Buckets:

hf-doc-build
/

doc

Files

xet

hf-doc-build/doc / transformers /main /ja /model_doc /clipseg.md

HuggingFaceDocBuilder

about 6 hours ago

preview code

download

raw

50.8 kB

	# CLIPSeg

	## Overview

	CLIPSeg モデルは、Timo Lüddecke, Alexander Ecker によって [Image Segmentation using Text and Image Prompts](https://huggingface.co/papers/2112.10003) で提案されました。
	そしてアレクサンダー・エッカー。 CLIPSeg は、ゼロショットおよびワンショット画像セグメンテーションのために、凍結された [CLIP](clip) モデルの上に最小限のデコーダを追加します。

	論文の要約は次のとおりです。

	*画像のセグメンテーションは通常、トレーニングによって解決されます。
	オブジェクトクラスの固定セットのモデル。後で追加のクラスやより複雑なクエリを組み込むとコストがかかります
	これらの式を含むデータセットでモデルを再トレーニングする必要があるためです。ここでシステムを提案します
	任意の情報に基づいて画像セグメンテーションを生成できます。
	テスト時にプロンプトが表示されます。プロンプトはテキストまたは
	画像。このアプローチにより、統一されたモデルを作成できます。
	3 つの一般的なセグメンテーションタスクについて (1 回トレーニング済み)
	参照式のセグメンテーション、ゼロショットセグメンテーション、ワンショットセグメンテーションという明確な課題が伴います。
	CLIP モデルをバックボーンとして構築し、これをトランスベースのデコーダで拡張して、高密度なデータ通信を可能にします。
	予測。の拡張バージョンでトレーニングした後、
	PhraseCut データセット、私たちのシステムは、フリーテキストプロンプトまたは
	クエリを表す追加の画像。後者の画像ベースのプロンプトのさまざまなバリエーションを詳細に分析します。
	この新しいハイブリッド入力により、動的適応が可能になります。
	前述の 3 つのセグメンテーションタスクのみですが、
	テキストまたは画像をクエリするバイナリセグメンテーションタスクに
	定式化することができる。最後に、システムがうまく適応していることがわかりました
	アフォーダンスまたはプロパティを含む一般化されたクエリ*

	<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/clipseg_architecture.png"
	alt="描画" width="600"/>

	CLIPSeg の概要。元の論文から抜粋。

	このモデルは、[nielsr](https://huggingface.co/nielsr) によって提供されました。
	元のコードは [ここ](https://github.com/timojl/clipseg) にあります。

	## Usage tips

	- [CLIPSegForImageSegmentation](/docs/transformers/main/ja/model_doc/clipseg#transformers.CLIPSegForImageSegmentation) は、[CLIPSegModel](/docs/transformers/main/ja/model_doc/clipseg#transformers.CLIPSegModel) の上にデコーダを追加します。後者は [CLIPModel](/docs/transformers/main/ja/model_doc/clip#transformers.CLIPModel) と同じです。
	- [CLIPSegForImageSegmentation](/docs/transformers/main/ja/model_doc/clipseg#transformers.CLIPSegForImageSegmentation) は、テスト時に任意のプロンプトに基づいて画像セグメンテーションを生成できます。プロンプトはテキストのいずれかです
	(`input_ids` としてモデルに提供される) または画像 (`conditional_pixel_values` としてモデルに提供される)。カスタムを提供することもできます
	条件付き埋め込み (`conditional_embeddings`としてモデルに提供されます)。

	## Resources

	CLIPSeg の使用を開始するのに役立つ、公式 Hugging Face およびコミュニティ (🌎 で示されている) リソースのリスト。ここに含めるリソースの送信に興味がある場合は、お気軽にプルリクエストを開いてください。審査させていただきます。リソースは、既存のリソースを複製するのではなく、何か新しいものを示すことが理想的です。

	- [CLIPSeg を使用したゼロショット画像セグメンテーション](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/CLIPSeg/Zero_shot_image_segmentation_with_CLIPSeg.ipynb) を説明するノートブック。

	## CLIPSegConfig[[transformers.CLIPSegConfig]]

	#### transformers.CLIPSegConfig[[transformers.CLIPSegConfig]]

	[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/clipseg/configuration_clipseg.py#L124)

