The OWL-ViT (short for Vision Transformer for Open-World Localization) was proposed in Simple Open-Vocabulary Object Detection with Vision Transformers by Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Xiaohua Zhai, Thomas Kipf, and Neil Houlsby. OWL-ViT is an open-vocabulary object detection network trained on a variety of (image, text) pairs. It can be used to query an image with one or multiple text queries to search for and detect target objects described in text.
The abstract from the paper is the following:
Combining simple architectures with large-scale pre-training has led to massive improvements in image classification. For object detection, pre-training and scaling approaches are less well established, especially in the long-tailed and open-vocabulary setting, where training data is relatively scarce. In this paper, we propose a strong recipe for transferring image-text models to open-vocabulary object detection. We use a standard Vision Transformer architecture with minimal modifications, contrastive image-text pre-training, and end-to-end detection fine-tuning. Our analysis of the scaling properties of this setup shows that increasing image-level pre-training and model size yield consistent improvements on the downstream detection task. We provide the adaptation strategies and regularizations needed to attain very strong performance on zero-shot text-conditioned and one-shot image-conditioned object detection. Code and models are available on GitHub.
OWL-ViT architecture. Taken from the original paper. This model was contributed by adirik. The original code can be found here.
OWL-ViT is a zero-shot text-conditioned object detection model. OWL-ViT uses CLIP as its multi-modal backbone, with a ViT-like Transformer to get visual features and a causal language model to get the text features. To use CLIP for detection, OWL-ViT removes the final token pooling layer of the vision model and attaches a lightweight classification and box head to each transformer output token. Open-vocabulary classification is enabled by replacing the fixed classification layer weights with the class-name embeddings obtained from the text model. The authors first train CLIP from scratch and fine-tune it end-to-end with the classification and box heads on standard detection datasets using a bipartite matching loss. One or multiple text queries per image can be used to perform zero-shot text-conditioned object detection.
OwlViTImageProcessor can be used to resize (or rescale) and normalize images for the model and CLIPTokenizer is used to encode the text. OwlViTProcessor wraps OwlViTImageProcessor and CLIPTokenizer into a single instance to both encode the text and prepare the images. The following example shows how to perform object detection using OwlViTProcessor and OwlViTForObjectDetection.
>>> import requests
>>> from PIL import Image
>>> import torch
>>> from transformers import OwlViTProcessor, OwlViTForObjectDetection
>>> processor = OwlViTProcessor.from_pretrained("google/owlvit-base-patch32")
>>> model = OwlViTForObjectDetection.from_pretrained("google/owlvit-base-patch32")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> texts = [["a photo of a cat", "a photo of a dog"]]
>>> inputs = processor(text=texts, images=image, return_tensors="pt")
>>> outputs = model(**inputs)
>>> # Target image sizes (height, width) to rescale box predictions [batch_size, 2]
>>> target_sizes = torch.Tensor([image.size[::-1]])
>>> # Convert outputs (bounding boxes and class logits) to Pascal VOC format (xmin, ymin, xmax, ymax)
>>> results = processor.post_process_object_detection(outputs=outputs, target_sizes=target_sizes, threshold=0.1)
>>> i = 0 # Retrieve predictions for the first image for the corresponding text queries
>>> text = texts[i]
>>> boxes, scores, labels = results[i]["boxes"], results[i]["scores"], results[i]["labels"]
>>> for box, score, label in zip(boxes, scores, labels):
... box = [round(i, 2) for i in box.tolist()]
... print(f"Detected {text[label]} with confidence {round(score.item(), 3)} at location {box}")
Detected a photo of a cat with confidence 0.707 at location [324.97, 20.44, 640.58, 373.29]
Detected a photo of a cat with confidence 0.717 at location [1.46, 55.26, 315.55, 472.17]A demo notebook on using OWL-ViT for zero- and one-shot (image-guided) object detection can be found here.
