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TIPSv2 DPT

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This model was published in HF papers on 2026-04-13 and contributed to Hugging Face Transformers on 2026-07-06.

TIPSv2 DPT

FlashAttention SDPA

Overview

TIPSv2 with dense prediction transformer head (DPT), see the TIPSv2 documentation for more information.

tipsv2 architecture overview

This model was contributed by guarin. The original code can be found here.

You can find all the original TIPSv2 DPT checkpoints under the TIPSv2 collection.

See TIPSv2 for the TIPSv2 vision and language backbones as well as zero shot image classification.

Pipeline
Dense prediction
Depth estimation
Normal estimation
Semantic segmentation
from transformers import pipeline

pipe = pipeline(task="depth-estimation", model="google/tipsv2-b14-dpt", device_map="auto")
out = pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/room.jpg")
out["depth"]  # PIL Image normalized to [min, max] of predicted_depth

# Visualization
import matplotlib.pyplot as plt
from transformers.image_utils import load_image

image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/room.jpg")

figure, axes = plt.subplots(1, 2, figsize=(10, 5))
axes[0].imshow(image)
axes[1].imshow(out["depth"])
axes[1].set_title("Depth")
for axis in axes:
    axis.axis("off")
plt.show()
tipsv2 depth estimation pipeline 
from transformers import pipeline

pipe = pipeline(task="image-segmentation", model="google/tipsv2-b14-dpt", device_map="auto")
out = pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/room.jpg")
out[0]["label"] # string class label
out[0]["mask"]  # PIL Image with binary mask set to class id

# Visualization
import matplotlib.pyplot as plt
from transformers.image_utils import load_image

image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/room.jpg")

figure, axes = plt.subplots(1, 2, figsize=(10, 5))
axes[0].imshow(image)
axes[1].imshow(out[0]["mask"], cmap="gray")
axes[1].set_title(out[0]["label"])
for axis in axes:
    axis.axis("off")
plt.show()
tipsv2 image segmentation pipeline

Notes

Tipsv2DptConfig

class transformers.Tipsv2DptConfig

< >

( transformers_version: str | None = Nonearchitectures: list[str] | None = Noneoutput_hidden_states: bool | None = Falsereturn_dict: bool | None = Truedtype: typing.Union[str, ForwardRef('torch.dtype'), NoneType] = Nonechunk_size_feed_forward: int = 0is_encoder_decoder: bool = Falseid2label: dict[int, str] | dict[str, str] | None = Nonelabel2id: dict[str, int] | dict[str, str] | None = Noneproblem_type: typing.Optional[typing.Literal['regression', 'single_label_classification', 'multi_label_classification']] = Nonebackbone_config: dict | transformers.configuration_utils.PreTrainedConfig | None = Noneneck_hidden_sizes: list[int] | tuple[int, ...] | None = Nonefusion_hidden_size: int = 256reassemble_factors: list[int | float] | tuple[int | float, ...] | None = Nonereadout_activation: str = 'gelu_pytorch_tanh'num_depth_bins: int = 256min_depth: float = 0.001max_depth: float = 10.0depth_decoder_activation: str = 'relu'semantic_loss_ignore_index: int = 255 )

Parameters

  • backbone_config (Union[dict, ~configuration_utils.PreTrainedConfig], optional) — The configuration of the backbone model.
  • neck_hidden_sizes (list[int], optional, defaults to [96, 192, 384, 768]) — The hidden sizes to project to for the feature maps of the backbone.
  • fusion_hidden_size (int, optional, defaults to 256) — The number of channels before fusion.
  • reassemble_factors (list[float], optional, defaults to [4, 2, 1, 0.5]) — The up/downsampling factors of the reassemble layers.
  • readout_activation (str, optional, defaults to "gelu_pytorch_tanh") — Activation applied after the readout projection layer.
  • num_depth_bins (int, optional, defaults to 256) — The number of depth bins used by the depth-estimation head.
  • min_depth (float, optional, defaults to 0.001) — The minimum depth value (meters) for depth bin calculation.
  • max_depth (float, optional, defaults to 10.0) — The maximum depth value (meters) for depth bin calculation.
  • depth_decoder_activation (str, optional, defaults to "relu") — Activation applied after the depth decoder projection layer.
  • semantic_loss_ignore_index (int, optional, defaults to 255) — Label index to ignore in the cross-entropy loss for semantic segmentation.

