The SigLIP2 model was proposed in SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features by Michael Tschannen, Alexey Gritsenko, Xiao Wang, Muhammad Ferjad Naeem, Ibrahim Alabdulmohsin, Nikhil Parthasarathy, Talfan Evans, Lucas Beyer, Ye Xia, Basil Mustafa, Olivier Hénaff, Jeremiah Harmsen, Andreas Steiner and Xiaohua Zhai.
The model comes in two variants
1) FixRes - model works with fixed resolution images (backward compatible with SigLIP v1)
2) NaFlex - model works with variable image aspect ratios and resolutions (SigLIP2 in transformers
)
The abstract from the paper is the following:
We introduce SigLIP 2, a family of new multilingual vision-language encoders that build on the success of the original SigLIP. In this second iteration, we extend the original image-text training objective with several prior, independently developed techniques into a unified recipe—this includes decoder-based pretraining, self-supervised losses (self-distillation, masked prediction) and online data curation. With these changes, SigLIP 2 models outperform their SigLIP counterparts at all model scales in core capabilities, including zero-shot classification (best SigLIP 2 ViT-g/16 achieves 85.0% ImageNet zero-shot accuracy), image-text retrieval, and transfer performance when extracting visual representations for Vision-Language Models (VLMs). Furthermore, the new training recipe leads to significant improvements on localization and dense prediction tasks. We also train variants which support multiple resolutions and preserve the input’s native aspect ratio. Finally, we train on a more diverse data-mixture that includes de-biasing techniques, leading to much better multilingual understanding and improved fair- ness. To provide users with the ability to trade-off inference cost with performance, we release model checkpoints at four sizes (ViT-B/86M, L/303M, So400m/400M, and g/1B).
torch.distributed
utilities which may limit the scalability of batch size. However, DDP and FDSP works on single-node multi-gpu setup.padding="max_length"
and max_length=64
as that’s how the model was trained.max_num_patches
parameter in the Processor
. The default value is max_num_patches=256
. Increasing max_num_patches
to 1024 (4x) will approximately double processed image height and width, while preserving the aspect ratio.This model was contributed by qubvel. The original code can be found here.
There are 2 main ways to use SigLIP2: either using the pipeline API, which abstracts away all the complexity for you, or by using the Siglip2Model
class yourself.
Pipeline API
The pipeline allows to use the model in a few lines of code:
>>> from transformers import pipeline
>>> from PIL import Image
>>> import requests
>>> # load pipe
>>> image_classifier = pipeline(
... task="zero-shot-image-classification",
... model="google/siglip2-base-patch16-224",
... )
>>> # load image
>>> url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> # inference
>>> candidate_labels = ["2 cats", "a plane", "a remote"]
>>> outputs = image_classifier(image, candidate_labels=candidate_labels)
>>> outputs = [{"score": round(output["score"], 4), "label": output["label"] } for output in outputs]
>>> print(outputs)
[{'score': 0.1499, 'label': '2 cats'}, {'score': 0.0008, 'label': 'a remote'}, {'score': 0.0, 'label': 'a plane'}]
Using the model yourself
If you want to do the pre- and postprocessing yourself, here’s how to do that:
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, AutoModel
>>> import torch
>>> model = AutoModel.from_pretrained("google/siglip2-base-patch16-224")
>>> processor = AutoProcessor.from_pretrained("google/siglip2-base-patch16-224")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> candidate_labels = ["2 cats", "2 dogs"]
# follows the pipeline prompt template to get same results
>>> texts = [f"This is a photo of {label}." for label in candidate_labels]
# IMPORTANT: we pass `padding=max_length` and `max_length=64` since the model was trained with this
>>> inputs = processor(text=texts, images=image, padding="max_length", max_length=64, return_tensors="pt")
>>> with torch.no_grad():
... outputs = model(**inputs)
>>> logits_per_image = outputs.logits_per_image
>>> probs = torch.sigmoid(logits_per_image) # these are the probabilities
>>> print(f"{probs[0][0]:.1%} that image 0 is '{candidate_labels[0]}'")
15.0% that image 0 is '2 cats'
NaFlex combines ideas from FlexiViT, i.e. supporting multiple, predefined sequence lengths with a single ViT model, and NaViT, namely processing images at their native aspect ratio. This enables processing different types of images at appropriate resolution, e.g. using a larger resolution to process document images, while at the same time minimizing the impact of aspect ratio distortion on certain inference tasks, e.g. on OCR.