	This is the configuration class to store the configuration of a CLIPSegModel. It is used to instantiate a Clipseg
	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 [CIDAS/clipseg-rd64](https://huggingface.co/CIDAS/clipseg-rd64)

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

	Example:

	```python
	>>> from transformers import CLIPSegConfig, CLIPSegModel

	>>> # Initializing a CLIPSegConfig with CIDAS/clipseg-rd64 style configuration
	>>> configuration = CLIPSegConfig()

	>>> # Initializing a CLIPSegModel (with random weights) from the CIDAS/clipseg-rd64 style configuration
	>>> model = CLIPSegModel(configuration)

	>>> # Accessing the model configuration
	>>> configuration = model.config

	>>> # We can also initialize a CLIPSegConfig from a CLIPSegTextConfig and a CLIPSegVisionConfig

	>>> # Initializing a CLIPSegText and CLIPSegVision configuration
	>>> config_text = CLIPSegTextConfig()
	>>> config_vision = CLIPSegVisionConfig()

	>>> config = CLIPSegConfig(text_config=config_text, vision_config=config_vision)
	```

	Parameters:

	text_config (`Union[dict, ~models.clipseg.configuration_clipseg.CLIPSegTextConfig]`, optional) : The config object or dictionary of the text backbone.

	vision_config (`Union[dict, ~models.clipseg.configuration_clipseg.CLIPSegVisionConfig]`, optional) : The config object or dictionary of the vision backbone.

	projection_dim (`int`, optional, defaults to `512`) : Dimensionality of text and vision projection layers.

	logit_scale_init_value (`Union[float, int]`, optional, defaults to `2.6592`) : The initial value of the logit_scale parameter.

	initializer_factor (`float`, optional, defaults to `1.0`) : A factor for initializing all weight matrices (should be kept to 1, used internally for initialization testing).

	extract_layers (`list[int]`, optional, defaults to `[3, 6, 9]`) : Layers to extract when forwarding the query image through the frozen visual backbone of CLIP.

	reduce_dim (`int`, optional, defaults to 64) : Dimensionality to reduce the CLIP vision embedding.

	decoder_num_attention_heads (`int`, optional, defaults to `4`) : Number of attention heads for each attention layer in the Transformer decoder.

	decoder_attention_dropout (`Union[float, int]`, optional, defaults to `0.0`) : The dropout ratio for the attention probabilities.

	decoder_hidden_act (`str`, optional, defaults to `quick_gelu`) : The non-linear activation function (function or string) in the decoder. For example, `"gelu"`, `"relu"`, `"silu"`, etc.

	decoder_intermediate_size (`int`, optional, defaults to `2048`) : Dimension of the MLP representations.

	conditional_layer (`int`, optional, defaults to 0) : The layer to use of the Transformer encoder whose activations will be combined with the condition embeddings using FiLM (Feature-wise Linear Modulation). If 0, the last layer is used.

	use_complex_transposed_convolution (`bool`, optional, defaults to `False`) : Whether to use a more complex transposed convolution in the decoder, enabling more fine-grained segmentation..

	## CLIPSegTextConfig[[transformers.CLIPSegTextConfig]]

	#### transformers.CLIPSegTextConfig[[transformers.CLIPSegTextConfig]]

	[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/clipseg/configuration_clipseg.py#L31)

	This is the configuration class to store the configuration of a CLIPSegModel. It is used to instantiate a Clipseg
	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 [CIDAS/clipseg-rd64](https://huggingface.co/CIDAS/clipseg-rd64)

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

	Example:

	```python
	>>> from transformers import CLIPSegTextConfig, CLIPSegTextModel

	>>> # Initializing a CLIPSegTextConfig with CIDAS/clipseg-rd64 style configuration
	>>> configuration = CLIPSegTextConfig()

	>>> # Initializing a CLIPSegTextModel (with random weights) from the CIDAS/clipseg-rd64 style configuration
	>>> model = CLIPSegTextModel(configuration)

	>>> # Accessing the model configuration
	>>> configuration = model.config
	```

