 with SegFormer is by checking the example notebooks (which showcase both inference and
fine-tuning on custom data). One can also check out the blog post introducing SegFormer and illustrating how it can be fine-tuned on custom data.
TensorFlow users should refer to this repository that shows off-the-shelf inference and fine-tuning.
One can also check out this interactive demo on Hugging Face Spaces
to try out a SegFormer model on custom images.
SegFormer works on any input size, as it pads the input to be divisible by config.patch_sizes.
One can use SegformerImageProcessor to prepare images and corresponding segmentation maps
for the model. Note that this image processor is fairly basic and does not include all data augmentations used in
the original paper. The original preprocessing pipelines (for the ADE20k dataset for instance) can be found here. The most
important preprocessing step is that images and segmentation maps are randomly cropped and padded to the same size,
such as 512x512 or 640x640, after which they are normalized.
One additional thing to keep in mind is that one can initialize SegformerImageProcessor with
reduce_labels set to True or False. In some datasets (like ADE20k), the 0 index is used in the annotated
segmentation maps for background. However, ADE20k doesn’t include the “background” class in its 150 labels.
Therefore, reduce_labels is used to reduce all labels by 1, and to make sure no loss is computed for the
background class (i.e. it replaces 0 in the annotated maps by 255, which is the ignore_index of the loss function
used by SegformerForSemanticSegmentation). However, other datasets use the 0 index as
background class and include this class as part of all labels. In that case, reduce_labels should be set to
False, as loss should also be computed for the background class.
As most models, SegFormer comes in different sizes, the details of which can be found in the table below
(taken from Table 7 of the original paper).
Model variant
Depths
Hidden sizes
Decoder hidden size
Params (M)
ImageNet-1k Top 1
MiT-b0
[2, 2, 2, 2]
[32, 64, 160, 256]
256
3.7
70.5
MiT-b1
[2, 2, 2, 2]
[64, 128, 320, 512]
256
14.0
78.7
MiT-b2
[3, 4, 6, 3]
[64, 128, 320, 512]
768
25.4
81.6
MiT-b3
[3, 4, 18, 3]
[64, 128, 320, 512]
768
45.2
83.1
MiT-b4
[3, 8, 27, 3]
[64, 128, 320, 512]
768
62.6
83.6
MiT-b5
[3, 6, 40, 3]
[64, 128, 320, 512]
768
82.0
83.8
Note that MiT in the above table refers to the Mix Transformer encoder backbone introduced in SegFormer. For
SegFormer’s results on the segmentation datasets like ADE20k, refer to the paper.
Resources
	
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with SegFormer.
Image Classification
SegformerForImageClassification is supported by this example script and notebook.
Image classification task guide
Semantic segmentation:
SegformerForSemanticSegmentation is supported by this example script.
A blog on fine-tuning SegFormer on a custom dataset can be found here.
More demo notebooks on SegFormer (both inference + fine-tuning on a custom dataset) can be found here.
TFSegformerForSemanticSegmentation is supported by this example notebook.
Semantic segmentation task guide
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.
SegformerConfig
	