Given a patch size and target sequence length, NaFlex preprocesses the data by first resizing the input image such that the height and width after resizing are multiples of the patch size, while
max_num_patches
)The resulting distortion in width and height is at most (patch_size - 1) / width
and
(patch_size - 1) / height
, respectively, which tends to be small for common resolutions and aspect ratios.
After resizing, the image is split into a sequence of patches, and a mask with padding information is added.
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, AutoModel
>>> import torch
>>> model = AutoModel.from_pretrained("google/siglip2-base-patch16-naflex")
>>> processor = AutoProcessor.from_pretrained("google/siglip2-base-patch16-naflex")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> candidate_labels = ["2 cats", "2 dogs"]
# follows the pipeline prompt template to get same results
>>> texts = [f"This is a photo of {label}." for label in candidate_labels]
# default value for `max_num_patches` is 256, but you can increase resulted image resolution providing
# higher values e.g. `max_num_patches=512`
>>> inputs = processor(text=texts, images=image, max_num_patches=256, return_tensors="pt")
>>> with torch.no_grad():
... outputs = model(**inputs)
>>> logits_per_image = outputs.logits_per_image
>>> probs = torch.sigmoid(logits_per_image) # these are the probabilities
>>> print(f"{probs[0][0]:.1%} that image 0 is '{candidate_labels[0]}'")
21.1% that image 0 is '2 cats'
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with SigLIP2.
If you’re interested in submitting a resource to be included here, please feel free to open a Pull Request and we’ll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
First, make sure to install the latest version of Flash Attention 2.
pip install -U flash-attn --no-build-isolation
Make also sure that you have a hardware that is compatible with Flash-Attention 2. Read more about it in the official documentation of flash-attn repository. Make also sure to load your model in half-precision (e.g. `torch.float16“)
To load and run a model using Flash Attention 2, refer to the snippet below:
>>> import torch
>>> import requests
>>> from PIL import Image
>>> from transformers import AutoProcessor, AutoModel
>>> device = "cuda" # the device to load the model onto
>>> model = AutoModel.from_pretrained(
... "google/siglip2-so400m-patch14-384",
... attn_implementation="flash_attention_2",
... torch_dtype=torch.float16,
... device_map=device,
... )
>>> processor = AutoProcessor.from_pretrained("google/siglip2-so400m-patch14-384")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> candidate_labels = ["2 cats", "2 dogs"]
# follows the pipeline prompt template to get same results
>>> texts = [f'This is a photo of {label}.' for label in candidate_labels]
# important: we pass `padding=max_length` since the model was trained with this
>>> inputs = processor(text=texts, images=image, padding="max_length", return_tensors="pt").to(device)
>>> with torch.no_grad():
... with torch.autocast(device):
... outputs = model(**inputs)
>>> logits_per_image = outputs.logits_per_image
>>> probs = torch.sigmoid(logits_per_image) # these are the probabilities
>>> print(f"{probs[0][0]:.1%} that image 0 is '{candidate_labels[0]}'")
19.8% that image 0 is '2 cats'
( text_config = None vision_config = None **kwargs )
Parameters
dict
, optional) —
Dictionary of configuration options used to initialize Siglip2TextConfig. dict
, optional) —
Dictionary of configuration options used to initialize Siglip2VisionConfig. Siglip2Config is the configuration class to store the configuration of a Siglip2Model. It is used to instantiate a Siglip2 model according to the specified arguments, defining the text model and vision model configs. Instantiating a configuration with the defaults will yield a similar configuration to that of the Siglip2 google/siglip2-base-patch16-224 architecture.
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 Siglip2Config, Siglip2Model
>>> # Initializing a Siglip2Config with google/siglip2-base-patch16-224 style configuration
>>> configuration = Siglip2Config()
>>> # Initializing a Siglip2Model (with random weights) from the google/siglip2-base-patch16-224 style configuration
>>> model = Siglip2Model(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
>>> # We can also initialize a Siglip2Config from a Siglip2TextConfig and a Siglip2VisionConfig
>>> from transformers import Siglip2TextConfig, Siglip2VisionConfig
>>> # Initializing a Siglip2Text and Siglip2Vision configuration
>>> config_text = Siglip2TextConfig()
>>> config_vision = Siglip2VisionConfig()
>>> config = Siglip2Config.from_text_vision_configs(config_text, config_vision)
( text_config: Siglip2TextConfig vision_config: Siglip2VisionConfig **kwargs ) → Siglip2Config
Instantiate a Siglip2Config (or a derived class) from siglip2 text model configuration and siglip2 vision model configuration.