( text_config = None vision_config = None projection_dim = 512 logit_scale_init_value = 2.6592 return_dict = True **kwargs )
Parameters
dict, optional) —
Dictionary of configuration options used to initialize OwlViTTextConfig. dict, optional) —
Dictionary of configuration options used to initialize OwlViTVisionConfig. int, optional, defaults to 512) —
Dimensionality of text and vision projection layers. float, optional, defaults to 2.6592) —
The inital value of the logit_scale parameter. Default is used as per the original OWL-ViT
implementation. bool, optional, defaults to True) —
Whether or not the model should return a dictionary. If False, returns a tuple. OwlViTConfig is the configuration class to store the configuration of an OwlViTModel. It is used to instantiate an OWL-ViT 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 OWL-ViT google/owlvit-base-patch32 architecture.
Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.
( text_config: Dict vision_config: Dict **kwargs ) → OwlViTConfig
Instantiate a OwlViTConfig (or a derived class) from owlvit text model configuration and owlvit vision model configuration.
( vocab_size = 49408 hidden_size = 512 intermediate_size = 2048 num_hidden_layers = 12 num_attention_heads = 8 max_position_embeddings = 16 hidden_act = 'quick_gelu' layer_norm_eps = 1e-05 attention_dropout = 0.0 initializer_range = 0.02 initializer_factor = 1.0 pad_token_id = 0 bos_token_id = 49406 eos_token_id = 49407 **kwargs )
Parameters
int, optional, defaults to 49408) —
Vocabulary size of the OWL-ViT text model. Defines the number of different tokens that can be represented
by the inputs_ids passed when calling OwlViTTextModel. int, optional, defaults to 512) —
Dimensionality of the encoder layers and the pooler layer. int, optional, defaults to 2048) —
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 8) —
Number of attention heads for each attention layer in the Transformer encoder. int, optional, defaults to 16) —
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 "quick_gelu") —
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-05) —
The epsilon used by the layer normalization layers. float, optional, defaults to 0.0) —
The dropout ratio for the attention probabilities. float, optional, defaults to 0.02) —
The standard deviation of the truncated_normal_initializer for initializing all weight matrices. float, optional, defaults to 1.0) —
A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
testing). int, optional, defaults to 0) —
The id of the padding token in the input sequences. int, optional, defaults to 49406) —
The id of the beginning-of-sequence token in the input sequences. int, optional, defaults to 49407) —
The id of the end-of-sequence token in the input sequences. This is the configuration class to store the configuration of an OwlViTTextModel. It is used to instantiate an OwlViT 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 OwlViT google/owlvit-base-patch32 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 OwlViTTextConfig, OwlViTTextModel
>>> # Initializing a OwlViTTextModel with google/owlvit-base-patch32 style configuration
>>> configuration = OwlViTTextConfig()
>>> # Initializing a OwlViTTextConfig from the google/owlvit-base-patch32 style configuration
>>> model = OwlViTTextModel(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 image_size = 768 patch_size = 32 hidden_act = 'quick_gelu' layer_norm_eps = 1e-05 attention_dropout = 0.0 initializer_range = 0.02 initializer_factor = 1.0 **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 768) —
The size (resolution) of each image. int, optional, defaults to 32) —
The size (resolution) of each patch. str or function, optional, defaults to "quick_gelu") —
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-05) —
The epsilon used by the layer normalization layers. float, optional, defaults to 0.0) —
The dropout ratio for the attention probabilities. float, optional, defaults to 0.02) —
The standard deviation of the truncated_normal_initializer for initializing all weight matrices. float, optional, defaults to 1.0) —
A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
testing). This is the configuration class to store the configuration of an OwlViTVisionModel. It is used to instantiate an OWL-ViT image 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 OWL-ViT google/owlvit-base-patch32 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 OwlViTVisionConfig, OwlViTVisionModel
>>> # Initializing a OwlViTVisionModel with google/owlvit-base-patch32 style configuration
>>> configuration = OwlViTVisionConfig()
>>> # Initializing a OwlViTVisionModel model from the google/owlvit-base-patch32 style configuration
>>> model = OwlViTVisionModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config( do_resize = True size = None resample = <Resampling.BICUBIC: 3> do_center_crop = False crop_size = None do_rescale = True rescale_factor = 0.00392156862745098 do_normalize = True image_mean = None image_std = None **kwargs )