This is the configuration class to store the configuration of a Tipsv2 DptModel. It is used to instantiate a Tipsv2 Dpt 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 google/tipsv2-b14-dpt

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 Tipsv2DptConfig, Tipsv2DptForDensePrediction

>>> configuration = Tipsv2DptConfig()
>>> model = Tipsv2DptForDensePrediction(configuration)
>>> configuration = model.config

Tipsv2DptImageProcessor

class transformers.Tipsv2DptImageProcessor

< >

( **kwargs: Unpack )

Parameters

  • **kwargs (ImagesKwargs, optional) — Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class for the complete list of supported arguments.

Constructs a Tipsv2DptImageProcessor image processor.

preprocess

< >

( images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']]*args**kwargs: Unpack ) → ~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.
  • return_tensors (str or TensorType, optional) — Returns stacked tensors if set to 'pt', otherwise returns a list of tensors.
  • **kwargs (ImagesKwargs, optional) — Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class for the complete list of supported arguments.

Returns

~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/Numpy Tensors at initialization.

post_process_depth_estimation

< >

( outputstarget_sizes: transformers.utils.generic.TensorType | list[tuple[int, int]] | None = None ) → list[dict[str, torch.Tensor]]

Parameters

  • outputs (DepthEstimatorOutput or Tipsv2DptDensePredictorOutput) — Raw outputs of the model.
  • target_sizes (TensorType or list[tuple[int, int]], optional) — Tensor of shape (batch_size, 2) or list of tuples (tuple[int, int]) containing the target size (height, width) of each image in the batch. If left to None, predictions will not be resized.

Returns

list[dict[str, torch.Tensor]]

A list of dictionaries of tensors representing the processed depth predictions.

Converts the output of Tipsv2DptForDepthEstimation or Tipsv2DptForDensePrediction into final depth predictions.

post_process_normal_estimation

< >

( outputstarget_sizes: transformers.utils.generic.TensorType | list[tuple[int, int]] | None = None )

Parameters

  • outputs (Tipsv2DptNormalEstimatorOutput or Tipsv2DptDensePredictorOutput) — Raw outputs of the model.
  • target_sizes (TensorType or list[tuple[int, int]], optional) — Tensor of shape (batch_size, 2) or list of tuples (tuple[int, int]) containing the target size (height, width) of each image in the batch. If left to None, predictions will not be resized.

Converts the output of Tipsv2DptForNormalEstimation or Tipsv2DptForDensePrediction into L2-normalized surface normal maps.

post_process_semantic_segmentation

< >

( outputstarget_sizes: transformers.utils.generic.TensorType | list[tuple[int, int]] | None = Nonereturn_segmentation_scores: bool = False ) → list[torch.Tensor] or list[SemanticSegmentationPostProcessorOutput]

Parameters

  • outputs (SemanticSegmenterOutput or Tipsv2DptDensePredictorOutput) — Raw outputs of the model.
  • target_sizes [(TensorType or list[tuple[int, int]], optional) — Tensor of shape (batch_size, 2) or list of tuples (tuple[int, int]) containing the target size (height, width) of each image in the batch. If left to None, predictions will not be resized.
  • return_segmentation_scores (bool, optional, defaults to False) — Whether to return segmentation scores alongside the segmentation map. When True, each element of the returned list is a SemanticSegmentationPostProcessorOutput with fields segmentation (class IDs, shape (height, width)) and segmentation_scores (shape (num_classes, height, width)).

Returns

list[torch.Tensor] or list[SemanticSegmentationPostProcessorOutput]

When return_segmentation_scores=False (default), a list of length batch_size where each item is a segmentation map of shape (height, width) with class IDs. When return_segmentation_scores=True, a list of SemanticSegmentationPostProcessorOutput with fields segmentation (class IDs, shape (height, width)) and segmentation_scores (shape (num_classes, height, width)). In both cases, (height, width) corresponds to the target size (if target_sizes is specified).

Converts the output of Tipsv2DptForSemanticSegmentation or Tipsv2DptForDensePrediction into semantic segmentation maps.

Tipsv2DptForDensePrediction

class transformers.Tipsv2DptForDensePrediction

< >

( config: Tipsv2DptConfig )

Parameters

  • config (Tipsv2DptConfig) — 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.

TIPSv2-DPT Model with three independent heads for depth estimation, surface normal estimation, and semantic segmentation — running a single shared backbone forward pass.