( vocab_size = 32000 hidden_size = 768 intermediate_size = 3072 num_hidden_layers = 12 num_attention_heads = 12 max_position_embeddings = 64 hidden_act = 'gelu_pytorch_tanh' layer_norm_eps = 1e-06 attention_dropout = 0.0 pad_token_id = 1 bos_token_id = 49406 eos_token_id = 49407 projection_size = None **kwargs )
Parameters
int
, optional, defaults to 32000) —
Vocabulary size of the Siglip2 text model. Defines the number of different tokens that can be represented by
the inputs_ids
passed when calling Siglip2Model. int
, optional, defaults to 768) —
Dimensionality of the encoder layers and the pooler layer. int
, optional, defaults to 3072) —
Dimensionality of the “intermediate” (i.e., feed-forward) layer in the Transformer encoder. int
, optional, defaults to 12) —
Number of hidden layers in the Transformer encoder. int
, optional, defaults to 12) —
Number of attention heads for each attention layer in the Transformer encoder. int
, optional, defaults to 64) —
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048). str
or function
, optional, defaults to "gelu_pytorch_tanh"
) —
The non-linear activation function (function or string) in the encoder and pooler. If string, "gelu"
,
"relu"
, "selu"
and "gelu_new"
"quick_gelu"
are supported. float
, optional, defaults to 1e-06) —
The epsilon used by the layer normalization layers. float
, optional, defaults to 0.0) —
The dropout ratio for the attention probabilities. int
, optional, defaults to 1) —
The id of the padding token in the vocabulary. int
, optional, defaults to 49406) —
The id of the beginning-of-sequence token in the vocabulary. int
, optional, defaults to 49407) —
The id of the end-of-sequence token in the vocabulary. int
, optional, defaults to hidden_size
) —
The size of the projection head. This is the configuration class to store the configuration of a Siglip2TextModel. It is used to instantiate a Siglip2 text encoder according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the text encoder of the Siglip2 google/siglip2-base-patch16-224 architecture.
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 Siglip2TextConfig, Siglip2TextModel
>>> # Initializing a Siglip2TextConfig with google/siglip2-base-patch16-224 style configuration
>>> configuration = Siglip2TextConfig()
>>> # Initializing a Siglip2TextModel (with random weights) from the google/siglip2-base-patch16-224 style configuration
>>> model = Siglip2TextModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
( hidden_size = 768 intermediate_size = 3072 num_hidden_layers = 12 num_attention_heads = 12 num_channels = 3 num_patches = 256 patch_size = 16 hidden_act = 'gelu_pytorch_tanh' layer_norm_eps = 1e-06 attention_dropout = 0.0 **kwargs )
Parameters
int
, optional, defaults to 768) —
Dimensionality of the encoder layers and the pooler layer. int
, optional, defaults to 3072) —
Dimensionality of the “intermediate” (i.e., feed-forward) layer in the Transformer encoder. int
, optional, defaults to 12) —
Number of hidden layers in the Transformer encoder. int
, optional, defaults to 12) —
Number of attention heads for each attention layer in the Transformer encoder. int
, optional, defaults to 3) —
Number of channels in the input images. int
, optional, defaults to 256) —
The number of patches in the image with the size of (patch_size
, patch_size
).
The image is resized to fill maximum of this number of patches, and to preserve
the aspect ratio. In case the resulted number of patches is lower, the image is
padded in “patch” dimension. int
, optional, defaults to 16) —
The size (resolution) of each patch. str
or function
, optional, defaults to "gelu_pytorch_tanh"
) —
The non-linear activation function (function or string) in the encoder and pooler. If string, "gelu"
,
"relu"
, "selu"
and "gelu_new"
"quick_gelu"
are supported. float
, optional, defaults to 1e-06) —
The epsilon used by the layer normalization layers. float
, optional, defaults to 0.0) —
The dropout ratio for the attention probabilities. This is the configuration class to store the configuration of a Siglip2VisionModel. It is used to instantiate a Siglip2 vision encoder according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the vision encoder of the Siglip2 google/siglip2-base-patch16-naflex architecture.