class transformers.SegformerConfig
<
source
>
(
num_channels = 3
num_encoder_blocks = 4
depths = [2, 2, 2, 2]
sr_ratios = [8, 4, 2, 1]
hidden_sizes = [32, 64, 160, 256]
patch_sizes = [7, 3, 3, 3]
strides = [4, 2, 2, 2]
num_attention_heads = [1, 2, 5, 8]
mlp_ratios = [4, 4, 4, 4]
hidden_act = 'gelu'
hidden_dropout_prob = 0.0
attention_probs_dropout_prob = 0.0
classifier_dropout_prob = 0.1
initializer_range = 0.02
drop_path_rate = 0.1
layer_norm_eps = 1e-06
decoder_hidden_size = 256
semantic_loss_ignore_index = 255
**kwargs
)
Parameters 
num_channels (int, optional, defaults to 3) —
The number of input channels.
num_encoder_blocks (int, optional, defaults to 4) —
The number of encoder blocks (i.e. stages in the Mix Transformer encoder).
depths (List[int], optional, defaults to [2, 2, 2, 2]) —
The number of layers in each encoder block.
sr_ratios (List[int], optional, defaults to [8, 4, 2, 1]) —
Sequence reduction ratios in each encoder block.
hidden_sizes (List[int], optional, defaults to [32, 64, 160, 256]) —
Dimension of each of the encoder blocks.
patch_sizes (List[int], optional, defaults to [7, 3, 3, 3]) —
Patch size before each encoder block.
strides (List[int], optional, defaults to [4, 2, 2, 2]) —
Stride before each encoder block.
num_attention_heads (List[int], optional, defaults to [1, 2, 5, 8]) —
Number of attention heads for each attention layer in each block of the Transformer encoder.
mlp_ratios (List[int], optional, defaults to [4, 4, 4, 4]) —
Ratio of the size of the hidden layer compared to the size of the input layer of the Mix FFNs in the
encoder blocks.
hidden_act (str or function, optional, defaults to "gelu") —
The non-linear activation function (function or string) in the encoder and pooler. If string, "gelu",
"relu", "selu" and "gelu_new" are supported.
hidden_dropout_prob (float, optional, defaults to 0.0) —
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (float, optional, defaults to 0.0) —
The dropout ratio for the attention probabilities.
classifier_dropout_prob (float, optional, defaults to 0.1) —
The dropout probability before the classification head.
initializer_range (float, optional, defaults to 0.02) —
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
drop_path_rate (float, optional, defaults to 0.1) —
The dropout probability for stochastic depth, used in the blocks of the Transformer encoder.
layer_norm_eps (float, optional, defaults to 1e-6) —
The epsilon used by the layer normalization layers.
decoder_hidden_size (int, optional, defaults to 256) —
The dimension of the all-MLP decode head.
semantic_loss_ignore_index (int, optional, defaults to 255) —
The index that is ignored by the loss function of the semantic segmentation model.
This is the configuration class to store the configuration of a SegformerModel. It is used to instantiate an
SegFormer 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 SegFormer
nvidia/segformer-b0-finetuned-ade-512-512
architecture.
Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the
documentation from PretrainedConfig for more information.
Example:
	Copied
 from transformers import SegformerModel, SegformerConfig
 # Initializing a SegFormer nvidia/segformer-b0-finetuned-ade-512-512 style configuration
 configuration = SegformerConfig()
 # Initializing a model from the nvidia/segformer-b0-finetuned-ade-512-512 style configuration
 model = SegformerModel(configuration)
 # Accessing the model configuration
 configuration = model.config
SegformerFeatureExtractor
	
class transformers.SegformerFeatureExtractor
<
source
>
(
*args
**kwargs
)
__call__
<
source
>
(
images
segmentation_maps = None
**kwargs
)
Preprocesses a batch of images and optionally segmentation maps.
Overrides the __call__ method of the Preprocessor class so that both images and segmentation maps can be
passed in as positional arguments.
post_process_semantic_segmentation
<
source
>
(
outputs
target_sizes: typing.List[typing.Tuple] = None
)
→
semantic_segmentation
Parameters 
outputs (SegformerForSemanticSegmentation) —
Raw outputs of the model.
target_sizes (List[Tuple] of length batch_size, optional) —
List of tuples corresponding to the requested final size (height, width) of each prediction. If left to
None, predictions will not be resized.
Returns
semantic_segmentation
List[torch.Tensor] of length batch_size, where each item is a semantic
segmentation map of shape (height, width) corresponding to the target_sizes entry (if target_sizes is
specified). Each entry of each torch.Tensor correspond to a semantic class id.
Converts the output of SegformerForSemanticSegmentation into semantic segmentation maps. Only supports
PyTorch.
SegformerImageProcessor
	
