice03) and [Bearnardd](https://huggingface.co/Bearnardd). The original code can be found [here](https://github.com/snap-research/EfficientFormer). The TensorFlow version of this model was added by [D-Roberts](https://huggingface.co/D-Roberts).

## [](#documentation-resources)Documentation resources

-   [Image classification task guide](../tasks/image_classification)

## [](#transformers.EfficientFormerConfig)EfficientFormerConfig

### class transformers.EfficientFormerConfig

[](#transformers.EfficientFormerConfig)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/efficientformer/configuration_efficientformer.py#L32)

( depths: typing.List\[int\] = \[3, 2, 6, 4\]hidden\_sizes: typing.List\[int\] = \[48, 96, 224, 448\]downsamples: typing.List\[bool\] = \[True, True, True, True\]dim: int = 448key\_dim: int = 32attention\_ratio: int = 4resolution: int = 7num\_hidden\_layers: int = 5num\_attention\_heads: int = 8mlp\_expansion\_ratio: int = 4hidden\_dropout\_prob: float = 0.0patch\_size: int = 16num\_channels: int = 3pool\_size: int = 3downsample\_patch\_size: int = 3downsample\_stride: int = 2downsample\_pad: int = 1drop\_path\_rate: float = 0.0num\_meta3d\_blocks: int = 1distillation: bool = Trueuse\_layer\_scale: bool = Truelayer\_scale\_init\_value: float = 1e-05hidden\_act: str = 'gelu'initializer\_range: float = 0.02layer\_norm\_eps: float = 1e-12image\_size: int = 224batch\_norm\_eps: float = 1e-05\*\*kwargs )

This is the configuration class to store the configuration of an [EfficientFormerModel](/docs/transformers/v4.30.0/en/model_doc/efficientformer#transformers.EfficientFormerModel). It is used to instantiate an EfficientFormer 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 EfficientFormer [snap-research/efficientformer-l1](https://huggingface.co/snap-research/efficientformer-l1) architecture.

Configuration objects inherit from [PretrainedConfig](/docs/transformers/v4.30.0/en/main_classes/configuration#transformers.PretrainedConfig) and can be used to control the model outputs. Read the documentation from [PretrainedConfig](/docs/transformers/v4.30.0/en/main_classes/configuration#transformers.PretrainedConfig) for more information.

Example:

```
>>> from transformers import EfficientFormerConfig, EfficientFormerModel

>>> 
>>> configuration = EfficientFormerConfig()

>>> 
>>> model = EfficientFormerModel(configuration)

>>> 
>>> configuration = model.config```

## [](#transformers.EfficientFormerImageProcessor)EfficientFormerImageProcessor

### class transformers.EfficientFormerImageProcessor

[](#transformers.EfficientFormerImageProcessor)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/efficientformer/image_processing_efficientformer.py#L46)

( do\_resize: bool = Truesize: typing.Union\[typing.Dict\[str, int\], NoneType\] = Noneresample: Resampling = <Resampling.BICUBIC: 3>do\_center\_crop: bool = Truedo\_rescale: bool = Truerescale\_factor: typing.Union\[int, float\] = 0.00392156862745098crop\_size: typing.Dict\[str, int\] = Nonedo\_normalize: bool = Trueimage\_mean: typing.Union\[float, typing.List\[float\], NoneType\] = Noneimage\_std: typing.Union\[float, typing.List\[float\], NoneType\] = None\*\*kwargs )

Constructs a EfficientFormer image processor.

#### preprocess

[](#transformers.EfficientFormerImageProcessor.preprocess)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/efficientformer/image_processing_efficientformer.py#L229)

( images: typing.Union\[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), typing.List\[ForwardRef('PIL.Image.Image')\], typing.List\[numpy.ndarray\], typing.List\[ForwardRef('torch.Tensor')\]\]do\_resize: typing.Optional\[bool\] = Nonesize: typing.Dict\[str, int\] = Noneresample: Resampling = Nonedo\_center\_crop: bool = Nonecrop\_size: int = Nonedo\_rescale: typing.Optional\[bool\] = Nonerescale\_factor: typing.Optional\[float\] = Nonedo\_normalize: typing.Optional\[bool\] = Noneimage\_mean: typing.Union\[float, typing.List\[float\], NoneType\] = Noneimage\_std: typing.Union\[float, typing.List\[float\], NoneType\] = Nonereturn\_tensors: typing.Union\[str, transformers.utils.generic.TensorType, NoneType\] = Nonedata\_format: typing.Union\[str, transformers.image\_utils.ChannelDimension\] = <ChannelDimension.FIRST: 'channels\_first'>\*\*kwargs )

Preprocess an image or batch of images.

## [](#transformers.EfficientFormerModel)EfficientFormerModel

### class transformers.EfficientFormerModel

[](#transformers.EfficientFormerModel)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/efficientformer/modeling_efficientformer.py#L557)

( config: EfficientFormerConfig )

Parameters

-   [](#transformers.EfficientFormerModel.config)**config** ([EfficientFormerConfig](/docs/transformers/v4.30.0/en/model_doc/efficientformer#transformers.EfficientFormerConfig)) — 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()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

The bare EfficientFormer Model transformer outputting raw hidden-states without any specific head on top. This model is a PyTorch [nn.Module](https://pytorch.org/docs/stable/nn.html#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.

The [EfficientFormerModel](/docs/transformers/v4.30.0/en/model_doc/efficientformer#transformers.EfficientFormerModel) 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:

```
>>> from transformers import AutoImageProcessor, EfficientFormerModel
>>> import torch
>>> from datasets import load_dataset

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

>>> image_processor = AutoImageProcessor.from_pretrained("snap-research/efficientformer-l1-300")
>>> model = EfficientFormerModel.from_pretrained("snap-research/efficientformer-l1-300")

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

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

>>> last_hidden_states = outputs.last_hidden_state
>>> list(last_hidden_states.shape)
[1, 49, 448]```

## [](#transformers.EfficientFormerForImageClassification)EfficientFormerForImageClassification

### class transformers.EfficientFormerForImageClassification

[](#transformers.EfficientFormerForImageClassification)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/efficientformer/modeling_efficientformer.py#L619)

( config: EfficientFormerConfig )

Parameters

-   [](#transformers.EfficientFormerForImageClassification.config)**config** ([EfficientFormerConfig](/docs/transformers/v4.30.0/en/model_doc/efficientformer#transformers.EfficientFormerConfig)) — 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()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

EfficientFormer Model transformer with an image classification head on top (a linear layer on top of the final hidden state of the \[CLS\] token) e.g. for ImageNet.

This model is a PyTorch [nn.Module](https://pytorch.org/docs/stable/nn.html#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

[](#transformers.EfficientFormerForImageClassification.forward)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/efficientformer/modeling_efficientformer.py#L634)

( pixel\_values: typing.Optional\[torch.Tensor\] = Nonelabels: typing.Optional\[torch.Tensor\] = Noneoutput\_attentions: typing.Optional\[bool\] = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = None ) → [transformers.modeling\_outputs.ImageClassifierOutput](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_outputs.ImageClassifierOutput) or `tuple(torch.FloatTensor)`

The [EfficientFormerForImageClassification](/docs/transformers/v4.30.0/en/model_doc/efficientformer#transformers.EfficientFormerForImageClassification) 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:

```
>>> from transformers import AutoImageProcessor, EfficientFormerForImageClassification
>>> import torch
>>> from datasets import load_dataset

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

>>> image_processor = AutoImageProcessor.from_pretrained("snap-research/efficientformer-l1-300")
>>> model = EfficientFormerForImageClassification.from_pretrained("snap-research/efficientformer-l1-300")

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

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

>>> 
>>> predicted_label = logits.argmax(-1).item()
>>> print(model.config.id2label[predicted_label])
Egyptian cat```

## [](#transformers.EfficientFormerForImageClassificationWithTeacher)EfficientFormerForImageClassificationWithTeacher

### class transformers.EfficientFormerForImageClassificationWithTeacher

[](#transformers.EfficientFormerForImageClassificationWithTeacher)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/efficientformer/modeling_efficientformer.py#L749)

( config: EfficientFormerConfig )

Parameters

-   [](#transformers.EfficientFormerForImageClassificationWithTeacher.config)**config** ([EfficientFormerConfig](/docs/transformers/v4.30.0/en/model_doc/efficientformer#transformers.EfficientFormerConfig)) — 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()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

EfficientFormer Model transformer with image classification heads on top (a linear layer on top of the final hidden state of the \[CLS\] token and a linear layer on top of the final hidden state of the distillation token) e.g. for ImageNet.

This model supports inference-only. Fine-tuning with distillation (i.e. with a teacher) is not yet supported.

This model is a PyTorch [nn.Module](https://pytorch.org/docs/stable/nn.html#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

[](#transformers.EfficientFormerForImageClassificationWithTeacher.forward)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/efficientformer/modeling_efficientformer.py#L766)

( pixel\_values: typing.Optional\[torch.Tensor\] = Noneoutput\_attentions: typing.Optional\[bool\] = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = None ) → `transformers.models.efficientformer.modeling_efficientformer.EfficientFormerForImageClassificationWithTeacherOutput` or `tuple(torch.FloatTensor)`

The [EfficientFormerForImageClassificationWithTeacher](/docs/transformers/v4.30.0/en/model_doc/efficientformer#transformers.EfficientFormerForImageClassificationWithTeacher) 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:

```
>>> from transformers import AutoImageProcessor, EfficientFormerForImageClassificationWithTeacher
>>> import torch
>>> from datasets import load_dataset

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

>>> image_processor = AutoImageProcessor.from_pretrained("snap-research/efficientformer-l1-300")
>>> model = EfficientFormerForImageClassificationWithTeacher.from_pretrained("snap-research/efficientformer-l1-300")

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

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

>>> 
>>> predicted_label = logits.argmax(-1).item()
>>> print(model.config.id2label[predicted_label])
Egyptian cat```

## [](#transformers.TFEfficientFormerModel)TFEfficientFormerModel

### class transformers.TFEfficientFormerModel

[](#transformers.TFEfficientFormerModel)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/efficientformer/modeling_tf_efficientformer.py#L775)

( \*args\*\*kwargs )

Parameters

-   [](#transformers.TFEfficientFormerModel.config)**config** ([EfficientFormerConfig](/docs/transformers/v4.30.0/en/model_doc/efficientformer#transformers.EfficientFormerConfig)) — 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()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

The bare EfficientFormer Model transformer outputting raw hidden-states without any specific head on top. This model is a TensorFlow [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer). Use it as a regular TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and behavior.

The [TFEfficientFormerModel](/docs/transformers/v4.30.0/en/model_doc/efficientformer#transformers.TFEfficientFormerModel) 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:

```
>>> from transformers import AutoImageProcessor, TFEfficientFormerModel
>>> from datasets import load_dataset

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

>>> image_processor = AutoImageProcessor.from_pretrained("snap-research/efficientformer-l1-300")
>>> model = TFEfficientFormerModel.from_pretrained("snap-research/efficientformer-l1-300")

>>> inputs = image_processor(image, return_tensors="tf")
>>> outputs = model(**inputs)

>>> last_hidden_states = outputs.last_hidden_state
>>> list(last_hidden_states.shape)
[1, 49, 448]```

## [](#transformers.TFEfficientFormerForImageClassification)TFEfficientFormerForImageClassification

### class transformers.TFEfficientFormerForImageClassification

[](#transformers.TFEfficientFormerForImageClassification)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/efficientformer/modeling_tf_efficientformer.py#L815)

( \*args\*\*kwargs )

Parameters

-   [](#transformers.TFEfficientFormerForImageClassification.config)**config** ([EfficientFormerConfig](/docs/transformers/v4.30.0/en/model_doc/efficientformer#transformers.EfficientFormerConfig)) — 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()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

EfficientFormer Model transformer with an image classification head on top of pooled last hidden state, e.g. for ImageNet.

This model is a TensorFlow [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer). Use it as a regular TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and behavior.

#### call

[](#transformers.TFEfficientFormerForImageClassification.call)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/efficientformer/modeling_tf_efficientformer.py#L829)

( pixel\_values: typing.Optional\[tensorflow.python.framework.ops.Tensor\] = Nonelabels: typing.Optional\[tensorflow.python.framework.ops.Tensor\] = Noneoutput\_attentions: typing.Optional\[bool\] = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = Nonetraining: bool = False ) → `transformers.modeling_tf_outputs.TFImageClassifierOutput` or `tuple(tf.Tensor)`

The [TFEfficientFormerForImageClassification](/docs/transformers/v4.30.0/en/model_doc/efficientformer#transformers.TFEfficientFormerForImageClassification) 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:

```
>>> from transformers import AutoImageProcessor, TFEfficientFormerForImageClassification
>>> import tensorflow as tf
>>> from datasets import load_dataset

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

>>> image_processor = AutoImageProcessor.from_pretrained("snap-research/efficientformer-l1-300")
>>> model = TFEfficientFormerForImageClassification.from_pretrained("snap-research/efficientformer-l1-300")

>>> inputs = image_processor(image, return_tensors="tf")
>>> logits = model(**inputs).logits

>>> 
>>> predicted_label = int(tf.math.argmax(logits, axis=-1))
>>> print(model.config.id2label[predicted_label])
LABEL_281```

## [](#transformers.TFEfficientFormerForImageClassificationWithTeacher)TFEfficientFormerForImageClassificationWithTeacher

### class transformers.TFEfficientFormerForImageClassificationWithTeacher

[](#transformers.TFEfficientFormerForImageClassificationWithTeacher)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/efficientformer/modeling_tf_efficientformer.py#L920)

( \*args\*\*kwargs )

Parameters

-   [](#transformers.TFEfficientFormerForImageClassificationWithTeacher.config)**config** ([EfficientFormerConfig](/docs/transformers/v4.30.0/en/model_doc/efficientformer#transformers.EfficientFormerConfig)) — 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()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

EfficientFormer Model transformer with image classification heads on top (a linear layer on top of the final hidden state and a linear layer on top of the final hidden state of the distillation token) e.g. for ImageNet.

.. warning:: This model supports inference-only. Fine-tuning with distillation (i.e. with a teacher) is not yet supported.

This model is a TensorFlow [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer). Use it as a regular TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and behavior.

#### call

[](#transformers.TFEfficientFormerForImageClassificationWithTeacher.call)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/efficientformer/modeling_tf_efficientformer.py#L939)

( pixel\_values: typing.Optional\[tensorflow.python.framework.ops.Tensor\] = Noneoutput\_attentions: typing.Optional\[bool\] = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = Nonetraining: bool = False ) → `transformers.models.efficientformer.modeling_tf_efficientformer.TFEfficientFormerForImageClassificationWithTeacherOutput` or `tuple(tf.Tensor)`

The [TFEfficientFormerForImageClassificationWithTeacher](/docs/transformers/v4.30.0/en/model_doc/efficientformer#transformers.TFEfficientFormerForImageClassificationWithTeacher) 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.

-   **Output** type of [EfficientFormerForImageClassificationWithTeacher](/docs/transformers/v4.30.0/en/model_doc/efficientformer#transformers.EfficientFormerForImageClassificationWithTeacher). logits (`tf.Tensor` of shape `(batch_size, config.num_labels)`) — Prediction scores as the average of the cls\_logits and distillation logits. cls\_logits (`tf.Tensor` of shape `(batch_size, config.num_labels)`) — Prediction scores of the classification head (i.e. the linear layer on top of the final hidden state of the class token). distillation\_logits (`tf.Tensor` of shape `(batch_size, config.num_labels)`) — Prediction scores of the distillation head (i.e. the linear layer on top of the final hidden state of the distillation token). hidden\_states (`tuple(tf.Tensor)`, _optional_, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(tf.Tensor)`, _optional_, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

Example:

```
>>> from transformers import AutoImageProcessor, TFEfficientFormerForImageClassificationWithTeacher
>>> import tensorflow as tf
>>> from datasets import load_dataset

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

>>> image_processor = AutoImageProcessor.from_pretrained("snap-research/efficientformer-l1-300")
>>> model = TFEfficientFormerForImageClassificationWithTeacher.from_pretrained("snap-research/efficientformer-l1-300")

>>> inputs = image_processor(image, return_tensors="tf")
>>> logits = model(**inputs).logits

>>> 
>>> predicted_label = int(tf.math.argmax(logits, axis=-1))
>>> print(model.config.id2label[predicted_label])
LABEL_281```