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 Siglip2VisionConfig, Siglip2VisionModel
>>> # Initializing a Siglip2VisionConfig with google/siglip2-base-patch16-naflex style configuration
>>> configuration = Siglip2VisionConfig()
>>> # Initializing a Siglip2VisionModel (with random weights) from the google/siglip2-base-patch16-naflex style configuration
>>> model = Siglip2VisionModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
( do_resize: bool = True resample: Resampling = <Resampling.BILINEAR: 2> do_rescale: bool = True rescale_factor: float = 0.00392156862745098 do_normalize: bool = True image_mean: typing.Union[float, typing.List[float], NoneType] = None image_std: typing.Union[float, typing.List[float], NoneType] = None do_convert_rgb: typing.Optional[bool] = None patch_size: int = 16 max_num_patches: int = 256 **kwargs )
Parameters
bool
, optional, defaults to True
) —
Whether to resize the image’s dimensions to fit max_num_patches
according to given patch_size
.
Can be overridden by do_resize
in the preprocess
method. PILImageResampling
, optional, defaults to Resampling.BILINEAR
) —
Resampling filter to use if resizing the image. Can be overridden by resample
in the preprocess
method. bool
, optional, defaults to True
) —
Whether to rescale the image by the specified scale rescale_factor
. Can be overridden by do_rescale
in
the preprocess
method. int
or float
, optional, defaults to 1/255
) —
Scale factor to use if rescaling the image. Can be overridden by rescale_factor
in the preprocess
method. bool
, optional, defaults to True
) —
Whether to normalize the image by the specified mean and standard deviation. Can be overridden by
do_normalize
in the preprocess
method. float
or List[float]
, optional, defaults to [0.5, 0.5, 0.5]
) —
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
channels in the image. Can be overridden by the image_mean
parameter in the preprocess
method. float
or List[float]
, optional, defaults to [0.5, 0.5, 0.5]
) —
Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
number of channels in the image. Can be overridden by the image_std
parameter in the preprocess
method.
Can be overridden by the image_std
parameter in the preprocess
method. bool
, optional, defaults to True
) —
Whether to convert the image to RGB. int
, optional, defaults to 16) —
The size (resolution) of each patch the image will be split to. int
, optional, defaults to 256) —
The image will be resized to have at most this number of patches,
and then padded in “patch” dimension to match this number exactly. Constructs a SigLIP2 image processor.
( images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), typing.List[ForwardRef('PIL.Image.Image')], typing.List[numpy.ndarray], typing.List[ForwardRef('torch.Tensor')]] do_resize: typing.Optional[bool] = None resample: typing.Optional[PIL.Image.Resampling] = None do_rescale: typing.Optional[bool] = None rescale_factor: typing.Optional[float] = None do_normalize: typing.Optional[bool] = None image_mean: typing.Union[float, typing.List[float], NoneType] = None image_std: typing.Union[float, typing.List[float], NoneType] = None return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None input_data_format: typing.Union[str, transformers.image_utils.ChannelDimension, NoneType] = None do_convert_rgb: typing.Optional[bool] = None patch_size: typing.Optional[int] = None max_num_patches: typing.Optional[int] = None )
Parameters
ImageInput
) —
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
. bool
, optional, defaults to self.do_resize
) —
Whether to resize the image. Dict[str, int]
, optional, defaults to self.size
) —
Size of the image after resizing. int
, optional, defaults to self.resample
) —
Resampling filter to use if resizing the image. This can be one of the enum PILImageResampling
. Only
has an effect if do_resize
is set to True
. bool
, optional, defaults to self.do_rescale
) —
Whether to rescale the image. float
, optional, defaults to self.rescale_factor
) —
Rescale factor to rescale the image by if do_rescale
is set to True
. bool
, optional, defaults to self.do_normalize
) —
Whether to normalize the image. float
or List[float]
, optional, defaults to self.image_mean
) —
Image mean to use for normalization. Only has an effect if do_normalize
is set to True
. float
or List[float]
, optional, defaults to self.image_std
) —
Image standard deviation to use for normalization. Only has an effect if do_normalize
is set to
True
. str
or TensorType
, optional) —
The type of tensors to return. Can be one of:np.ndarray
.TensorType.TENSORFLOW
or 'tf'
: Return a batch of type tf.Tensor
.TensorType.PYTORCH
or 'pt'
: Return a batch of type torch.Tensor
.TensorType.NUMPY
or 'np'
: Return a batch of type np.ndarray
.TensorType.JAX
or 'jax'
: Return a batch of type jax.numpy.ndarray
.ChannelDimension
or str
, optional) —
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.bool
, optional, defaults to self.do_convert_rgb
) —
Whether to convert the image to RGB. int
, optional, defaults to self.patch_size
) —
Patch size for processing, same as the patch size used in the model. int
, optional, defaults to self.max_num_patches
) —
Maximum number of patches per image, the image will be resized to have at most this number of patches. Preprocess an image or batch of images.