class transformers.SegformerImageProcessor
<
source
>
(
do_resize: bool = True
size: typing.Dict[str, int] = None
resample: Resampling = <Resampling.BILINEAR: 2>
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
do_reduce_labels: bool = False
**kwargs
)
Parameters 
do_resize (bool, optional, defaults to True) —
Whether to resize the image’s (height, width) dimensions to the specified (size["height"], size["width"]). Can be overridden by the do_resize parameter in the preprocess method.
size (Dict[str, int] optional, defaults to {"height" -- 512, "width": 512}):
Size of the output image after resizing. Can be overridden by the size parameter 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_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) —
Whether to normalize the image. Can be overridden by the do_normalize parameter in the preprocess
method.
do_normalize (bool, optional, defaults to True) —
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.
do_reduce_labels (bool, optional, defaults to False) —
Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is
used for background, and background itself is not included in all classes of a dataset (e.g. ADE20k). The
background label will be replaced by 255. Can be overridden by the do_reduce_labels parameter in the
preprocess method.
Constructs a Segformer image processor.
preprocess
<
source
>
(
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')]]
segmentation_maps: 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')], NoneType] = None
do_resize: typing.Optional[bool] = None
size: typing.Union[typing.Dict[str, int], NoneType] = None
resample: Resampling = 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
do_reduce_labels: typing.Optional[bool] = None
return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None
data_format: ChannelDimension = <ChannelDimension.FIRST: 'channels_first'>
**kwargs
)
Parameters 
images (ImageInput) —
Image to preprocess.
segmentation_maps (ImageInput, optional) —
Segmentation map to preprocess.
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 resize is applied.
resample (int, optional, defaults to self.resample) —
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_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.
image_std (float or List[float], optional, defaults to self.image_std) —
Image standard deviation.
do_reduce_labels (bool, optional, defaults to self.do_reduce_labels) —
Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0
is used for background, and background itself is not included in all classes of a dataset (e.g.
ADE20k). The background label will be replaced by 255.
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:
ChannelDimension.FIRST: image in (num_channels, height, width) format.
ChannelDimension.LAST: image in (height, width, num_channels) format.
Preprocess an image or batch of images.
post_process_semantic_segmentation
<
source
>
(
outputs
target_sizes: typing.List[typing.Tuple] = None
)
→
semantic_segmentation
Parameters 
outputs (SegformerForSemanticSegmentation) —
Raw outputs of the model.
target_sizes (List[Tuple] of length batch_size, optional) —
List of tuples corresponding to the requested final size (height, width) of each prediction. If left to
None, predictions will not be resized.
Returns
semantic_segmentation
List[torch.Tensor] of length batch_size, where each item is a semantic
segmentation map of shape (height, width) corresponding to the target_sizes entry (if target_sizes is
specified). Each entry of each torch.Tensor correspond to a semantic class id.
Converts the output of SegformerForSemanticSegmentation into semantic segmentation maps. Only supports
PyTorch.
SegformerModel
	
class transformers.SegformerModel
<
source
>
(
config
)
Parameters 
config (SegformerConfig) — 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.
The bare SegFormer encoder (Mix-Transformer) outputting raw hidden-states without any specific head on top.
This model is a PyTorch torch.nn.Module sub-class. 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: FloatTensor
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.BaseModelOutput or tuple(torch.FloatTensor)
Parameters 
pixel_values (torch.FloatTensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
AutoImageProcessor. See SegformerImageProcessor.call() for details.
output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.
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.BaseModelOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.BaseModelOutput 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 (SegformerConfig) and inputs.
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.
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, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (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.
The SegformerModel 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:
	Copied
 from transformers import AutoImageProcessor, SegformerModel
 import torch
 from datasets import load_dataset
 dataset = load_dataset("huggingface/cats-image")
 image = dataset["test"]["image"][0]
 image_processor = AutoImageProcessor.from_pretrained("nvidia/mit-b0")
 model = SegformerModel.from_pretrained("nvidia/mit-b0")
 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, 256, 16, 16]
SegformerDecodeHead
	
class transformers.SegformerDecodeHead
<
source
>
(
config
)
forward
<
source
>
(
encoder_hidden_states: FloatTensor
)
SegformerForImageClassification
	
class transformers.SegformerForImageClassification
<
source
>
(
config
)
Parameters 
config (SegformerConfig) — 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.
SegFormer Model transformer with an image classification head on top (a linear layer on top of the final hidden
states) e.g. for ImageNet.
This model is a PyTorch torch.nn.Module sub-class. 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.FloatTensor] = None
labels: typing.Optional[torch.LongTensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.models.segformer.modeling_segformer.SegFormerImageClassifierOutput or tuple(torch.FloatTensor)
Parameters 
pixel_values (torch.FloatTensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
AutoImageProcessor. See SegformerImageProcessor.call() for details.
output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.
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.
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
transformers.models.segformer.modeling_segformer.SegFormerImageClassifierOutput or tuple(torch.FloatTensor)
A transformers.models.segformer.modeling_segformer.SegFormerImageClassifierOutput 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 (SegformerConfig) 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.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, patch_size, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
The SegformerForImageClassification 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:
	Copied
 from transformers import AutoImageProcessor, SegformerForImageClassification
 import torch
 from datasets import load_dataset
 dataset = load_dataset("huggingface/cats-image")
 image = dataset["test"]["image"][0]
 image_processor = AutoImageProcessor.from_pretrained("nvidia/mit-b0")
 model = SegformerForImageClassification.from_pretrained("nvidia/mit-b0")
 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])
tabby, tabby cat
SegformerForSemanticSegmentation
	