Parameters
bool, optional, defaults to True) —
Whether to resize the shorter edge of the input to a certain size. Dict[str, int], optional, defaults to {“height” — 768, “width”: 768}):
The size to use for resizing the image. Only has an effect if do_resize is set to True. If size is a
sequence like (h, w), output size will be matched to this. If size is an int, then image will be resized
to (size, size). int, optional, defaults to Resampling.BICUBIC) —
An optional resampling filter. This can be one of PIL.Image.Resampling.NEAREST,
PIL.Image.Resampling.BOX, PIL.Image.Resampling.BILINEAR, PIL.Image.Resampling.HAMMING,
PIL.Image.Resampling.BICUBIC or PIL.Image.Resampling.LANCZOS. Only has an effect if do_resize is set
to True. bool, optional, defaults to False) —
Whether to crop the input at the center. If the input size is smaller than crop_size along any edge, the
image is padded with 0’s and then center cropped. int, optional, defaults to {“height” — 768, “width”: 768}):
The size to use for center cropping the image. Only has an effect if do_center_crop is set to True. bool, optional, defaults to True) —
Whether to rescale the input by a certain factor. float, optional, defaults to 1/255) —
The factor to use for rescaling the image. Only has an effect if do_rescale is set to True. bool, optional, defaults to True) —
Whether or not to normalize the input with image_mean and image_std. Desired output size when applying
center-cropping. Only has an effect if do_center_crop is set to True. List[int], optional, defaults to [0.48145466, 0.4578275, 0.40821073]) —
The sequence of means for each channel, to be used when normalizing images. List[int], optional, defaults to [0.26862954, 0.26130258, 0.27577711]) —
The sequence of standard deviations for each channel, to be used when normalizing images. Constructs an OWL-ViT image processor.
This image processor inherits from ImageProcessingMixin which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.
( images: Union do_resize: Optional = None size: Optional = None resample: Resampling = None do_center_crop: Optional = None crop_size: Optional = None do_rescale: Optional = None rescale_factor: Optional = None do_normalize: Optional = None image_mean: Union = None image_std: Union = None return_tensors: Union = None data_format: Union = <ChannelDimension.FIRST: 'channels_first'> input_data_format: Union = None **kwargs )
Parameters
ImageInput) —
The image or batch of images to be prepared. 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 or not to resize the input. If True, will resize the input to the size specified by size. Dict[str, int], optional, defaults to self.size) —
The size to resize the input to. Only has an effect if do_resize is set to True. PILImageResampling, optional, defaults to self.resample) —
The resampling filter to use when resizing the input. Only has an effect if do_resize is set to
True. bool, optional, defaults to self.do_center_crop) —
Whether or not to center crop the input. If True, will center crop the input to the size specified by
crop_size. Dict[str, int], optional, defaults to self.crop_size) —
The size to center crop the input to. Only has an effect if do_center_crop is set to True. bool, optional, defaults to self.do_rescale) —
Whether or not to rescale the input. If True, will rescale the input by dividing it by
rescale_factor. float, optional, defaults to self.rescale_factor) —
The factor to rescale the input by. Only has an effect if do_rescale is set to True. bool, optional, defaults to self.do_normalize) —
Whether or not to normalize the input. If True, will normalize the input by subtracting image_mean
and dividing by image_std. Union[float, List[float]], optional, defaults to self.image_mean) —
The mean to subtract from the input when normalizing. Only has an effect if do_normalize is set to
True. Union[float, List[float]], optional, defaults to self.image_std) —
The standard deviation to divide the input by when normalizing. 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, defaults to ChannelDimension.FIRST) —
The channel dimension format for the output image. Can be one of:ChannelDimension.FIRST: image in (num_channels, height, width) format.ChannelDimension.LAST: image in (height, width, num_channels) format.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.Prepares an image or batch of images for the model.
( outputs threshold: float = 0.1 target_sizes: Union = None ) → List[Dict]
Parameters
OwlViTObjectDetectionOutput) —
Raw outputs of the model. float, optional) —
Score threshold to keep object detection predictions. torch.Tensor or List[Tuple[int, int]], optional) —
Tensor of shape (batch_size, 2) or list of tuples (Tuple[int, int]) containing the target size
(height, width) of each image in the batch. If unset, predictions will not be resized. Returns
List[Dict]
A list of dictionaries, each dictionary containing the scores, labels and boxes for an image in the batch as predicted by the model.
