CvT
Last updated
Last updated
The CvT model was proposed in by Haiping Wu, Bin Xiao, Noel Codella, Mengchen Liu, Xiyang Dai, Lu Yuan and Lei Zhang. The Convolutional vision Transformer (CvT) improves the in performance and efficiency by introducing convolutions into ViT to yield the best of both designs.
The abstract from the paper is the following:
We present in this paper a new architecture, named Convolutional vision Transformer (CvT), that improves Vision Transformer (ViT) in performance and efficiency by introducing convolutions into ViT to yield the best of both designs. This is accomplished through two primary modifications: a hierarchy of Transformers containing a new convolutional token embedding, and a convolutional Transformer block leveraging a convolutional projection. These changes introduce desirable properties of convolutional neural networks (CNNs) to the ViT architecture (\ie shift, scale, and distortion invariance) while maintaining the merits of Transformers (\ie dynamic attention, global context, and better generalization). We validate CvT by conducting extensive experiments, showing that this approach achieves state-of-the-art performance over other Vision Transformers and ResNets on ImageNet-1k, with fewer parameters and lower FLOPs. In addition, performance gains are maintained when pretrained on larger datasets (\eg ImageNet-22k) and fine-tuned to downstream tasks. Pre-trained on ImageNet-22k, our CvT-W24 obtains a top-1 accuracy of 87.7\% on the ImageNet-1k val set. Finally, our results show that the positional encoding, a crucial component in existing Vision Transformers, can be safely removed in our model, simplifying the design for higher resolution vision tasks.
Tips:
CvT models are regular Vision Transformers, but trained with convolutions. They outperform the when fine-tuned on ImageNet-1K and CIFAR-100.
You can check out demo notebooks regarding inference as well as fine-tuning on custom data (you can just replace by and by ).
The available checkpoints are either (1) pre-trained on (a collection of 14 million images and 22k classes) only, (2) also fine-tuned on ImageNet-22k or (3) also fine-tuned on (also referred to as ILSVRC 2012, a collection of 1.3 million images and 1,000 classes).
This model was contributed by . The original code can be found .
A list of official BOINC AI and community (indicated by 🌎) resources to help you get started with CvT.
Image Classification
is supported by this and .
See also:
If you’re interested in submitting a resource to be included here, please feel free to open a Pull Request and we’ll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
( num_channels = 3patch_sizes = [7, 3, 3]patch_stride = [4, 2, 2]patch_padding = [2, 1, 1]embed_dim = [64, 192, 384]num_heads = [1, 3, 6]depth = [1, 2, 10]mlp_ratio = [4.0, 4.0, 4.0]attention_drop_rate = [0.0, 0.0, 0.0]drop_rate = [0.0, 0.0, 0.0]drop_path_rate = [0.0, 0.0, 0.1]qkv_bias = [True, True, True]cls_token = [False, False, True]qkv_projection_method = ['dw_bn', 'dw_bn', 'dw_bn']kernel_qkv = [3, 3, 3]padding_kv = [1, 1, 1]stride_kv = [2, 2, 2]padding_q = [1, 1, 1]stride_q = [1, 1, 1]initializer_range = 0.02layer_norm_eps = 1e-12**kwargs )
Parameters
num_channels (int, optional, defaults to 3) — The number of input channels.
patch_sizes (List[int], optional, defaults to [7, 3, 3]) — The kernel size of each encoder’s patch embedding.
patch_stride (List[int], optional, defaults to [4, 2, 2]) — The stride size of each encoder’s patch embedding.
patch_padding (List[int], optional, defaults to [2, 1, 1]) — The padding size of each encoder’s patch embedding.
embed_dim (List[int], optional, defaults to [64, 192, 384]) — Dimension of each of the encoder blocks.
num_heads (List[int], optional, defaults to [1, 3, 6]) — Number of attention heads for each attention layer in each block of the Transformer encoder.
depth (List[int], optional, defaults to [1, 2, 10]) — The number of layers in each encoder block.
