# EoMT ## Overview [The Encoder-only Mask Transformer]((https://www.tue-mps.org/eomt)) (EoMT) model was introduced in the CVPR 2025 Highlight Paper *[Your ViT is Secretly an Image Segmentation Model](https://huggingface.co/papers/2503.19108)* by Tommie Kerssies, Niccolò Cavagnero, Alexander Hermans, Narges Norouzi, Giuseppe Averta, Bastian Leibe, Gijs Dubbelman, and Daan de Geus. EoMT reveals Vision Transformers can perform image segmentation efficiently without task-specific components. The abstract from the paper is the following: *Vision Transformers (ViTs) have shown remarkable performance and scalability across various computer vision tasks. To apply single-scale ViTs to image segmentation, existing methods adopt a convolutional adapter to generate multi-scale features, a pixel decoder to fuse these features, and a Transformer decoder that uses the fused features to make predictions. In this paper, we show that the inductive biases introduced by these task-specific components can instead be learned by the ViT itself, given sufficiently large models and extensive pre-training. Based on these findings, we introduce the Encoder-only Mask Transformer (EoMT), which repurposes the plain ViT architecture to conduct image segmentation. With large-scale models and pre-training, EoMT obtains a segmentation accuracy similar to state-of-the-art models that use task-specific components. At the same time, EoMT is significantly faster than these methods due to its architectural simplicity, e.g., up to 4x faster with ViT-L. Across a range of model sizes, EoMT demonstrates an optimal balance between segmentation accuracy and prediction speed, suggesting that compute resources are better spent on scaling the ViT itself rather than adding architectural complexity.* This model was contributed by [Yaswanth Gali](https://huggingface.co/yaswanthgali). The original code can be found [here](https://github.com/tue-mps/eomt). ## Architecture Info The `EoMT` model uses a DINOv2-pretrained Vision Transformer with **register tokens** as its backbone. EoMT simplifies the segmentation pipeline by relying solely on the encoder, eliminating the need for task-specific decoders commonly used in prior approaches. Architecturally, EoMT introduces a small set of **learned queries** and a lightweight **mask prediction module**. These queries are injected into the final encoder blocks, enabling **joint attention** between image patches and object queries. During training, **masked attention** is applied to constrain each query to focus on its corresponding region—effectively mimicking cross-attention. This constraint is gradually phased out via a **mask annealing strategy**, allowing for **efficient, decoder-free inference** without compromising segmentation performance. The model supports semantic, instance, and panoptic segmentation using a unified architecture and task-specific post-processing. ## Usage Examples Use the Hugging Face implementation of EoMT for inference with pre-trained models. ### Semantic Segmentation The EoMT model performs semantic segmentation using sliding-window inference. The input image is resized such that the shorter side matches the target input size, then it is split into overlapping crops. Each crop is then passed through the model. After inference, the predicted logits from each crop are stitched back together and rescaled to the original image size to get the final segmentation mask. > **Note:** > If you want to use a custom target size for **semantic segmentation**, specify it in the following format: > `{"shortest_edge": 512}` > Notice that `longest_edge` is not provided here — this is intentional. For semantic segmentation, images are typically **scaled so that the shortest edge is greater than or equal to the target size** hence longest_edge is not necessary. ```python import matplotlib.pyplot as plt import requests import torch from PIL import Image from transformers import EomtForUniversalSegmentation, AutoImageProcessor model_id = "tue-mps/ade20k_semantic_eomt_large_512" processor = AutoImageProcessor.from_pretrained(model_id) model = EomtForUniversalSegmentation.from_pretrained(model_id) image = Image.open(requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw) inputs = processor( images=image, return_tensors="pt", ) with torch.inference_mode(): outputs = model(**inputs) # Prepare the original image size in the format (height, width) target_sizes = [(image.height, image.width)] # Post-process the model outputs to get final segmentation prediction preds = processor.post_process_semantic_segmentation( outputs, target_sizes=target_sizes, ) # Visualize the segmentation mask plt.imshow(preds[0]) plt.axis("off") plt.title("Semantic Segmentation") plt.show() ``` ### Instance Segmentation The EoMT model performs instance segmentation using padded inference. The input image is resized so that the longer side matches the target input size, and the shorter side is zero-padded to form a square. The resulting mask and class logits are combined through post-processing (adapted from Mask2Former) to produce a unified instance segmentation map, along with segment metadata like segment id, class labels and confidence scores. > **Note:** > To use a custom target size, specify the size as a dictionary in the following format: > `{"shortest_edge": 512, "longest_edge": 512}` > For both instance and panoptic segmentation, input images will be **scaled and padded** to this target size. ```python import matplotlib.pyplot as plt import requests import torch from PIL import Image from transformers import EomtForUniversalSegmentation, AutoImageProcessor model_id = "tue-mps/coco_instance_eomt_large_640" processor = AutoImageProcessor.from_pretrained(model_id) model = EomtForUniversalSegmentation.from_pretrained(model_id) image = Image.open(requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw) inputs = processor( images=image, return_tensors="pt", ) with torch.inference_mode(): outputs = model(**inputs) # Prepare the original image size in the format (height, width) target_sizes = [(image.height, image.width)] # Post-process the model outputs to get final segmentation prediction preds = processor.post_process_instance_segmentation( outputs, target_sizes=target_sizes, ) # Visualize the segmentation mask plt.imshow(preds[0]["segmentation"]) plt.axis("off") plt.title("Instance Segmentation") plt.show() ``` ### Panoptic Segmentation The EoMT model performs panoptic segmentation using the same padded inference strategy as in instance segmentation. After padding and normalization, the model predicts both thing (instances) and stuff (amorphous regions) classes. The resulting mask and class logits are combined through post-processing (adapted from Mask2Former) to produce a unified panoptic segmentation map, along with segment metadata like segment id, class labels and confidence scores. ```python import matplotlib.pyplot as plt import requests import torch from PIL import Image from transformers import EomtForUniversalSegmentation, AutoImageProcessor model_id = "tue-mps/coco_panoptic_eomt_large_640" processor = AutoImageProcessor.from_pretrained(model_id) model = EomtForUniversalSegmentation.from_pretrained(model_id) image = Image.open(requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw) inputs = processor( images=image, return_tensors="pt", ) with torch.inference_mode(): outputs = model(**inputs) # Prepare the original image size in the format (height, width) target_sizes = [(image.height, image.width)] # Post-process the model outputs to get final segmentation prediction preds = processor.post_process_panoptic_segmentation( outputs, target_sizes=target_sizes, ) # Visualize the panoptic segmentation mask plt.imshow(preds[0]["segmentation"]) plt.axis("off") plt.title("Panoptic Segmentation") plt.show() ``` ## EomtImageProcessor[[transformers.EomtImageProcessor]] #### transformers.EomtImageProcessor[[transformers.EomtImageProcessor]] [Source](https://github.com/huggingface/transformers/blob/v5.1.0/src/transformers/models/eomt/image_processing_eomt.py#L221) Constructs a EoMT image processor. The image processor can be used to prepare image(s) and optional targets for the model. This image processor inherits from [BaseImageProcessor](/docs/transformers/v5.1.0/en/main_classes/image_processor#transformers.BaseImageProcessor) which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. preprocesstransformers.EomtImageProcessor.preprocesshttps://github.com/huggingface/transformers/blob/v5.1.0/src/transformers/models/eomt/image_processing_eomt.py#L479[{"name": "images", "val": ": typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']]"}, {"name": "segmentation_maps", "val": ": list[dict[int, int]] | dict[int, int] | None = None"}, {"name": "instance_id_to_semantic_id", "val": ": dict[int, int] | None = None"}, {"name": "do_split_image", "val": ": bool | None = None"}, {"name": "do_resize", "val": ": bool | None = None"}, {"name": "size", "val": ": dict[str, int] | None = None"}, {"name": "resample", "val": ": PIL.Image.Resampling | None = None"}, {"name": "do_rescale", "val": ": bool | None = None"}, {"name": "rescale_factor", "val": ": float | None = None"}, {"name": "do_normalize", "val": ": bool | None = None"}, {"name": "do_pad", "val": ": bool | None = None"}, {"name": "image_mean", "val": ": float | list[float] | None = None"}, {"name": "image_std", "val": ": float | list[float] | None = None"}, {"name": "ignore_index", "val": ": int | None = None"}, {"name": "return_tensors", "val": ": str | transformers.utils.generic.TensorType | None = None"}, {"name": "data_format", "val": ": str | transformers.image_utils.ChannelDimension = "}, {"name": "input_data_format", "val": ": str | transformers.image_utils.ChannelDimension | None = None"}]- **images** (`ImageInput`) -- Image or batch of images to preprocess. - **segmentation_maps** (`ImageInput`, *optional*) -- The corresponding semantic segmentation maps with the pixel-wise annotations. - **instance_id_to_semantic_id** (`list[dict[int, int]]` or `dict[int, int]`, *optional*) -- A mapping between object instance ids and class ids. - **do_split_image** (`bool`, *optional*, defaults to `self.do_split_image`) -- Whether to split the input images into overlapping patches for semantic segmentation. - **do_resize** (`bool`, *optional*, defaults to `self.do_resize`) -- Whether to resize the input images. - **size** (`dict[str, int]`, *optional*, defaults to `self.size`) -- Target size as a dictionary with `"shortest_edge"` and `"longest_edge"` keys. - **resample** (`PILImageResampling`, *optional*, defaults to `self.resample`) -- Resampling filter to use when resizing. - **do_rescale** (`bool`, *optional*, defaults to `self.do_rescale`) -- Whether to rescale the input images by `rescale_factor`. - **rescale_factor** (`float`, *optional*, defaults to `self.rescale_factor`) -- Factor to scale image pixel values. - **do_normalize** (`bool`, *optional*, defaults to `self.do_normalize`) -- Whether to normalize the input images. - **do_pad** (`bool`, *optional*, defaults to `False`) -- Whether to pad the image. If `True`, will pad the patch dimension of the images in the batch to the largest number of patches in the batch. Padding will be applied to the bottom and right with zeros. - **image_mean** (`float` or `list[float]`, *optional*, defaults to `self.image_mean`) -- Mean for normalization. Single value or list for each channel. - **image_std** (`float` or `list[float]`, *optional*, defaults to `self.image_std`) -- Standard deviation for normalization. Single value or list for each channel. - **ignore_index** (`int`, *optional*) -- Label to be assigned to background pixels in segmentation maps. If provided, segmentation map pixels denoted with 0 (background) will be replaced with `ignore_index`. - **return_tensors** (`str` or `TensorType`, *optional*) -- The type of tensors to return. Can be `"pt"` or `"np"`. - **data_format** (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`) -- Channel format of the output image. Either `"channels_first"` or `"channels_last"`. - **input_data_format** (`ChannelDimension` or `str`, *optional*) -- Channel format of the input image.0 Preprocesses images or a batch of images. **Parameters:** do_resize (`bool`, *optional*, defaults to `True`) : Whether to resize the input to a certain `size`. size (`int`, *optional*, defaults to 640) : Resize the input to the given size. Only has an effect if `do_resize` is set to `True`. If size is a sequence like `(width, height)`, output size will be matched to this. If size is an int, smaller edge of the image will be matched to this number. i.e, if `height > width`, then image will be rescaled to `(size * height / width, size)`. resample (`int`, *optional*, defaults to `Resampling.BILINEAR`) : An optional resampling filter. This can be one of `PIL.Image.Resampling.NEAREST`, `PIL.Image.Resampling.BOX`, `PIL.Image.Resampling.BILINEAR`, `PIL.Image.Resampling.HAMMING`, `PIL.Image.Resampling.BICUBIC` or `PIL.Image.Resampling.LANCZOS`. Only