Converts the raw output of OwlViTForObjectDetection into final bounding boxes in (top_left_x, top_left_y, bottom_right_x, bottom_right_y) format.
( outputs threshold = 0.0 nms_threshold = 0.3 target_sizes = None ) → List[Dict]
Parameters
OwlViTImageGuidedObjectDetectionOutput) —
Raw outputs of the model. float, optional, defaults to 0.0) —
Minimum confidence threshold to use to filter out predicted boxes. float, optional, defaults to 0.3) —
IoU threshold for non-maximum suppression of overlapping boxes. torch.Tensor, optional) —
Tensor of shape (batch_size, 2) where each entry is the (height, width) of the corresponding image in
the batch. If set, predicted normalized bounding boxes are rescaled to the target sizes. If left to
None, predictions will not be unnormalized. Returns
List[Dict]
A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
in the batch as predicted by the model. All labels are set to None as
OwlViTForObjectDetection.image_guided_detection perform one-shot object detection.
Converts the output of OwlViTForObjectDetection.image_guided_detection() into the format expected by the COCO api.
Preprocess an image or a batch of images.
( outputs target_sizes ) → List[Dict]
Parameters
OwlViTObjectDetectionOutput) —
Raw outputs of the model. torch.Tensor of shape (batch_size, 2)) —
Tensor containing the size (h, w) of each image of the batch. For evaluation, this must be the original
image size (before any data augmentation). For visualization, this should be the image size after data
augment, but before padding. Returns
List[Dict]
A list of dictionaries, each dictionary containing the scores, labels and boxes for an image in the batch as predicted by the model.
Converts the raw output of OwlViTForObjectDetection into final bounding boxes in (top_left_x, top_left_y, bottom_right_x, bottom_right_y) format.
( outputs threshold = 0.0 nms_threshold = 0.3 target_sizes = None ) → List[Dict]
Parameters
OwlViTImageGuidedObjectDetectionOutput) —
Raw outputs of the model. float, optional, defaults to 0.0) —
Minimum confidence threshold to use to filter out predicted boxes. float, optional, defaults to 0.3) —
IoU threshold for non-maximum suppression of overlapping boxes. torch.Tensor, optional) —
Tensor of shape (batch_size, 2) where each entry is the (height, width) of the corresponding image in
the batch. If set, predicted normalized bounding boxes are rescaled to the target sizes. If left to
None, predictions will not be unnormalized. Returns
List[Dict]
A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
in the batch as predicted by the model. All labels are set to None as
OwlViTForObjectDetection.image_guided_detection perform one-shot object detection.
Converts the output of OwlViTForObjectDetection.image_guided_detection() into the format expected by the COCO api.
( image_processor = None tokenizer = None **kwargs )
Parameters
CLIPTokenizer, CLIPTokenizerFast], optional) —
The tokenizer is a required input. Constructs an OWL-ViT processor which wraps OwlViTImageProcessor and CLIPTokenizer/CLIPTokenizerFast
into a single processor that interits both the image processor and tokenizer functionalities. See the
__call__() and decode() for more information.
This method forwards all its arguments to CLIPTokenizerFast’s batch_decode(). Please refer to the docstring of this method for more information.
This method forwards all its arguments to CLIPTokenizerFast’s decode(). Please refer to the docstring of this method for more information.
This method forwards all its arguments to OwlViTImageProcessor.post_process(). Please refer to the docstring of this method for more information.
This method forwards all its arguments to OwlViTImageProcessor.post_process_one_shot_object_detection.
Please refer to the docstring of this method for more information.
This method forwards all its arguments to OwlViTImageProcessor.post_process_object_detection(). Please refer to the docstring of this method for more information.