( **kwargs: typing_extensions.Unpack[transformers.models.siglip2.image_processing_siglip2_fast.Siglip2FastImageProcessorKwargs] )
Parameters
bool
, optional, defaults to self.do_resize
) —
Whether to resize the image’s (height, width) dimensions to the specified size
. Can be overridden by the
do_resize
parameter in the preprocess
method. dict
, optional, defaults to self.size
) —
Size of the output image after resizing. Can be overridden by the size
parameter in the preprocess
method. bool
, optional, defaults to self.default_to_square
) —
Whether to default to a square image when resizing, if size is an int. PILImageResampling
, optional, defaults to self.resample
) —
Resampling filter to use if resizing the image. Only has an effect if do_resize
is set to True
. Can be
overridden by the resample
parameter in the preprocess
method. bool
, optional, defaults to self.do_center_crop
) —
Whether to center crop the image to the specified crop_size
. Can be overridden by do_center_crop
in the
preprocess
method. Dict[str, int]
optional, defaults to self.crop_size
) —
Size of the output image after applying center_crop
. Can be overridden by crop_size
in the preprocess
method. bool
, optional, defaults to self.do_rescale
) —
Whether to rescale the image by the specified scale rescale_factor
. Can be overridden by the
do_rescale
parameter in the preprocess
method. int
or float
, optional, defaults to self.rescale_factor
) —
Scale factor to use if rescaling the image. Only has an effect if do_rescale
is set to True
. Can be
overridden by the rescale_factor
parameter in the preprocess
method. bool
, optional, defaults to self.do_normalize
) —
Whether to normalize the image. Can be overridden by the do_normalize
parameter in the preprocess
method. Can be overridden by the do_normalize
parameter in the preprocess
method. float
or List[float]
, optional, defaults to self.image_mean
) —
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
channels in the image. Can be overridden by the image_mean
parameter in the preprocess
method. Can be
overridden by the image_mean
parameter in the preprocess
method. float
or List[float]
, optional, defaults to self.image_std
) —
Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
number of channels in the image. Can be overridden by the image_std
parameter in the preprocess
method.
Can be overridden by the image_std
parameter in the preprocess
method. bool
, optional, defaults to self.do_convert_rgb
) —
Whether to convert the image to RGB. str
or TensorType
, optional, defaults to self.return_tensors
) —
Returns stacked tensors if set to `pt, otherwise returns a list of tensors. ChannelDimension
or str
, optional, defaults to self.data_format
) —
Only ChannelDimension.FIRST
is supported. Added for compatibility with slow processors. ChannelDimension
or str
, optional, defaults to self.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.torch.device
, optional, defaults to self.device
) —
The device to process the images on. If unset, the device is inferred from the input images. int
, optional, defaults to 16) —
The size (resolution) of each patch the image will be split to. int
, optional, defaults to 256) —
The image will be resized to have at most this number of patches,
and then padded in “patch” dimension to match this number exactly. Constructs a fast Siglip2 image processor.