	Parameters:

	vocab_size (`int`, optional, defaults to `49408`) : Vocabulary size of the model. Defines the number of different tokens that can be represented by the `input_ids`.

	hidden_size (`int`, optional, defaults to `512`) : Dimension of the hidden representations.

	intermediate_size (`int`, optional, defaults to `2048`) : Dimension of the MLP representations.

	num_hidden_layers (`int`, optional, defaults to `12`) : Number of hidden layers in the Transformer decoder.

	num_attention_heads (`int`, optional, defaults to `8`) : Number of attention heads for each attention layer in the Transformer decoder.

	max_position_embeddings (`int`, optional, defaults to `77`) : The maximum sequence length that this model might ever be used with.

	hidden_act (`str`, optional, defaults to `quick_gelu`) : The non-linear activation function (function or string) in the decoder. For example, `"gelu"`, `"relu"`, `"silu"`, etc.

	layer_norm_eps (`float`, optional, defaults to `1e-05`) : The epsilon used by the layer normalization layers.

	attention_dropout (`Union[int, float]`, optional, defaults to `0.0`) : The dropout ratio for the attention probabilities.

	initializer_range (`float`, optional, defaults to `0.02`) : The standard deviation of the truncated_normal_initializer for initializing all weight matrices.

	initializer_factor (`float`, optional, defaults to `1.0`) : A factor for initializing all weight matrices (should be kept to 1, used internally for initialization testing).

	pad_token_id (`int`, optional, defaults to `1`) : Token id used for padding in the vocabulary.

	bos_token_id (`int`, optional, defaults to `49406`) : Token id used for beginning-of-stream in the vocabulary.

	eos_token_id (`Union[int, list[int]]`, optional, defaults to `49407`) : Token id used for end-of-stream in the vocabulary.

	## CLIPSegVisionConfig[[transformers.CLIPSegVisionConfig]]

	#### transformers.CLIPSegVisionConfig[[transformers.CLIPSegVisionConfig]]

	[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/clipseg/configuration_clipseg.py#L80)

	This is the configuration class to store the configuration of a CLIPSegModel. It is used to instantiate a Clipseg
	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 [CIDAS/clipseg-rd64](https://huggingface.co/CIDAS/clipseg-rd64)

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

	Example:

	```python
	>>> from transformers import CLIPSegVisionConfig, CLIPSegVisionModel

	>>> # Initializing a CLIPSegVisionConfig with CIDAS/clipseg-rd64 style configuration
	>>> configuration = CLIPSegVisionConfig()

	>>> # Initializing a CLIPSegVisionModel (with random weights) from the CIDAS/clipseg-rd64 style configuration
	>>> model = CLIPSegVisionModel(configuration)

	>>> # Accessing the model configuration
	>>> configuration = model.config
	```

	Parameters:

	hidden_size (`int`, optional, defaults to `768`) : Dimension of the hidden representations.

	intermediate_size (`int`, optional, defaults to `3072`) : Dimension of the MLP representations.

	num_hidden_layers (`int`, optional, defaults to `12`) : Number of hidden layers in the Transformer decoder.

	num_attention_heads (`int`, optional, defaults to `12`) : Number of attention heads for each attention layer in the Transformer decoder.

	num_channels (`int`, optional, defaults to `3`) : The number of input channels.

	image_size (`Union[int, list[int], tuple[int, int]]`, optional, defaults to `224`) : The size (resolution) of each image.

	patch_size (`Union[int, list[int], tuple[int, int]]`, optional, defaults to `32`) : The size (resolution) of each patch.

	hidden_act (`str`, optional, defaults to `quick_gelu`) : The non-linear activation function (function or string) in the decoder. For example, `"gelu"`, `"relu"`, `"silu"`, etc.

	layer_norm_eps (`float`, optional, defaults to `1e-05`) : The epsilon used by the layer normalization layers.

	attention_dropout (`Union[int, float]`, optional, defaults to `0.0`) : The dropout ratio for the attention probabilities.

	initializer_range (`float`, optional, defaults to `0.02`) : The standard deviation of the truncated_normal_initializer for initializing all weight matrices.

	initializer_factor (`float`, optional, defaults to `1.0`) : A factor for initializing all weight matrices (should be kept to 1, used internally for initialization testing).