mlp_ratios (List[float], optional, defaults to [4.0, 4.0, 4.0, 4.0]) — Ratio of the size of the hidden layer compared to the size of the input layer of the Mix FFNs in the encoder blocks.
attention_drop_rate (List[float], optional, defaults to [0.0, 0.0, 0.0]) — The dropout ratio for the attention probabilities.
drop_rate (List[float], optional, defaults to [0.0, 0.0, 0.0]) — The dropout ratio for the patch embeddings probabilities.
drop_path_rate (List[float], optional, defaults to [0.0, 0.0, 0.1]) — The dropout probability for stochastic depth, used in the blocks of the Transformer encoder.
qkv_bias (List[bool], optional, defaults to [True, True, True]) — The bias bool for query, key and value in attentions
cls_token (List[bool], optional, defaults to [False, False, True]) — Whether or not to add a classification token to the output of each of the last 3 stages.
qkv_projection_method (List[string], optional, defaults to [“dw_bn”, “dw_bn”, “dw_bn”]`) — The projection method for query, key and value Default is depth-wise convolutions with batch norm. For Linear projection use “avg”.
kernel_qkv (List[int], optional, defaults to [3, 3, 3]) — The kernel size for query, key and value in attention layer
padding_kv (List[int], optional, defaults to [1, 1, 1]) — The padding size for key and value in attention layer
stride_kv (List[int], optional, defaults to [2, 2, 2]) — The stride size for key and value in attention layer
padding_q (List[int], optional, defaults to [1, 1, 1]) — The padding size for query in attention layer
stride_q (List[int], optional, defaults to [1, 1, 1]) — The stride size for query in attention layer
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 1e-6) — The epsilon used by the layer normalization layers.
Example:
Copied
( configadd_pooling_layer = True )
Parameters
forward
( pixel_values: typing.Optional[torch.Tensor] = Noneoutput_hidden_states: typing.Optional[bool] = Nonereturn_dict: typing.Optional[bool] = None ) → transformers.models.cvt.modeling_cvt.BaseModelOutputWithCLSToken or tuple(torch.FloatTensor)
Parameters
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.
Returns
transformers.models.cvt.modeling_cvt.BaseModelOutputWithCLSToken or tuple(torch.FloatTensor)
last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model.
cls_token_value (torch.FloatTensor of shape (batch_size, 1, hidden_size)) — Classification token at the output of the last layer of the model.
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 + 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.
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:
Copied
( config )
Parameters
Cvt 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.
forward
Parameters
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.LongTensor 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).
Returns
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.
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:
Copied
( *args**kwargs )
Parameters
The bare Cvt Model transformer outputting raw hidden-states without any specific head on top.
TF 2.0 models accepts two formats as inputs:
having all inputs as keyword arguments (like PyTorch models), or
having all inputs as a list, tuple or dict in the first positional arguments.
This second option is useful when using tf.keras.Model.fit method which currently requires having all the tensors in the first argument of the model call function: model(inputs).
call
( pixel_values: tf.Tensor | None = Noneoutput_hidden_states: Optional[bool] = Nonereturn_dict: Optional[bool] = Nonetraining: Optional[bool] = False ) → transformers.models.cvt.modeling_tf_cvt.TFBaseModelOutputWithCLSToken or tuple(tf.Tensor)
Parameters
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. This argument can be used only in eager mode, in graph mode the value in the config will be used instead.
training (bool, optional, defaults to `False“) — Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation).
Returns
transformers.models.cvt.modeling_tf_cvt.TFBaseModelOutputWithCLSToken or tuple(tf.Tensor)
last_hidden_state (tf.Tensor of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model.
cls_token_value (tf.Tensor of shape (batch_size, 1, hidden_size)) — Classification token at the output of the last layer of the model.
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.
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.
Examples:
Copied
( *args**kwargs )
Parameters
Cvt 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.