( images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), typing.List[ForwardRef('PIL.Image.Image')], typing.List[numpy.ndarray], typing.List[ForwardRef('torch.Tensor')]] **kwargs: typing_extensions.Unpack[transformers.models.siglip2.image_processing_siglip2_fast.Siglip2FastImageProcessorKwargs] )
Parameters
ImageInput
) —
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
. bool
, optional, defaults to self.do_resize
) —
Whether to resize the image. Dict[str, int]
, optional, defaults to self.size
) —
Describes the maximum input dimensions to the model. PILImageResampling
or InterpolationMode
, optional, defaults to self.resample
) —
Resampling filter to use if resizing the image. This can be one of the enum PILImageResampling
. Only
has an effect if do_resize
is set to True
. bool
, optional, defaults to self.do_center_crop
) —
Whether to center crop the image. Dict[str, int]
, optional, defaults to self.crop_size
) —
Size of the output image after applying center_crop
. bool
, optional, defaults to self.do_rescale
) —
Whether to rescale the image. float
, optional, defaults to self.rescale_factor
) —
Rescale factor to rescale the image by if do_rescale
is set to True
. bool
, optional, defaults to self.do_normalize
) —
Whether to normalize the image. float
or List[float]
, optional, defaults to self.image_mean
) —
Image mean to use for normalization. Only has an effect if do_normalize
is set to True
. float
or List[float]
, optional, defaults to self.image_std
) —
Image standard deviation to use for normalization. Only has an effect if do_normalize
is set to
True
. bool
, optional, defaults to self.do_convert_rgb
) —
Whether to convert the image to RGB. str
or TensorType
, optional, defaults to self.return_tensors
) —
Returns stacked tensors if set to `pt, otherwise returns a list of tensors. ChannelDimension
or str
, optional, defaults to self.data_format
) —
Only ChannelDimension.FIRST
is supported. Added for compatibility with slow processors. ChannelDimension
or str
, optional, defaults to self.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.torch.device
, optional, defaults to self.device
) —
The device to process the images on. If unset, the device is inferred from the input images. int
, optional, defaults to self.patch_size
) —
The size (resolution) of each patch the image will be split to. int
, optional, defaults to self.max_num_patches
) —
The image will be resized to have at most this number of patches,
and then padded in “patch” dimension to match this number exactly. Preprocess an image or batch of images.
( image_processor tokenizer )
Parameters
Constructs a Siglip2 processor which wraps a Siglip2 image processor and a Gemma tokenizer into a single processor.
Siglip2Processor offers all the functionalities of Siglip2ImageProcessor and GemmaTokenizerFast. See the
__call__()
and decode() for more information.
This method forwards all its arguments to Siglip2Tokenizer’s batch_decode(). Please refer to the docstring of this method for more information.
This method forwards all its arguments to Siglip2Tokenizer’s decode(). Please refer to the docstring of this method for more information.
( config: Siglip2Config )
Parameters
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.
( input_ids: typing.Optional[torch.LongTensor] = None pixel_values: typing.Optional[torch.FloatTensor] = None pixel_attention_mask: typing.Optional[torch.Tensor] = None spatial_shapes: typing.Optional[torch.LongTensor] = None attention_mask: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.LongTensor] = None return_loss: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) → transformers.models.siglip2.modeling_siglip2.Siglip2Output
or tuple(torch.FloatTensor)
Parameters
torch.LongTensor
of shape (batch_size, sequence_length)
) —
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
torch.Tensor
of shape (batch_size, sequence_length)
, optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]
:
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.max_position_embeddings - 1]
.
torch.FloatTensor
of shape (batch_size, num_channels, height, width)
) —
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
AutoImageProcessor. See CLIPImageProcessor.call() for details. bool
, optional) —
Whether or not to return the contrastive loss. bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail. bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail. bool
, optional, defaults to False
) —
Whether to interpolate the pre-trained position encodings. bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. Returns
transformers.models.siglip2.modeling_siglip2.Siglip2Output
or tuple(torch.FloatTensor)
A transformers.models.siglip2.modeling_siglip2.Siglip2Output
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 (<class 'transformers.models.siglip2.configuration_siglip2.Siglip2Config'>
) and inputs.
torch.FloatTensor
of shape (1,)
, optional, returned when return_loss
is True
) — Contrastive loss for image-text similarity.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.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.torch.FloatTensor
of shape (batch_size, output_dim
) — The text embeddings obtained by applying the projection layer to the pooled output of Siglip2TextModel.torch.FloatTensor
of shape (batch_size, output_dim
) — The image embeddings obtained by applying the projection layer to the pooled output of Siglip2VisionModel.BaseModelOutputWithPooling
) — The output of the Siglip2TextModel.BaseModelOutputWithPooling
) — The output of the Siglip2VisionModel.The Siglip2Model forward method, overrides the __call__
special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Examples:
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, AutoModel
>>> import torch
>>> model = AutoModel.from_pretrained("google/siglip2-base-patch16-224")
>>> processor = AutoProcessor.from_pretrained("google/siglip2-base-patch16-224")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> texts = ["a photo of 2 cats", "a photo of 2 dogs"]
>>> # important: we pass `padding=max_length` since the model was trained with this
>>> inputs = processor(text=texts, images=image, padding="max_length", return_tensors="pt")
>>> with torch.no_grad():
... outputs = model(**inputs)
>>> logits_per_image = outputs.logits_per_image
>>> probs = torch.sigmoid(logits_per_image) # these are the probabilities
>>> print(f"{probs[0][0]:.1%} that image 0 is '{texts[0]}'")
31.9% that image 0 is 'a photo of 2 cats'
( input_ids: typing.Optional[torch.Tensor] = None attention_mask: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.Tensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) → text_features (torch.FloatTensor
of shape (batch_size, output_dim
)
Parameters
torch.LongTensor
of shape (batch_size, sequence_length)
) —
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
torch.Tensor
of shape (batch_size, sequence_length)
, optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]
:
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.max_position_embeddings - 1]
.
bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail. bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail. bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. Returns
text_features (torch.FloatTensor
of shape (batch_size, output_dim
)
The text embeddings obtained by applying the projection layer to the pooled output of Siglip2TextModel.
The Siglip2Model forward method, overrides the __call__
special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Examples:
>>> from transformers import AutoTokenizer, AutoModel
>>> import torch
>>> model = AutoModel.from_pretrained("google/siglip2-base-patch16-224")
>>> tokenizer = AutoTokenizer.from_pretrained("google/siglip2-base-patch16-224")
>>> # important: make sure to set padding="max_length" as that's how the model was trained
>>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding="max_length", return_tensors="pt")
>>> with torch.no_grad():
... text_features = model.get_text_features(**inputs)
( pixel_values: typing.Optional[torch.FloatTensor] = None pixel_attention_mask: typing.Optional[torch.Tensor] = None spatial_shapes: typing.Optional[torch.LongTensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) → image_features (torch.FloatTensor
of shape (batch_size, output_dim
)
Parameters
torch.FloatTensor
of shape (batch_size, num_channels, height, width)
) —
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
AutoImageProcessor. See CLIPImageProcessor.call() for details. bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail. bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail. bool
, optional, defaults to False
) —
Whether to interpolate the pre-trained position encodings. bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. Returns
image_features (torch.FloatTensor
of shape (batch_size, output_dim
)
The image embeddings obtained by applying the projection layer to the pooled output of Siglip2VisionModel.
The Siglip2Model forward method, overrides the __call__
special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Examples:
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, AutoModel
>>> import torch
>>> model = AutoModel.from_pretrained("google/siglip2-base-patch16-224")
>>> processor = AutoProcessor.from_pretrained("google/siglip2-base-patch16-224")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = processor(images=image, return_tensors="pt")
>>> with torch.no_grad():
... image_features = model.get_image_features(**inputs)
( config: Siglip2TextConfig )
Parameters
The text model from Siglip2 without any head or projection on top. 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.
( input_ids: typing.Optional[torch.Tensor] = None attention_mask: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.Tensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) → transformers.modeling_outputs.BaseModelOutputWithPooling or tuple(torch.FloatTensor)
Parameters
torch.LongTensor
of shape (batch_size, sequence_length)
) —
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
torch.Tensor
of shape (batch_size, sequence_length)
, optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]
:
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.max_position_embeddings - 1]
.
bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail. bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail. bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. Returns
transformers.modeling_outputs.BaseModelOutputWithPooling or tuple(torch.FloatTensor)
A 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 (<class 'transformers.models.siglip2.configuration_siglip2.Siglip2TextConfig'>
) and inputs.
last_hidden_state (torch.FloatTensor
of shape (batch_size, sequence_length, hidden_size)
) — Sequence of hidden-states at the output of the last layer of the model.
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.
The Siglip2TextModel forward method, overrides the __call__
special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Examples:
>>> from transformers import AutoTokenizer, Siglip2TextModel
>>> model = Siglip2TextModel.from_pretrained("google/siglip2-base-patch16-224")
>>> tokenizer = AutoTokenizer.from_pretrained("google/siglip2-base-patch16-224")
>>> # important: make sure to set padding="max_length" as that's how the model was trained
>>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding="max_length", return_tensors="pt")
>>> outputs = model(**inputs)
>>> last_hidden_state = outputs.last_hidden_state
>>> pooled_output = outputs.pooler_output # pooled (EOS token) states
( config: Siglip2VisionConfig )
Parameters
The vision model from Siglip2 without any head or projection on top. 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.