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

< >

( pixel_values: FloatTensor**kwargs: Unpack ) → Tipsv2DptDensePredictorOutput or tuple(torch.FloatTensor)

Parameters

  • pixel_values (torch.FloatTensor of shape (batch_size, num_channels, image_size, image_size)) — The tensors corresponding to the input images. Pixel values can be obtained using Tipsv2DptImageProcessor. See Tipsv2DptImageProcessor.__call__() for details (processor_class uses Tipsv2DptImageProcessor for processing images).

Returns

Tipsv2DptDensePredictorOutput or tuple(torch.FloatTensor)

A Tipsv2DptDensePredictorOutput 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 (Tipsv2DptConfig) and inputs.

The Tipsv2DptForDensePrediction 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.

  • predicted_depth (torch.FloatTensor of shape (batch_size, height, width)) — Predicted depth for each pixel.
  • normals (torch.FloatTensor of shape (batch_size, 3, height, width)) — Raw normal map predictions (unnormalized).
  • segmentation_logits (torch.FloatTensor of shape (batch_size, config.num_labels, height, 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.

Example:

>>> import torch
>>> from transformers import Tipsv2DptForDensePrediction, AutoImageProcessor
>>> from transformers.image_utils import load_image

>>> model_id = "google/tipsv2-b14-dpt"
>>> model = Tipsv2DptForDensePrediction.from_pretrained(model_id, device_map="auto")
>>> image_processor = AutoImageProcessor.from_pretrained(model_id)

>>> image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/room.jpg")
>>> inputs = image_processor(images=image, return_tensors="pt").to(model.device)

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

>>> # outputs.predicted_depth: (batch_size, height, width) tensor with predicted depth in meters
>>> # outputs.normals: (batch_size, 3, height, width) tensor with normals in XYZ format (unnormalized)
>>> # outputs.segmentation_logits: (batch_size, config.num_labels, height, width) tensor with segmentation logits
>>> depth_results = image_processor.post_process_depth_estimation(outputs, target_sizes=[(image.height, image.width)])
>>> normal_results = image_processor.post_process_normal_estimation(outputs, target_sizes=[(image.height, image.width)])
>>> segmentation_results = image_processor.post_process_semantic_segmentation(outputs, target_sizes=[(image.height, image.width)])

>>> predicted_depth = depth_results[0]["predicted_depth"]  # (height, width) tensor with predicted depth in meters
>>> normals = normal_results[0]["normals"]  # (3, height, width) tensor with normals in XYZ format (L2-normalized)
>>> segmentation = segmentation_results[0]  # (height, width) tensor with class ids

Tipsv2DptForDepthEstimation

class transformers.Tipsv2DptForDepthEstimation

< >

( config: Tipsv2DptConfig )

Parameters

  • config (Tipsv2DptConfig) — 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.

TIPSv2-DPT Model with a monocular depth estimation head.

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

< >

( pixel_values: FloatTensorlabels: typing.Optional[torch.FloatTensor] = None**kwargs: Unpack ) → DepthEstimatorOutput or tuple(torch.FloatTensor)

Parameters

  • pixel_values (torch.FloatTensor of shape (batch_size, num_channels, image_size, image_size)) — The tensors corresponding to the input images. Pixel values can be obtained using Tipsv2DptImageProcessor. See Tipsv2DptImageProcessor.__call__() for details (processor_class uses Tipsv2DptImageProcessor for processing images).
  • labels (torch.FloatTensor of shape (batch_size, sequence_length), optional) — Labels for computing the masked language modeling loss. Indices should either be in [0, ..., config.vocab_size] or -100 (see input_ids docstring). Tokens with indices set to -100 are ignored (masked), the loss is only computed for the tokens with labels in [0, ..., config.vocab_size].

Returns

DepthEstimatorOutput or tuple(torch.FloatTensor)

A DepthEstimatorOutput 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 (Tipsv2DptConfig) and inputs.

The Tipsv2DptForDepthEstimation 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.

  • loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Classification (or regression if config.num_labels==1) loss.

  • predicted_depth (torch.FloatTensor of shape (batch_size, height, width)) — Predicted depth for each pixel.

  • 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.

  • 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.

Example:

>>> import torch
>>> from transformers import AutoModelForDepthEstimation, AutoImageProcessor
>>> from transformers.image_utils import load_image

>>> model_id = "google/tipsv2-b14-dpt"
>>> model = AutoModelForDepthEstimation.from_pretrained(model_id, device_map="auto")
>>> image_processor = AutoImageProcessor.from_pretrained(model_id)

>>> image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/room.jpg")
>>> inputs = image_processor(images=image, return_tensors="pt").to(model.device)

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

>>> results = image_processor.post_process_depth_estimation(outputs, target_sizes=[(image.height, image.width)])
>>> predicted_depth = results[0]["predicted_depth"]  # (height, width) tensor with predicted depth in meters

Tipsv2DptForNormalEstimation

class transformers.Tipsv2DptForNormalEstimation

< >

( config: Tipsv2DptConfig )

Parameters

  • config (Tipsv2DptConfig) — 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.