( config: OwlViTConfig )
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: Optional = None pixel_values: Optional = None attention_mask: Optional = None return_loss: Optional = None output_attentions: Optional = None output_hidden_states: Optional = None return_base_image_embeds: Optional = None return_dict: Optional = None ) → transformers.models.owlvit.modeling_owlvit.OwlViTOutput or tuple(torch.FloatTensor)
Parameters
torch.LongTensor of shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary. Indices can be obtained using AutoTokenizer. See
PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details. What are input
IDs? 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.FloatTensor of shape (batch_size, num_channels, height, width)) —
Pixel values. 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) —
Whether or not to return a ModelOutput instead of a plain tuple. Returns
transformers.models.owlvit.modeling_owlvit.OwlViTOutput or tuple(torch.FloatTensor)
A transformers.models.owlvit.modeling_owlvit.OwlViTOutput 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.owlvit.configuration_owlvit.OwlViTConfig'>) 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 * num_max_text_queries, output_dim) — The text embeddings obtained by applying the projection layer to the pooled output of OwlViTTextModel.torch.FloatTensor of shape (batch_size, output_dim) — The image embeddings obtained by applying the projection layer to the pooled output of
OwlViTVisionModel.BaseModelOutputWithPooling) — The output of the OwlViTTextModel.BaseModelOutputWithPooling) — The output of the OwlViTVisionModel.The OwlViTModel 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, OwlViTModel
>>> model = OwlViTModel.from_pretrained("google/owlvit-base-patch32")
>>> processor = AutoProcessor.from_pretrained("google/owlvit-base-patch32")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = processor(text=[["a photo of a cat", "a photo of a dog"]], images=image, return_tensors="pt")
>>> 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( input_ids: Optional = None attention_mask: Optional = None output_attentions: Optional = None output_hidden_states: Optional = None return_dict: Optional = None ) → text_features (torch.FloatTensor of shape (batch_size, output_dim)
Parameters
torch.LongTensor of shape (batch_size * num_max_text_queries, sequence_length)) —
Indices of input sequence tokens in the vocabulary. Indices can be obtained using AutoTokenizer. See
PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details. What are input
IDs? torch.Tensor of shape (batch_size, num_max_text_queries, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 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 OwlViTTextModel.
The OwlViTModel 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 AutoProcessor, OwlViTModel
>>> model = OwlViTModel.from_pretrained("google/owlvit-base-patch32")
>>> processor = AutoProcessor.from_pretrained("google/owlvit-base-patch32")
>>> inputs = processor(
... text=[["a photo of a cat", "a photo of a dog"], ["photo of a astranaut"]], return_tensors="pt"
... )
>>> text_features = model.get_text_features(**inputs)( pixel_values: Optional = None output_attentions: Optional = None output_hidden_states: Optional = None return_dict: Optional = None ) → image_features (torch.FloatTensor of shape (batch_size, output_dim)
Parameters
torch.FloatTensor of shape (batch_size, num_channels, height, width)) —
Pixel values. 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
image_features (torch.FloatTensor of shape (batch_size, output_dim)
The image embeddings obtained by applying the projection layer to the pooled output of OwlViTVisionModel.
The OwlViTModel 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, OwlViTModel
>>> model = OwlViTModel.from_pretrained("google/owlvit-base-patch32")
>>> processor = AutoProcessor.from_pretrained("google/owlvit-base-patch32")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = processor(images=image, return_tensors="pt")
>>> image_features = model.get_image_features(**inputs)( input_ids: Tensor attention_mask: Optional = None output_attentions: Optional = None output_hidden_states: Optional = None return_dict: Optional = None ) → transformers.modeling_outputs.BaseModelOutputWithPooling or tuple(torch.FloatTensor)
Parameters
torch.LongTensor of shape (batch_size * num_max_text_queries, sequence_length)) —
Indices of input sequence tokens in the vocabulary. Indices can be obtained using AutoTokenizer. See
PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details. What are input
IDs? torch.Tensor of shape (batch_size, num_max_text_queries, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 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.owlvit.configuration_owlvit.OwlViTTextConfig'>) 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 OwlViTTextModel 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 AutoProcessor, OwlViTTextModel
>>> model = OwlViTTextModel.from_pretrained("google/owlvit-base-patch32")
>>> processor = AutoProcessor.from_pretrained("google/owlvit-base-patch32")
>>> inputs = processor(
... text=[["a photo of a cat", "a photo of a dog"], ["photo of a astranaut"]], return_tensors="pt"
... )
>>> outputs = model(**inputs)
>>> last_hidden_state = outputs.last_hidden_state
>>> pooled_output = outputs.pooler_output # pooled (EOS token) states( pixel_values: Optional = None output_attentions: Optional = None output_hidden_states: Optional = None return_dict: Optional = None ) → transformers.modeling_outputs.BaseModelOutputWithPooling or tuple(torch.FloatTensor)
Parameters
torch.FloatTensor of shape (batch_size, num_channels, height, width)) —