TF 2.0 models accepts two formats as inputs:
having all inputs as keyword arguments (like PyTorch models), or
having all inputs as a list, tuple or dict in the first positional arguments.
This second option is useful when using tf.keras.Model.fit method which currently requires having all the tensors in the first argument of the model call function: model(inputs).
call
( pixel_values: tf.Tensor | None = Nonelabels: tf.Tensor | None = Noneoutput_hidden_states: Optional[bool] = Nonereturn_dict: Optional[bool] = Nonetraining: Optional[bool] = False ) → transformers.modeling_tf_outputs.TFImageClassifierOutputWithNoAttention or tuple(tf.Tensor)
Parameters
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. This argument can be used only in eager mode, in graph mode the value in the config will be used instead.
training (bool, optional, defaults to `False“) — Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation).
labels (tf.Tensor or np.ndarray 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).
Returns
transformers.modeling_tf_outputs.TFImageClassifierOutputWithNoAttention or tuple(tf.Tensor)
loss (tf.Tensor of shape (1,), optional, returned when labels is provided) — Classification (or regression if config.num_labels==1) loss.
logits (tf.Tensor of shape (batch_size, config.num_labels)) — Classification (or regression if config.num_labels==1) scores (before SoftMax).
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, 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.
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.
Examples:
Copied
This is the configuration class to store the configuration of a . It is used to instantiate a CvT 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 CvT architecture.
Configuration objects inherit from and can be used to control the model outputs. Read the documentation from for more information.
config () — 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 method to load the model weights.
The bare Cvt Model transformer outputting raw hidden-states without any specific head on top. This model is a PyTorch subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
pixel_values (torch.FloatTensor of shape (batch_size, num_channels, height, width)) — Pixel values. Pixel values can be obtained using . See CvtImageProcessor.__call__ for details.
return_dict (bool, optional) — Whether or not to return a instead of a plain tuple.
A transformers.models.cvt.modeling_cvt.BaseModelOutputWithCLSToken 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 () and inputs.
The forward method, overrides the __call__ special method.
config () — 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 method to load the model weights.
This model is a PyTorch subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
( pixel_values: typing.Optional[torch.Tensor] = Nonelabels: typing.Optional[torch.Tensor] = Noneoutput_hidden_states: typing.Optional[bool] = Nonereturn_dict: typing.Optional[bool] = None ) → or tuple(torch.FloatTensor)
pixel_values (torch.FloatTensor of shape (batch_size, num_channels, height, width)) — Pixel values. Pixel values can be obtained using . See CvtImageProcessor.__call__ for details.
return_dict (bool, optional) — Whether or not to return a instead of a plain tuple.
or tuple(torch.FloatTensor)
A 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 () and inputs.
The forward method, overrides the __call__ special method.
config () — 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 method to load the model weights.
This model inherits from . 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 subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.
pixel_values (np.ndarray, tf.Tensor, List[tf.Tensor] `Dict[str, tf.Tensor] or Dict[str, np.ndarray] and each example must have the shape (batch_size, num_channels, height, width)) — Pixel values. Pixel values can be obtained using . See CvtImageProcessor.__call__ for details.
return_dict (bool, optional) — Whether or not to return a instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True.
A transformers.models.cvt.modeling_tf_cvt.TFBaseModelOutputWithCLSToken or a tuple of tf.Tensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration () and inputs.
The forward method, overrides the __call__ special method.
config () — 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 method to load the model weights.
This model inherits from . 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 subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.
pixel_values (np.ndarray, tf.Tensor, List[tf.Tensor] `Dict[str, tf.Tensor] or Dict[str, np.ndarray] and each example must have the shape (batch_size, num_channels, height, width)) — Pixel values. Pixel values can be obtained using . See CvtImageProcessor.__call__ for details.
return_dict (bool, optional) — Whether or not to return a instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True.
A transformers.modeling_tf_outputs.TFImageClassifierOutputWithNoAttention or a tuple of tf.Tensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration () and inputs.
The forward method, overrides the __call__ special method.