	## CLIPSegProcessor[[transformers.CLIPSegProcessor]]

	#### transformers.CLIPSegProcessor[[transformers.CLIPSegProcessor]]

	[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/clipseg/processing_clipseg.py#L24)

	Constructs a CLIPSegProcessor which wraps a image processor and a tokenizer into a single processor.

	[CLIPSegProcessor](/docs/transformers/main/ja/model_doc/clipseg#transformers.CLIPSegProcessor) offers all the functionalities of `ViTImageProcessor` and [CLIPTokenizer](/docs/transformers/main/ja/model_doc/clip#transformers.CLIPTokenizer). See the
	`~ViTImageProcessor` and [~CLIPTokenizer](/docs/transformers/main/ja/model_doc/clip#transformers.CLIPTokenizer) for more information.

	Parameters:

	image_processor (`ViTImageProcessor`) : The image processor is a required input.

	tokenizer (`CLIPTokenizer`) : The tokenizer is a required input.

	## CLIPSegModel[[transformers.CLIPSegModel]]

	#### transformers.CLIPSegModel[[transformers.CLIPSegModel]]

	[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/clipseg/modeling_clipseg.py#L789)

	The bare Clipseg Model outputting raw hidden-states without any specific head on top.

	This model inherits from [PreTrainedModel](/docs/transformers/main/ja/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.CLIPSegModel.forwardhttps://github.com/huggingface/transformers/blob/main/src/transformers/models/clipseg/modeling_clipseg.py#L883[{"name": "input_ids", "val": ": torch.LongTensor \| None = None"}, {"name": "pixel_values", "val": ": torch.FloatTensor \| None = None"}, {"name": "attention_mask", "val": ": torch.Tensor \| None = None"}, {"name": "position_ids", "val": ": torch.LongTensor \| None = None"}, {"name": "return_loss", "val": ": bool \| None = None"}, {"name": "interpolate_pos_encoding", "val": ": bool = True"}, {"name": "kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]- input_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional) --
	Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

	Indices can be obtained using [AutoTokenizer](/docs/transformers/main/ja/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/main/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
	[PreTrainedTokenizer.__call__()](/docs/transformers/main/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

	[What are input IDs?](../glossary#input-ids)
	- pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`, optional) --
	The tensors corresponding to the input images. Pixel values can be obtained using
	`ViTImageProcessor`. See `ViTImageProcessor.__call__()` for details ([CLIPSegProcessor](/docs/transformers/main/ja/model_doc/clipseg#transformers.CLIPSegProcessor) uses
	`ViTImageProcessor` for processing images).
	- attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) --
	Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

	- 1 for tokens that are not masked,
	- 0 for tokens that are masked.

	[What are attention masks?](../glossary#attention-mask)
	- position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional) --
	Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`.

	[What are position IDs?](../glossary#position-ids)
	- return_loss (`bool`, optional) --
	Whether or not to return the contrastive loss.
	- interpolate_pos_encoding (`bool`, optional, defaults to `True`) --
	Whether to interpolate the pre-trained position encodings.0`CLIPSegOutput` or `tuple(torch.FloatTensor)`A `CLIPSegOutput` 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 ([CLIPSegConfig](/docs/transformers/main/ja/model_doc/clipseg#transformers.CLIPSegConfig)) and inputs.
	The [CLIPSegModel](/docs/transformers/main/ja/model_doc/clipseg#transformers.CLIPSegModel) 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 `return_loss` is `True`) -- Contrastive loss for image-text similarity.
	- logits_per_image (`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`) -- The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text
	similarity scores.
	- logits_per_text (`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`) -- The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image
	similarity scores.
	- text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim`) -- The text embeddings obtained by applying the projection layer to the pooled output of [CLIPSegTextModel](/docs/transformers/main/ja/model_doc/clipseg#transformers.CLIPSegTextModel).
	- image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim`) -- The image embeddings obtained by applying the projection layer to the pooled output of [CLIPSegVisionModel](/docs/transformers/main/ja/model_doc/clipseg#transformers.CLIPSegVisionModel).
	- text_model_output (`~modeling_outputs.BaseModelOutputWithPooling`, defaults to `None`) -- The output of the [CLIPSegTextModel](/docs/transformers/main/ja/model_doc/clipseg#transformers.CLIPSegTextModel).
	- vision_model_output (`~modeling_outputs.BaseModelOutputWithPooling`, defaults to `None`) -- The output of the [CLIPSegVisionModel](/docs/transformers/main/ja/model_doc/clipseg#transformers.CLIPSegVisionModel).