class transformers.SegformerForSemanticSegmentation
<
source
>
(
config
)
Parameters 
config (SegformerConfig) — 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.
SegFormer Model transformer with an all-MLP decode head on top e.g. for ADE20k, CityScapes.
This model is a PyTorch torch.nn.Module sub-class. 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: FloatTensor
labels: typing.Optional[torch.LongTensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.SemanticSegmenterOutput or tuple(torch.FloatTensor)
Parameters 
pixel_values (torch.FloatTensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
AutoImageProcessor. See SegformerImageProcessor.call() for details.
output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.
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.
labels (torch.LongTensor of shape (batch_size, height, width), optional) —
Ground truth semantic segmentation maps for computing the loss. Indices should be in [0, ..., config.num_labels - 1]. If config.num_labels > 1, a classification loss is computed (Cross-Entropy).
Returns
transformers.modeling_outputs.SemanticSegmenterOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.SemanticSegmenterOutput 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 (SegformerConfig) 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, logits_height, logits_width)) — Classification scores for each pixel.
The logits returned do not necessarily have the same size as the pixel_values passed as inputs. This is
to avoid doing two interpolations and lose some quality when a user needs to resize the logits to the
original image size as post-processing. You should always check your logits shape and resize as needed.
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, patch_size, hidden_size).
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, patch_size, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
The SegformerForSemanticSegmentation 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.
Examples:
	Copied
 from transformers import AutoImageProcessor, SegformerForSemanticSegmentation
 from PIL import Image
 import requests
 image_processor = AutoImageProcessor.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512")
 model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512")
 url = "http://images.cocodataset.org/val2017/000000039769.jpg"
 image = Image.open(requests.get(url, stream=True).raw)
 inputs = image_processor(images=image, return_tensors="pt")
 outputs = model(**inputs)
 logits = outputs.logits  # shape (batch_size, num_labels, height/4, width/4)
 list(logits.shape)
[1, 150, 128, 128]
TFSegformerDecodeHead
	
class transformers.TFSegformerDecodeHead
<
source
>
(
*args
**kwargs
)
call
<
source
>
(
encoder_hidden_states
training: bool = False
)
TFSegformerModel
	
class transformers.TFSegformerModel
<
source
>
(
*args
**kwargs
)
Parameters 
config (SegformerConfig) — 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.
The bare SegFormer encoder (Mix-Transformer) outputting raw hidden-states without any specific head on top.
This model inherits from TFPreTrainedModel. 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 tf.keras.Model 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.
call
<
source
>
(
pixel_values: tf.Tensor
output_attentions: Optional[bool] = None
output_hidden_states: Optional[bool] = None
return_dict: Optional[bool] = None
training: bool = False
)
→
transformers.modeling_tf_outputs.TFBaseModelOutput or tuple(tf.Tensor)
Parameters 
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 AutoImageProcessor. See
SegformerImageProcessor.call() for details.
output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions 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.
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.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.
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.modeling_tf_outputs.TFBaseModelOutput or tuple(tf.Tensor)
A transformers.modeling_tf_outputs.TFBaseModelOutput 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 (SegformerConfig) and inputs.
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.
hidden_states (tuple(tf.FloatTensor), 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.
The TFSegformerModel 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:
	Copied
 from transformers import AutoImageProcessor, TFSegformerModel
 from datasets import load_dataset
 dataset = load_dataset("huggingface/cats-image")
 image = dataset["test"]["image"][0]
 image_processor = AutoImageProcessor.from_pretrained("nvidia/mit-b0")
 model = TFSegformerModel.from_pretrained("nvidia/mit-b0")
 inputs = image_processor(image, return_tensors="tf")
 outputs = model(**inputs)
 last_hidden_states = outputs.last_hidden_state
 list(last_hidden_states.shape)
[1, 256, 16, 16]
TFSegformerForImageClassification
	