Pixel values. 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.owlvit.configuration_owlvit.OwlViTVisionConfig'>) 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 OwlViTVisionModel 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, OwlViTVisionModel
>>> model = OwlViTVisionModel.from_pretrained("google/owlvit-base-patch32")
>>> processor = AutoProcessor.from_pretrained("google/owlvit-base-patch32")
>>> 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 CLS states( input_ids: Tensor pixel_values: FloatTensor attention_mask: Optional = None output_attentions: Optional = None output_hidden_states: Optional = None return_dict: Optional = None ) → transformers.models.owlvit.modeling_owlvit.OwlViTObjectDetectionOutput or tuple(torch.FloatTensor)
Parameters
torch.FloatTensor of shape (batch_size, num_channels, height, width)) —
Pixel values. torch.LongTensor of shape (batch_size * num_max_text_queries, sequence_length), optional) —
Indices of input sequence tokens in the vocabulary. Indices can be obtained using AutoTokenizer. See
PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details. What are input
IDs?. torch.Tensor of shape (batch_size, num_max_text_queries, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:bool, optional) —
Whether or not to return the last hidden state. See text_model_last_hidden_state and
vision_model_last_hidden_state under returned tensors for more detail. bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. Returns
transformers.models.owlvit.modeling_owlvit.OwlViTObjectDetectionOutput or tuple(torch.FloatTensor)
A transformers.models.owlvit.modeling_owlvit.OwlViTObjectDetectionOutput 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.owlvit.configuration_owlvit.OwlViTConfig'>) and inputs.
torch.FloatTensor of shape (1,), optional, returned when labels are provided)) — Total loss as a linear combination of a negative log-likehood (cross-entropy) for class prediction and a
bounding box loss. The latter is defined as a linear combination of the L1 loss and the generalized
scale-invariant IoU loss.Dict, optional) — A dictionary containing the individual losses. Useful for logging.torch.FloatTensor of shape (batch_size, num_patches, num_queries)) — Classification logits (including no-object) for all queries.torch.FloatTensor of shape (batch_size, num_patches, 4)) — Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These
values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding
possible padding). You can use post_process_object_detection() to retrieve the
unnormalized bounding boxes.torch.FloatTensor of shape (batch_size, num_max_text_queries, output_dim) — The text embeddings obtained by applying the projection layer to the pooled output of OwlViTTextModel.torch.FloatTensor of shape (batch_size, patch_size, patch_size, output_dim) — Pooled output of OwlViTVisionModel. OWL-ViT represents images as a set of image patches and computes
image embeddings for each patch.torch.FloatTensor of shape (batch_size, num_patches, hidden_size)) — Class embeddings of all image patches. OWL-ViT represents images as a set of image patches where the total
number of patches is (image_size / patch_size)**2.BaseModelOutputWithPooling) — The output of the OwlViTTextModel.BaseModelOutputWithPooling) — The output of the OwlViTVisionModel.The OwlViTForObjectDetection 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:
>>> import requests
>>> from PIL import Image
>>> import torch
>>> from transformers import AutoProcessor, OwlViTForObjectDetection
>>> processor = AutoProcessor.from_pretrained("google/owlvit-base-patch32")
>>> model = OwlViTForObjectDetection.from_pretrained("google/owlvit-base-patch32")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> texts = [["a photo of a cat", "a photo of a dog"]]
>>> inputs = processor(text=texts, images=image, return_tensors="pt")
>>> outputs = model(**inputs)
>>> # Target image sizes (height, width) to rescale box predictions [batch_size, 2]
>>> target_sizes = torch.Tensor([image.size[::-1]])
>>> # Convert outputs (bounding boxes and class logits) to final bounding boxes and scores
>>> results = processor.post_process_object_detection(
... outputs=outputs, threshold=0.1, target_sizes=target_sizes
... )
>>> i = 0 # Retrieve predictions for the first image for the corresponding text queries
>>> text = texts[i]
>>> boxes, scores, labels = results[i]["boxes"], results[i]["scores"], results[i]["labels"]
>>> for box, score, label in zip(boxes, scores, labels):
... box = [round(i, 2) for i in box.tolist()]
... print(f"Detected {text[label]} with confidence {round(score.item(), 3)} at location {box}")
Detected a photo of a cat with confidence 0.707 at location [324.97, 20.44, 640.58, 373.29]
Detected a photo of a cat with confidence 0.717 at location [1.46, 55.26, 315.55, 472.17]( pixel_values: FloatTensor query_pixel_values: Optional = None output_attentions: Optional = None output_hidden_states: Optional = None return_dict: Optional = None ) → transformers.models.owlvit.modeling_owlvit.OwlViTImageGuidedObjectDetectionOutput or tuple(torch.FloatTensor)
Parameters
torch.FloatTensor of shape (batch_size, num_channels, height, width)) —
Pixel values. torch.FloatTensor of shape (batch_size, num_channels, height, width)) —
Pixel values of query image(s) to be detected. Pass in one query image per target image. 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.models.owlvit.modeling_owlvit.OwlViTImageGuidedObjectDetectionOutput or tuple(torch.FloatTensor)
A transformers.models.owlvit.modeling_owlvit.OwlViTImageGuidedObjectDetectionOutput 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.owlvit.configuration_owlvit.OwlViTConfig'>) and inputs.