has an effect if `do_resize` is set to `True`. do_rescale (`bool`, *optional*, defaults to `True`) : Whether to rescale the input to a certain `scale`. rescale_factor (`float`, *optional*, defaults to `1/ 255`) : Rescale the input by the given factor. Only has an effect if `do_rescale` is set to `True`. do_normalize (`bool`, *optional*, defaults to `True`) : Whether or not to normalize the input with mean and standard deviation. do_split_image (`bool`, *optional*, defaults to `False`) : Whether to split the input images into overlapping patches for semantic segmentation. If set to `True`, the input images will be split into patches of size `size["shortest_edge"]` with an overlap between patches. Otherwise, the input images will be padded to the target size. do_pad (`bool`, *optional*, defaults to `False`) : Whether to pad the image. If `True`, will pad the patch dimension of the images in the batch to the largest number of patches in the batch. Padding will be applied to the bottom and right with zeros. image_mean (`int`, *optional*, defaults to `[0.485, 0.456, 0.406]`) : The sequence of means for each channel, to be used when normalizing images. Defaults to the ImageNet mean. image_std (`int`, *optional*, defaults to `[0.229, 0.224, 0.225]`) : The sequence of standard deviations for each channel, to be used when normalizing images. Defaults to the ImageNet std. ignore_index (`int`, *optional*) : Label to be assigned to background pixels in segmentation maps. If provided, segmentation map pixels denoted with 0 (background) will be replaced with `ignore_index`. num_labels (`int`, *optional*) : The number of labels in the segmentation map. #### post_process_semantic_segmentation[[transformers.EomtImageProcessor.post_process_semantic_segmentation]] [Source](https://github.com/huggingface/transformers/blob/v5.1.0/src/transformers/models/eomt/image_processing_eomt.py#L789) Post-processes model outputs into final semantic segmentation prediction. #### post_process_instance_segmentation[[transformers.EomtImageProcessor.post_process_instance_segmentation]] [Source](https://github.com/huggingface/transformers/blob/v5.1.0/src/transformers/models/eomt/image_processing_eomt.py#L890) Post-processes model outputs into Instance Segmentation Predictions. #### post_process_panoptic_segmentation[[transformers.EomtImageProcessor.post_process_panoptic_segmentation]] [Source](https://github.com/huggingface/transformers/blob/v5.1.0/src/transformers/models/eomt/image_processing_eomt.py#L833) Post-processes model outputs into final panoptic segmentation prediction. ## EomtImageProcessorFast[[transformers.EomtImageProcessorFast]] #### transformers.EomtImageProcessorFast[[transformers.EomtImageProcessorFast]] [Source](https://github.com/huggingface/transformers/blob/v5.1.0/src/transformers/models/eomt/image_processing_eomt_fast.py#L104) Constructs a fast Eomt image processor. preprocesstransformers.EomtImageProcessorFast.preprocesshttps://github.com/huggingface/transformers/blob/v5.1.0/src/transformers/models/eomt/image_processing_eomt_fast.py#L161[{"name": "images", "val": ": typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']]"}, {"name": "segmentation_maps", "val": ": list[torch.Tensor] | None = None"}, {"name": "instance_id_to_semantic_id", "val": ": dict[int, int] | None = None"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.models.eomt.image_processing_eomt.EomtImageProcessorKwargs]"}]- **images** (`Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list, list, list]`) -- 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`. - **segmentation_maps** (`ImageInput`, *optional*) -- The segmentation maps to preprocess for corresponding images. - **instance_id_to_semantic_id** (`list[dict[int, int]]` or `dict[int, int]`, *optional*) -- A mapping between object instance ids and class ids. - **do_convert_rgb** (`bool | None.do_convert_rgb`) -- Whether to convert the image to RGB. - **do_resize** (`bool | None.do_resize`) -- Whether to resize the image. - **size** (`Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None]`) -- Describes the maximum input dimensions to the model. - **crop_size** (`Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None]`) -- Size of the output image after applying `center_crop`. - **resample** (`Annotated[Union[PILImageResampling, int, NoneType], None]`) -- 