	Examples:

	```python
	>>> import torch
	>>> from transformers import AutoProcessor, CLIPSegModel
	>>> from transformers.image_utils import load_image

	>>> processor = AutoProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
	>>> model = CLIPSegModel.from_pretrained("CIDAS/clipseg-rd64-refined")

	>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
	>>> image = load_image(url)

	>>> inputs = processor(
	... text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True
	... )

	>>> with torch.inference_mode():
	... outputs = model(**inputs)
	>>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score
	>>> probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
	```

	Parameters:

	config ([CLIPSegConfig](/docs/transformers/main/ja/model_doc/clipseg#transformers.CLIPSegConfig)) : 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/main/ja/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

	Returns:

	``CLIPSegOutput` or `tuple(torch.FloatTensor)``

	A `CLIPSegOutput` 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 ([CLIPSegConfig](/docs/transformers/main/ja/model_doc/clipseg#transformers.CLIPSegConfig)) and inputs.
	#### get_text_features[[transformers.CLIPSegModel.get_text_features]]

	[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/clipseg/modeling_clipseg.py#L810)

	- 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.
	- pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`) -- Last layer hidden-state of the first token of the sequence (classification token) after further processing
	through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
	the classification token after processing through a linear layer and a tanh activation function. The linear
	layer weights are trained from the next sentence prediction (classification) objective during pretraining.
	- 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.

	Examples:

	```python
	>>> import torch
	>>> from transformers import AutoTokenizer, CLIPSegModel

	>>> tokenizer = AutoTokenizer.from_pretrained("CIDAS/clipseg-rd64-refined")
	>>> model = CLIPSegModel.from_pretrained("CIDAS/clipseg-rd64-refined")

	>>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt")
	>>> with torch.inference_mode():
	... text_features = model.get_text_features(**inputs)
	```

	Parameters:

	input_ids (`torch.Tensor` of shape `(batch_size, sequence_length)`) : Indices of input sequence tokens in the vocabulary. Padding will be ignored by default. Indices can be obtained using [AutoTokenizer](/docs/transformers/main/ja/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/main/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and [PreTrainedTokenizer.__call__()](/docs/transformers/main/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details. [What are input IDs?](../glossary#input-ids)

	attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) : Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are not masked, - 0 for tokens that are masked. [What are attention masks?](../glossary#attention-mask)

	position_ids (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) : Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids)

	Returns:

	`[BaseModelOutputWithPooling](/docs/transformers/main/ja/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)``

	A [BaseModelOutputWithPooling](/docs/transformers/main/ja/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) 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 ([CLIPSegConfig](/docs/transformers/main/ja/model_doc/clipseg#transformers.CLIPSegConfig)) and inputs.
	#### get_image_features[[transformers.CLIPSegModel.get_image_features]]

	[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/clipseg/modeling_clipseg.py#L845)

	- 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.
	- pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`) -- Last layer hidden-state of the first token of the sequence (classification token) after further processing
	through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
	the classification token after processing through a linear layer and a tanh activation function. The linear
	layer weights are trained from the next sentence prediction (classification) objective during pretraining.
	- 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.

	Examples:

	```python
	>>> import torch
	>>> from transformers import AutoProcessor, CLIPSegModel
	>>> from transformers.image_utils import load_image

	>>> processor = AutoProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
	>>> model = CLIPSegModel.from_pretrained("CIDAS/clipseg-rd64-refined")

	>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
	>>> image = load_image(url)

	>>> inputs = processor(images=image, return_tensors="pt")

	>>> with torch.inference_mode():
	... image_features = model.get_image_features(**inputs)
	```

	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 `ViTImageProcessor`. See `ViTImageProcessor.__call__()` for details ([CLIPSegProcessor](/docs/transformers/main/ja/model_doc/clipseg#transformers.CLIPSegProcessor) uses `ViTImageProcessor` for processing images).

	interpolate_pos_encoding (`bool`, optional, defaults to `True`) : Whether to interpolate the pre-trained position encodings.