( pixel_values: FloatTensor pixel_attention_mask: Tensor spatial_shapes: LongTensor output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) → transformers.modeling_outputs.BaseModelOutputWithPooling or tuple(torch.FloatTensor)
Parameters
torch.FloatTensor
of shape (batch_size, num_channels, height, width)
) —
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
AutoImageProcessor. See CLIPImageProcessor.call() for details. bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail. bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail. bool
, optional, defaults to False
) —
Whether to interpolate the pre-trained position encodings. bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. Returns
transformers.modeling_outputs.BaseModelOutputWithPooling or tuple(torch.FloatTensor)
A 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 (<class 'transformers.models.siglip2.configuration_siglip2.Siglip2VisionConfig'>
) and inputs.
last_hidden_state (torch.FloatTensor
of shape (batch_size, sequence_length, hidden_size)
) — Sequence of hidden-states at the output of the last layer of the model.
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.
The Siglip2VisionModel forward method, overrides the __call__
special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Examples:
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, Siglip2VisionModel
>>> model = Siglip2VisionModel.from_pretrained("google/siglip2-base-patch16-224")
>>> processor = AutoProcessor.from_pretrained("google/siglip2-base-patch16-224")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = processor(images=image, return_tensors="pt")
>>> outputs = model(**inputs)
>>> last_hidden_state = outputs.last_hidden_state
>>> pooled_output = outputs.pooler_output # pooled features
( config: Siglip2Config )
Parameters
Siglip2 vision encoder with an image classification head on top (a linear layer on top of the pooled final hidden states of the patch tokens) e.g. for ImageNet.
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.
( pixel_values: typing.Optional[torch.Tensor] = None pixel_attention_mask: typing.Optional[torch.Tensor] = None spatial_shapes: typing.Optional[torch.LongTensor] = None labels: typing.Optional[torch.Tensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) → transformers.modeling_outputs.ImageClassifierOutput or tuple(torch.FloatTensor)
Parameters
torch.LongTensor
of shape (batch_size, sequence_length)
) —
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
torch.Tensor
of shape (batch_size, sequence_length)
, optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]
:
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.max_position_embeddings - 1]
.
torch.FloatTensor
of shape (batch_size, num_channels, height, width)
) —
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
AutoImageProcessor. See CLIPImageProcessor.call() for details. bool
, optional) —
Whether or not to return the contrastive loss. bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail. bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail. bool
, optional, defaults to False
) —
Whether to interpolate the pre-trained position encodings. bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. torch.LongTensor
of shape (batch_size,)
, optional) —
Labels for computing the image classification/regression loss. Indices should be in [0, ..., config.num_labels - 1]
. If config.num_labels == 1
a regression loss is computed (Mean-Square loss), If
config.num_labels > 1
a classification loss is computed (Cross-Entropy). Returns
transformers.modeling_outputs.ImageClassifierOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.ImageClassifierOutput 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 (Siglip2Config) and inputs.
loss (torch.FloatTensor
of shape (1,)
, optional, returned when labels
is provided) — Classification (or regression if config.num_labels==1) loss.
logits (torch.FloatTensor
of shape (batch_size, config.num_labels)
) — Classification (or regression if config.num_labels==1) scores (before SoftMax).
hidden_states (tuple(torch.FloatTensor)
, optional, returned when output_hidden_states=True
is passed or when config.output_hidden_states=True
) — Tuple of torch.FloatTensor
(one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each stage) of shape (batch_size, sequence_length, hidden_size)
. Hidden-states
(also called feature maps) of the model at the output of each stage.
attentions (tuple(torch.FloatTensor)
, optional, returned when output_attentions=True
is passed or when config.output_attentions=True
) — Tuple of torch.FloatTensor
(one for each layer) of shape (batch_size, num_heads, patch_size, sequence_length)
.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
The Siglip2ForImageClassification forward method, overrides the __call__
special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Examples:
>>> from transformers import AutoImageProcessor, Siglip2ForImageClassification
>>> import torch
>>> from PIL import Image
>>> import requests
>>> torch.manual_seed(3)
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> # note: we are loading a `Siglip2Model` from the hub here,
>>> # so the head will be randomly initialized, hence the predictions will be random if seed is not set above.
>>> image_processor = AutoImageProcessor.from_pretrained("google/siglip2-base-patch16-224")
>>> model = Siglip2ForImageClassification.from_pretrained("google/siglip2-base-patch16-224")
>>> inputs = image_processor(images=image, return_tensors="pt")
>>> outputs = model(**inputs)
>>> logits = outputs.logits
>>> # model predicts one of the two classes
>>> predicted_class_idx = logits.argmax(-1).item()
>>> print("Predicted class:", model.config.id2label[predicted_class_idx])
Predicted class: LABEL_1