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 | None.do_rescale`) -- Whether to rescale the image. - **rescale_factor** (`float | None.rescale_factor`) -- Rescale factor to rescale the image by if `do_rescale` is set to `True`. - **do_normalize** (`bool | None.do_normalize`) -- Whether to normalize the image. - **image_mean** (`float | list[float] | tuple[float, ...] | None.image_mean`) -- Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`. - **image_std** (`float | list[float] | tuple[float, ...] | None.image_std`) -- Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to `True`. - **do_pad** (`bool | None.do_pad`) -- Whether to pad the image. Padding is done either to the largest size in the batch or to a fixed square size per image. The exact padding strategy depends on the model. - **pad_size** (`Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None]`) -- The size in `{"height": int, "width" int}` to pad the images to. Must be larger than any image size provided for preprocessing. If `pad_size` is not provided, images will be padded to the largest height and width in the batch. Applied only when `do_pad=True.` - **do_center_crop** (`bool | None.do_center_crop`) -- Whether to center crop the image. - **data_format** (`str | ~image_utils.ChannelDimension | None.data_format`) -- Only `ChannelDimension.FIRST` is supported. Added for compatibility with slow processors. - **input_data_format** (`str | ~image_utils.ChannelDimension | None.input_data_format`) -- The channel dimension format for the input image. If unset, the channel dimension format is inferred from the input image. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. - **device** (`Annotated[Union[str, torch.device, NoneType], None]`) -- The device to process the images on. If unset, the device is inferred from the input images. - **return_tensors** (`Annotated[str | ~utils.generic.TensorType | None, None]`) -- Returns stacked tensors if set to `pt, otherwise returns a list of tensors. - **disable_grouping** (`bool | None.disable_grouping`) -- Whether to disable grouping of images by size to process them individually and not in batches. If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157 - **image_seq_length** (`int | None.image_seq_length`) -- The number of image tokens to be used for each image in the input. Added for backward compatibility but this should be set as a processor attribute in future models. - **do_split_image** (`bool`, *optional*, defaults to `False`) -- Whether to split the input images into overlapping patches for semantic segmentation. If set to `True`, the input images will be split into patches of size `size["shortest_edge"]` with an overlap between patches. Otherwise, the input images will be padded to the target size. - **ignore_index** (`int`, *optional*) -- Label to be assigned to background pixels in segmentation maps. If provided, segmentation map pixels denoted with 0 (background) will be replaced with `ignore_index`.0``- **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. **Parameters:** images (`Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list, list, list]`) : 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`. segmentation_maps (`ImageInput`, *optional*) : The segmentation maps to preprocess for corresponding images. instance_id_to_semantic_id (`list[dict[int, int]]` or `dict[int, int]`, *optional*) : A mapping between object instance ids and class ids. do_convert_rgb (`bool | None.do_convert_rgb`) : Whether to convert the image to RGB. do_resize (`bool | None.do_resize`) : Whether to resize the image. size (`Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None]`) : Describes the maximum input dimensions to the model. crop_size (`Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None]`) : Size of the output image after applying `center_crop`. resample (`Annotated[Union[PILImageResampling, int, NoneType], None]`) : 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 | None.do_rescale`) : Whether to rescale the image. rescale_factor (`float | None.rescale_factor`) : Rescale factor to rescale the image by if `do_rescale` is set to `True`. do_normalize (`bool | None.do_normalize`) : Whether to normalize the image. image_mean (`float | list[float] | tuple[float, ...] | None.image_mean`) : Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`. image_std (`float | list[float] | tuple[float, ...] | None.image_std`) : Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to `True`. do_pad (`bool | None.do_pad`) : Whether to pad the image. Padding is done either to the largest size in the batch or to a fixed square size per image. The exact padding strategy depends on the model. pad_size (`Annotated[int | list[int] | tuple[int, ...] | dict[str, int] | None, None]`) : The size in `{"height": int, "width" int}` to pad the images to. Must be larger than any image size provided for preprocessing. If `pad_size` is not provided, images will be padded to the largest height and width in the batch. Applied only when `do_pad=True.` do_center_crop (`bool | None.do_center_crop`) : Whether to center crop the image. data_format (`str | ~image_utils.ChannelDimension | None.data_format`) : Only `ChannelDimension.FIRST` is supported. Added for compatibility with slow processors. input_data_format (`str | ~image_utils.ChannelDimension | None.input_data_format`) : The channel dimension format for the input image. If unset, the channel dimension format is inferred from the input image. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. device (`Annotated[Union[str, torch.device, NoneType], None]`) : The device to process the images on. If unset, the device is inferred from the input images. return_tensors (`Annotated[str | ~utils.generic.TensorType | None, None]`) : Returns stacked tensors if set to `pt, otherwise returns a list of tensors. disable_grouping (`bool | None.disable_grouping`) : Whether to disable grouping of images by size to process them individually and not in batches. If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157 image_seq_length (`int | None.image_seq_length`) : The number of image tokens to be used for each image in the input. Added for backward compatibility but this should be set as a processor attribute in future models. do_split_image (`bool`, *optional*, defaults to `False`) : Whether to split the input images into overlapping patches for semantic segmentation. If set to `True`, the input images will be split into patches of size `size["shortest_edge"]` with an overlap between patches. Otherwise, the input images will be padded to the target size. ignore_index (`int`, *optional*) : Label to be assigned to background pixels in segmentation maps. If provided, segmentation map pixels denoted with 0 (background) will be replaced with `ignore_index`. **Returns:** ```` - **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_semantic_segmentation[[transformers.EomtImageProcessorFast.post_process_semantic_segmentation]] [Source](https://github.com/huggingface/transformers/blob/v5.1.0/src/transformers/models/eomt/image_processing_eomt_fast.py#L392) Post-processes model outputs into final semantic segmentation prediction. #### post_process_instance_segmentation[[transformers.EomtImageProcessorFast.post_process_instance_segmentation]] [Source](https://github.com/huggingface/transformers/blob/v5.1.0/src/transformers/models/eomt/image_processing_eomt_fast.py#L493) Post-processes model outputs into Instance Segmentation Predictions. #### post_process_panoptic_segmentation[[transformers.EomtImageProcessorFast.post_process_panoptic_segmentation]] [Source](https://github.com/huggingface/transformers/blob/v5.1.0/src/transformers/models/eomt/image_processing_eomt_fast.py#L436) Post-processes model outputs into final panoptic segmentation prediction. ## EomtConfig[[transformers.EomtConfig]] #### transformers.EomtConfig[[transformers.EomtConfig]] [Source](https://github.com/huggingface/transformers/blob/v5.1.0/src/transformers/models/eomt/configuration_eomt.py#L24) This is the configuration class to store the configuration of a [EomtForUniversalSegmentation](/docs/transformers/v5.1.0/en/model_doc/eomt#transformers.EomtForUniversalSegmentation). It is used to instantiate an EoMT 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 EoMT [tue-mps/coco_panoptic_eomt_large_640](https://huggingface.co/tue-mps/coco_panoptic_eomt_large_640) architecture. Configuration objects inherit from [PreTrainedConfig](/docs/transformers/v5.1.0/en/main_classes/configuration#transformers.PreTrainedConfig) and can be used to control the model outputs. Read the documentation from [PreTrainedConfig](/docs/transformers/v5.1.0/en/main_classes/configuration#transformers.PreTrainedConfig) for more information. Example: ```python >>> from transformers import EomtConfig, EomtForUniversalSegmentation >>> # Initialize configuration >>> config = EomtConfig() >>> # Initialize model >>> model = EomtForUniversalSegmentation(config) >>> # Access config >>> config = model.config ``` **Parameters:** hidden_size (`int`, *optional*, defaults to 1024) : Dimensionality of the hidden representations. num_hidden_layers (`int`, *optional*, defaults to 24) : Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 16) : Number of attention heads in each attention layer. mlp_ratio (`int`, *optional*, defaults to 4) : Ratio of the MLP hidden dimensionality to the hidden size. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`) : The non-linear activation function (function or string) in the encoder. hidden_dropout_prob (`float`, *optional*, defaults to 0.0) : The dropout probability for all fully connected layers in the embeddings and encoder. 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-06) : The epsilon used by the layer normalization layers. image_size (`int`, *optional*, defaults to 640) : The size (resolution) of each input image. patch_size (`int`, *optional*, defaults to 16) : The size (resolution) of each patch. num_channels (`int`, *optional*, defaults to 3) : The number of input channels. layerscale_value (`float`, *optional*, defaults to 1.0) : Initial value for the LayerScale parameter. drop_path_rate (`float`, *optional*, defaults to 0.0) : The stochastic depth rate (drop path) used during training. num_upscale_blocks (`int`, *optional*, defaults to 2) : Number of upsampling blocks used in the decoder or segmentation head. attention_dropout (`float`, *optional*, defaults to 0.0) : Dropout probability applied after attention projection. use_swiglu_ffn (`bool`, *optional*, defaults to `False`) : Whether to use the SwiGLU feedforward neural network. num_blocks (`int`, *optional*, defaults to 4) : Number of feature blocks or stages in the architecture. no_object_weight (`float`, *optional*, defaults to 0.1) : Loss weight for the 'no object' class in panoptic/instance segmentation. class_weight (`float`, *optional*, defaults to 2.0) : Loss weight for classification targets. mask_weight (`float`, *optional*, defaults to 5.0) : Loss weight for mask prediction. dice_weight (`float`, *optional*, defaults to 5.0) : Loss weight for the dice loss component. train_num_points (`int`, *optional*, defaults to 12544) : Number of points to sample for mask loss computation during training. oversample_ratio (`float`, *optional*, defaults to 3.0) : Oversampling ratio used in point sampling for mask training. importance_sample_ratio (`float`, *optional*, defaults to 0.75) : Ratio of points to sample based on importance during training. num_queries (`int`, *optional*, defaults to 200) : Number of object queries in the Transformer. num_register_tokens (`int`, *optional*, defaults to 4) : Number of learnable register tokens added to the transformer input. ## EomtForUniversalSegmentation[[transformers.EomtForUniversalSegmentation]] #### transformers.EomtForUniversalSegmentation[[transformers.EomtForUniversalSegmentation]] [Source](https://github.com/huggingface/transformers/blob/v5.1.0/src/transformers/models/eomt/modeling_eomt.py#L1035) The EoMT Model with head on top for instance/semantic/panoptic segmentation. This model inherits from [PreTrainedModel](/docs/transformers/v5.1.0/en/main_classes/model#transformers.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](https://pytorch.org/docs/stable/nn.html#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. forwardtransformers.EomtForUniversalSegmentation.forwardhttps://github.com/huggingface/transformers/blob/v5.1.0/src/transformers/models/eomt/modeling_eomt.py#L1093[{"name": "pixel_values", "val": ": Tensor"}, {"name": "mask_labels", "val": ": list[torch.Tensor] | None = None"}, {"name": "class_labels", "val": ": list[torch.Tensor] | None = None"}, {"name": "patch_offsets", "val": ": list[torch.Tensor] | None = None"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]- **pixel_values** (`torch.Tensor` of shape `(batch_size, num_channels, image_size, image_size)`) -- The tensors corresponding to the input images. Pixel values can be obtained using [EomtImageProcessorFast](/docs/transformers/v5.1.0/en/model_doc/eomt#transformers.EomtImageProcessorFast). See [EomtImageProcessorFast.