	Returns:

	`[BaseModelOutputWithPooling](/docs/transformers/main/ja/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)``

	A [BaseModelOutputWithPooling](/docs/transformers/main/ja/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) 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 ([CLIPSegConfig](/docs/transformers/main/ja/model_doc/clipseg#transformers.CLIPSegConfig)) and inputs.

	## CLIPSegTextModel[[transformers.CLIPSegTextModel]]

	#### transformers.CLIPSegTextModel[[transformers.CLIPSegTextModel]]

	[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/clipseg/modeling_clipseg.py#L600)

	The text model from CLIPSEG without any head or projection on top.

	This model inherits from [PreTrainedModel](/docs/transformers/main/ja/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.CLIPSegTextModel.forwardhttps://github.com/huggingface/transformers/blob/main/src/transformers/models/clipseg/modeling_clipseg.py#L616[{"name": "input_ids", "val": ": torch.Tensor \| None = None"}, {"name": "attention_mask", "val": ": torch.Tensor \| None = None"}, {"name": "position_ids", "val": ": torch.Tensor \| None = None"}, {"name": "kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]- input_ids** (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) --
	Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

	Indices can be obtained using [AutoTokenizer](/docs/transformers/main/ja/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/main/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
	[PreTrainedTokenizer.__call__()](/docs/transformers/main/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

	[What are input IDs?](../glossary#input-ids)
	- attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) --
	Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

	- 1 for tokens that are not masked,
	- 0 for tokens that are masked.

	[What are attention masks?](../glossary#attention-mask)
	- position_ids (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) --
	Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`.

	[What are position IDs?](../glossary#position-ids)0[BaseModelOutputWithPooling](/docs/transformers/main/ja/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)`A [BaseModelOutputWithPooling](/docs/transformers/main/ja/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) 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 ([CLIPSegConfig](/docs/transformers/main/ja/model_doc/clipseg#transformers.CLIPSegConfig)) and inputs.
	The [CLIPSegTextModel](/docs/transformers/main/ja/model_doc/clipseg#transformers.CLIPSegTextModel) 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.

	- 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.
	- pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`) -- Last layer hidden-state of the first token of the sequence (classification token) after further processing
	through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
	the classification token after processing through a linear layer and a tanh activation function. The linear
	layer weights are trained from the next sentence prediction (classification) objective during pretraining.
	- 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.

	Examples:

	```python
	>>> from transformers import AutoTokenizer, CLIPSegTextModel

	>>> tokenizer = AutoTokenizer.from_pretrained("CIDAS/clipseg-rd64-refined")
	>>> model = CLIPSegTextModel.from_pretrained("CIDAS/clipseg-rd64-refined")

	>>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt")

	>>> outputs = model(**inputs)
	>>> last_hidden_state = outputs.last_hidden_state
	>>> pooled_output = outputs.pooler_output # pooled (EOS token) states
	```

	Parameters:

	config ([CLIPSegTextConfig](/docs/transformers/main/ja/model_doc/clipseg#transformers.CLIPSegTextConfig)) : 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/main/ja/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

	Returns:

	`[BaseModelOutputWithPooling](/docs/transformers/main/ja/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)``

	A [BaseModelOutputWithPooling](/docs/transformers/main/ja/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) 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 ([CLIPSegConfig](/docs/transformers/main/ja/model_doc/clipseg#transformers.CLIPSegConfig)) and inputs.

	## CLIPSegVisionModel[[transformers.CLIPSegVisionModel]]

	#### transformers.CLIPSegVisionModel[[transformers.CLIPSegVisionModel]]

	[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/clipseg/modeling_clipseg.py#L700)

	The vision model from CLIPSEG without any head or projection on top.