TIPSv2-DPT Model with a surface normal estimation head.

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

< >

( pixel_values: FloatTensorlabels: typing.Optional[torch.FloatTensor] = None**kwargs: Unpack ) → Tipsv2DptNormalEstimatorOutput or tuple(torch.FloatTensor)

Parameters

  • pixel_values (torch.FloatTensor of shape (batch_size, num_channels, image_size, image_size)) — The tensors corresponding to the input images. Pixel values can be obtained using Tipsv2DptImageProcessor. See Tipsv2DptImageProcessor.__call__() for details (processor_class uses Tipsv2DptImageProcessor for processing images).
  • labels (torch.FloatTensor of shape (batch_size, sequence_length), optional) — Labels for computing the masked language modeling loss. Indices should either be in [0, ..., config.vocab_size] or -100 (see input_ids docstring). Tokens with indices set to -100 are ignored (masked), the loss is only computed for the tokens with labels in [0, ..., config.vocab_size].

Returns

Tipsv2DptNormalEstimatorOutput or tuple(torch.FloatTensor)

A Tipsv2DptNormalEstimatorOutput 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 (Tipsv2DptConfig) and inputs.

The Tipsv2DptForNormalEstimation 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.

  • loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Normal estimation loss.
  • normals (torch.FloatTensor of shape (batch_size, num_labels, height, width)) — Raw normal map predictions as output by the model (unnormalized).
  • 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 stage) 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(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one per layer) of shape (batch_size, num_heads, sequence_length, sequence_length). Attentions weights after the attention softmax.

Example:

>>> import torch
>>> from transformers import Tipsv2DptForNormalEstimation, AutoImageProcessor
>>> from transformers.image_utils import load_image

>>> model_id = "google/tipsv2-b14-dpt"
>>> model = Tipsv2DptForNormalEstimation.from_pretrained(model_id, device_map="auto")
>>> image_processor = AutoImageProcessor.from_pretrained(model_id)

>>> image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/room.jpg")
>>> inputs = image_processor(images=image, return_tensors="pt").to(model.device)

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

>>> results = image_processor.post_process_normal_estimation(outputs, target_sizes=[(image.height, image.width)])
>>> normals = results[0]["normals"]  # (3, height, width) tensor with normals in XYZ format (L2-normalized)

Tipsv2DptForSemanticSegmentation

class transformers.Tipsv2DptForSemanticSegmentation

< >

( config: Tipsv2DptConfig )

Parameters

  • config (Tipsv2DptConfig) — 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.

TIPSv2-DPT Model with a semantic segmentation head.

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

< >

( pixel_values: FloatTensorlabels: typing.Optional[torch.LongTensor] = None**kwargs: Unpack ) → SemanticSegmenterOutput or tuple(torch.FloatTensor)

Parameters

  • pixel_values (torch.FloatTensor of shape (batch_size, num_channels, image_size, image_size)) — The tensors corresponding to the input images. Pixel values can be obtained using Tipsv2DptImageProcessor. See Tipsv2DptImageProcessor.__call__() for details (processor_class uses Tipsv2DptImageProcessor for processing images).
  • 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

SemanticSegmenterOutput or tuple(torch.FloatTensor)

A 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 (Tipsv2DptConfig) and inputs.

The Tipsv2DptForSemanticSegmentation 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.

  • 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.

Example:

>>> import torch
>>> from transformers import AutoModelForSemanticSegmentation, AutoImageProcessor
>>> from transformers.image_utils import load_image

>>> model_id = "google/tipsv2-b14-dpt"
>>> model = AutoModelForSemanticSegmentation.from_pretrained(model_id, device_map="auto")
>>> image_processor = AutoImageProcessor.from_pretrained(model_id)

>>> image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/room.jpg")
>>> inputs = image_processor(images=image, return_tensors="pt").to(model.device)

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

>>> results = image_processor.post_process_semantic_segmentation(outputs, target_sizes=[(image.height, image.width)])
>>> segmentation_map = results[0]  # (height, width) tensor with class ids
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