__call__()](/docs/transformers/v5.1.0/en/model_doc/fuyu#transformers.FuyuImageProcessor.__call__) for details (`processor_class` uses [EomtImageProcessorFast](/docs/transformers/v5.1.0/en/model_doc/eomt#transformers.EomtImageProcessorFast) for processing images). - **mask_labels** (`list[torch.Tensor]`, *optional*) -- list of mask labels of shape `(num_labels, height, width)` to be fed to a model - **class_labels** (`list[torch.LongTensor]`, *optional*) -- list of target class labels of shape `(num_labels, height, width)` to be fed to a model. They identify the labels of `mask_labels`, e.g. the label of `mask_labels[i][j]` if `class_labels[i][j]`. - **patch_offsets** (`list[torch.Tensor]`, *optional*) -- list of tuples indicating the image index and start and end positions of patches for semantic segmentation.0`transformers.models.eomt.modeling_eomt.EomtForUniversalSegmentationOutput` or `tuple(torch.FloatTensor)`A `transformers.models.eomt.modeling_eomt.EomtForUniversalSegmentationOutput` 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 ([EomtConfig](/docs/transformers/v5.1.0/en/model_doc/eomt#transformers.EomtConfig)) and inputs. - **loss** (`torch.Tensor`, *optional*) -- The computed loss, returned when labels are present. - **class_queries_logits** (`torch.FloatTensor | None.class_queries_logits`, defaults to `None`) -- A tensor of shape `(batch_size, num_queries, num_labels + 1)` representing the proposed classes for each query. Note the `+ 1` is needed because we incorporate the null class. - **masks_queries_logits** (`torch.FloatTensor | None.masks_queries_logits`, defaults to `None`) -- A tensor of shape `(batch_size, num_queries, height, width)` representing the proposed masks for each query. - **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`) -- Last hidden states (final feature map) of the last layer. - **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 all layers of the model. - **attentions** (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `tuple(torch.FloatTensor)` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Self and Cross Attentions weights from transformer decoder. - **patch_offsets** (`list[torch.Tensor]`, *optional*) -- list of tuples indicating the image index and start and end positions of patches for semantic segmentation. The [EomtForUniversalSegmentation](/docs/transformers/v5.1.0/en/model_doc/eomt#transformers.EomtForUniversalSegmentation) 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. **Parameters:** config ([EomtConfig](/docs/transformers/v5.1.0/en/model_doc/eomt#transformers.EomtConfig)) : 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/v5.1.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights. **Returns:** ``transformers.models.eomt.modeling_eomt.EomtForUniversalSegmentationOutput` or `tuple(torch.FloatTensor)`` A `transformers.models.eomt.modeling_eomt.EomtForUniversalSegmentationOutput` 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 ([EomtConfig](/docs/transformers/v5.1.0/en/model_doc/eomt#transformers.EomtConfig)) and inputs. - **loss** (`torch.Tensor`, *optional*) -- The computed loss, returned when labels are present. - **class_queries_logits** (`torch.FloatTensor | None.class_queries_logits`, defaults to `None`) -- A tensor of shape `(batch_size, num_queries, num_labels + 1)` representing the proposed classes for each query. Note the `+ 1` is needed because we incorporate the null class. - **masks_queries_logits** (`torch.FloatTensor | None.masks_queries_logits`, defaults to `None`) -- A tensor of shape `(batch_size, num_queries, height, width)` representing the proposed masks for each query. - **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`) -- Last hidden states (final feature map) of the last layer. - **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 all layers of the model. - **attentions** (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `tuple(torch.FloatTensor)` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Self and Cross Attentions weights from transformer decoder. - **patch_offsets** (`list[torch.Tensor]`, *optional*) -- list of tuples indicating the image index and start and end positions of patches for semantic segmentation.