	This model inherits from [PreTrainedModel](/docs/transformers/main/ja/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.CLIPSegVisionModel.forwardhttps://github.com/huggingface/transformers/blob/main/src/transformers/models/clipseg/modeling_clipseg.py#L716[{"name": "pixel_values", "val": ": torch.FloatTensor \| None"}, {"name": "interpolate_pos_encoding", "val": ": bool \| None = True"}, {"name": "kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]- pixel_values** (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`, optional) --
	The tensors corresponding to the input images. Pixel values can be obtained using
	`ViTImageProcessor`. See `ViTImageProcessor.__call__()` for details ([CLIPSegProcessor](/docs/transformers/main/ja/model_doc/clipseg#transformers.CLIPSegProcessor) uses
	`ViTImageProcessor` for processing images).
	- interpolate_pos_encoding (`bool`, optional, defaults to `True`) --
	Whether to interpolate the pre-trained position encodings.0[BaseModelOutputWithPooling](/docs/transformers/main/ja/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)`A [BaseModelOutputWithPooling](/docs/transformers/main/ja/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) 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 ([CLIPSegConfig](/docs/transformers/main/ja/model_doc/clipseg#transformers.CLIPSegConfig)) and inputs.
	The [CLIPSegVisionModel](/docs/transformers/main/ja/model_doc/clipseg#transformers.CLIPSegVisionModel) 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.

	- 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.
	- pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`) -- Last layer hidden-state of the first token of the sequence (classification token) after further processing
	through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
	the classification token after processing through a linear layer and a tanh activation function. The linear
	layer weights are trained from the next sentence prediction (classification) objective during pretraining.
	- 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.

	Examples:

	```python
	>>> import httpx
	>>> from io import BytesIO
	>>> from PIL import Image
	>>> from transformers import AutoProcessor, CLIPSegVisionModel

	>>> processor = AutoProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
	>>> model = CLIPSegVisionModel.from_pretrained("CIDAS/clipseg-rd64-refined")

	>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
	>>> with httpx.stream("GET", url) as response:
	... image = Image.open(BytesIO(response.read()))

	>>> inputs = processor(images=image, return_tensors="pt")

	>>> outputs = model(**inputs)
	>>> last_hidden_state = outputs.last_hidden_state
	>>> pooled_output = outputs.pooler_output # pooled CLS states
	```

	Parameters:

	config ([CLIPSegVisionConfig](/docs/transformers/main/ja/model_doc/clipseg#transformers.CLIPSegVisionConfig)) : 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/main/ja/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

	Returns:

	`[BaseModelOutputWithPooling](/docs/transformers/main/ja/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)``

	A [BaseModelOutputWithPooling](/docs/transformers/main/ja/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) 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 ([CLIPSegConfig](/docs/transformers/main/ja/model_doc/clipseg#transformers.CLIPSegConfig)) and inputs.

	## CLIPSegForImageSegmentation[[transformers.CLIPSegForImageSegmentation]]

	#### transformers.CLIPSegForImageSegmentation[[transformers.CLIPSegForImageSegmentation]]

	[Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/clipseg/modeling_clipseg.py#L967)

	CLIPSeg model with a Transformer-based decoder on top for zero-shot and one-shot image segmentation.

	This model inherits from [PreTrainedModel](/docs/transformers/main/ja/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.CLIPSegForImageSegmentation.forwardhttps://github.com/huggingface/transformers/blob/main/src/transformers/models/clipseg/modeling_clipseg.py#L1007[{"name": "input_ids", "val": ": torch.FloatTensor \| None = None"}, {"name": "pixel_values", "val": ": torch.FloatTensor \| None = None"}, {"name": "conditional_pixel_values", "val": ": torch.FloatTensor \| None = None"}, {"name": "conditional_embeddings", "val": ": torch.FloatTensor \| None = None"}, {"name": "attention_mask", "val": ": torch.Tensor \| None = None"}, {"name": "position_ids", "val": ": torch.LongTensor \| None = None"}, {"name": "labels", "val": ": torch.LongTensor \| None = None"}, {"name": "interpolate_pos_encoding", "val": ": bool = True"}, {"name": "kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]- input_ids** (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, optional) --
	Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