torch.FloatTensor of shape (batch_size, num_patches, num_queries)) — Classification logits (including no-object) for all queries.torch.FloatTensor of shape (batch_size, num_patches, 4)) — Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These
values are normalized in [0, 1], relative to the size of each individual target image in the batch
(disregarding possible padding). You can use post_process_object_detection() to
retrieve the unnormalized bounding boxes.torch.FloatTensor of shape (batch_size, num_patches, 4)) — Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These
values are normalized in [0, 1], relative to the size of each individual query image in the batch
(disregarding possible padding). You can use post_process_object_detection() to
retrieve the unnormalized bounding boxes.torch.FloatTensor of shape (batch_size, patch_size, patch_size, output_dim) — Pooled output of OwlViTVisionModel. OWL-ViT represents images as a set of image patches and computes
image embeddings for each patch.torch.FloatTensor of shape (batch_size, patch_size, patch_size, output_dim) — Pooled output of OwlViTVisionModel. OWL-ViT represents images as a set of image patches and computes
image embeddings for each patch.torch.FloatTensor of shape (batch_size, num_patches, hidden_size)) — Class embeddings of all image patches. OWL-ViT represents images as a set of image patches where the total
number of patches is (image_size / patch_size)**2.BaseModelOutputWithPooling) — The output of the OwlViTTextModel.BaseModelOutputWithPooling) — The output of the OwlViTVisionModel.The OwlViTForObjectDetection 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:
>>> import requests
>>> from PIL import Image
>>> import torch
>>> from transformers import AutoProcessor, OwlViTForObjectDetection
>>> processor = AutoProcessor.from_pretrained("google/owlvit-base-patch16")
>>> model = OwlViTForObjectDetection.from_pretrained("google/owlvit-base-patch16")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> query_url = "http://images.cocodataset.org/val2017/000000001675.jpg"
>>> query_image = Image.open(requests.get(query_url, stream=True).raw)
>>> inputs = processor(images=image, query_images=query_image, return_tensors="pt")
>>> with torch.no_grad():
... outputs = model.image_guided_detection(**inputs)
>>> # Target image sizes (height, width) to rescale box predictions [batch_size, 2]
>>> target_sizes = torch.Tensor([image.size[::-1]])
>>> # Convert outputs (bounding boxes and class logits) to Pascal VOC format (xmin, ymin, xmax, ymax)
>>> results = processor.post_process_image_guided_detection(
... outputs=outputs, threshold=0.6, nms_threshold=0.3, target_sizes=target_sizes
... )
>>> i = 0 # Retrieve predictions for the first image
>>> boxes, scores = results[i]["boxes"], results[i]["scores"]
>>> for box, score in zip(boxes, scores):
... box = [round(i, 2) for i in box.tolist()]
... print(f"Detected similar object with confidence {round(score.item(), 3)} at location {box}")
Detected similar object with confidence 0.856 at location [10.94, 50.4, 315.8, 471.39]
Detected similar object with confidence 1.0 at location [334.84, 25.33, 636.16, 374.71]