class transformers.TFSegformerForImageClassification
<
source
>
(
*args
**kwargs
)
Parameters 
config (SegformerConfig) — 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.
SegFormer Model transformer with an image classification head on top (a linear layer on top of the final hidden
states) e.g. for ImageNet.
This model inherits from TFPreTrainedModel. 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 tf.keras.Model 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.
call
<
source
>
(
pixel_values: tf.Tensor | None = None
labels: tf.Tensor | None = None
output_attentions: Optional[bool] = None
output_hidden_states: Optional[bool] = None
return_dict: Optional[bool] = None
)
→
transformers.modeling_tf_outputs.TFSequenceClassifierOutput or tuple(tf.Tensor)
Parameters 
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 AutoImageProcessor. See
SegformerImageProcessor.call() for details.
output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions 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.
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.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.
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.modeling_tf_outputs.TFSequenceClassifierOutput or tuple(tf.Tensor)
A transformers.modeling_tf_outputs.TFSequenceClassifierOutput 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 (SegformerConfig) and inputs.
loss (tf.Tensor of shape (batch_size, ), 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 + 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.
The TFSegformerForImageClassification 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:
	Copied
 from transformers import AutoImageProcessor, TFSegformerForImageClassification
 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("nvidia/mit-b0")
 model = TFSegformerForImageClassification.from_pretrained("nvidia/mit-b0")
 inputs = image_processor(image, return_tensors="tf")
 logits = model(**inputs).logits
 # model predicts one of the 1000 ImageNet classes
 predicted_label = int(tf.math.argmax(logits, axis=-1))
 print(model.config.id2label[predicted_label])
tabby, tabby cat
TFSegformerForSemanticSegmentation
	
class transformers.TFSegformerForSemanticSegmentation
<
source
>
(
*args
**kwargs
)
Parameters 
config (SegformerConfig) — 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.
SegFormer Model transformer with an all-MLP decode head on top e.g. for ADE20k, CityScapes.
This model inherits from TFPreTrainedModel. 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 tf.keras.Model 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.
call
<
source
>
(
pixel_values: tf.Tensor
labels: tf.Tensor | None = None
output_attentions: Optional[bool] = None
output_hidden_states: Optional[bool] = None
return_dict: Optional[bool] = None
)
→
transformers.modeling_tf_outputs.TFSemanticSegmenterOutput or tuple(tf.Tensor)
Parameters 
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 AutoImageProcessor. See
SegformerImageProcessor.call() for details.
output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions 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.
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.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.
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 of shape (batch_size, height, width), optional) —
Ground truth semantic segmentation maps for computing the loss. Indices should be in [0, ..., config.num_labels - 1]. If config.num_labels > 1, a (per-pixel) classification loss is computed
(Cross-Entropy).
Returns
transformers.modeling_tf_outputs.TFSemanticSegmenterOutput or tuple(tf.Tensor)
A transformers.modeling_tf_outputs.TFSemanticSegmenterOutput 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 (SegformerConfig) and inputs.
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, logits_height, logits_width)) — Classification scores for each pixel.
The logits returned do not necessarily have the same size as the pixel_values passed as inputs. This is
to avoid doing two interpolations and lose some quality when a user needs to resize the logits to the
original image size as post-processing. You should always check your logits shape and resize as needed.
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 layer) of shape (batch_size, patch_size, hidden_size).
Hidden-states of the model at the output of each layer plus the optional 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, patch_size, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
The TFSegformerForSemanticSegmentation 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.
Examples:
	Copied
 from transformers import AutoImageProcessor, TFSegformerForSemanticSegmentation
 from PIL import Image
 import requests
 url = "http://images.cocodataset.org/val2017/000000039769.jpg"
 image = Image.open(requests.get(url, stream=True).raw)
 image_processor = AutoImageProcessor.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512")
 model = TFSegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512")
 inputs = image_processor(images=image, return_tensors="tf")
 outputs = model(**inputs, training=False)
 # logits are of shape (batch_size, num_labels, height/4, width/4)
 logits = outputs.logits
 list(logits.shape)
[1, 150, 128, 128]
←ResNet
SwiftFormer→
SegFormer
Overview
Resources
SegformerConfig
SegformerFeatureExtractor
SegformerImageProcessor
SegformerModel
SegformerDecodeHead
SegformerForImageClassification
SegformerForSemanticSegmentation
TFSegformerDecodeHead
TFSegformerModel
TFSegformerForImageClassification
TFSegformerForSemanticSegmentation