	Indices can be obtained using [AutoTokenizer](/docs/transformers/main/ja/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/main/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
	[PreTrainedTokenizer.__call__()](/docs/transformers/main/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

	[What are input IDs?](../glossary#input-ids)
	- pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`, optional) --
	The tensors corresponding to the input images. Pixel values can be obtained using
	`ViTImageProcessor`. See `ViTImageProcessor.__call__()` for details ([CLIPSegProcessor](/docs/transformers/main/ja/model_doc/clipseg#transformers.CLIPSegProcessor) uses
	`ViTImageProcessor` for processing images).
	- conditional_pixel_values (`torch.FloatTensor`, optional) --
	The pixel values of the conditional images.
	- conditional_embeddings (`torch.FloatTensor` of shape `(batch_size, config.projection_dim)`, optional) --
	The conditional embeddings for the query images. If provided, the model will use this instead of computing
	the embeddings from the conditional_pixel_values.
	- attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) --
	Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

	- 1 for tokens that are not masked,
	- 0 for tokens that are masked.

	[What are attention masks?](../glossary#attention-mask)
	- position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional) --
	Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`.

	[What are position IDs?](../glossary#position-ids)
	- labels (`torch.LongTensor` of shape `(batch_size,)`, optional) --
	Labels for computing the sequence 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).
	- interpolate_pos_encoding (`bool`, optional, defaults to `True`) --
	Whether to interpolate the pre-trained position encodings.0`CLIPSegOutput` or `tuple(torch.FloatTensor)`A `CLIPSegOutput` 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 ([CLIPSegConfig](/docs/transformers/main/ja/model_doc/clipseg#transformers.CLIPSegConfig)) and inputs.
	The [CLIPSegForImageSegmentation](/docs/transformers/main/ja/model_doc/clipseg#transformers.CLIPSegForImageSegmentation) 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 `return_loss` is `True`) -- Contrastive loss for image-text similarity.
	- logits_per_image (`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`) -- The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text
	similarity scores.
	- logits_per_text (`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`) -- The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image
	similarity scores.
	- text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim`) -- The text embeddings obtained by applying the projection layer to the pooled output of [CLIPSegTextModel](/docs/transformers/main/ja/model_doc/clipseg#transformers.CLIPSegTextModel).
	- image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim`) -- The image embeddings obtained by applying the projection layer to the pooled output of [CLIPSegVisionModel](/docs/transformers/main/ja/model_doc/clipseg#transformers.CLIPSegVisionModel).
	- text_model_output (`~modeling_outputs.BaseModelOutputWithPooling`, defaults to `None`) -- The output of the [CLIPSegTextModel](/docs/transformers/main/ja/model_doc/clipseg#transformers.CLIPSegTextModel).
	- vision_model_output (`~modeling_outputs.BaseModelOutputWithPooling`, defaults to `None`) -- The output of the [CLIPSegVisionModel](/docs/transformers/main/ja/model_doc/clipseg#transformers.CLIPSegVisionModel).

	Examples:

	```python
	>>> import torch
	>>> from transformers import AutoProcessor, CLIPSegForImageSegmentation
	>>> from transformers.image_utils import load_image

	>>> processor = AutoProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
	>>> model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined")

	>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
	>>> image = load_image(url)

	>>> texts = ["a cat", "a remote", "a blanket"]
	>>> inputs = processor(text=texts, images=[image] * len(texts), padding=True, return_tensors="pt")

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

	>>> logits = outputs.logits
	>>> print(logits.shape)
	torch.Size([3, 352, 352])
	```

	Parameters:

	config ([CLIPSegConfig](/docs/transformers/main/ja/model_doc/clipseg#transformers.CLIPSegConfig)) : 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/main/ja/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

	Returns:

	``CLIPSegOutput` or `tuple(torch.FloatTensor)``

	A `CLIPSegOutput` 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 ([CLIPSegConfig](/docs/transformers/main/ja/model_doc/clipseg#transformers.CLIPSegConfig)) and inputs.

Xet Storage Details

Size:: 50.8 kB
Xet hash:: c9bfe9a7cc7ffdf4686740b55d3513d190937a8f33a6765a0bfdf333b1055c6d

Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.