# Grounding DINO[[grounding-dino]]
PyTorch
## 개요[[overview]] Grounding DINO 모델은 Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Chunyuan Li, Jianwei Yang, Hang Su, Jun Zhu, Lei Zhang이 [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://huggingface.co/papers/2303.05499)에서 제안한 모델입니다. Grounding DINO는 폐쇄형 객체 탐지 모델을 텍스트 인코더로 확장하여 개방형 객체 탐지를 가능하게 합니다. 이 모델은 COCO 제로샷에서 52.5 AP와 같은 놀라운 결과를 달성합니다. 논문의 초록은 다음과 같습니다: *본 논문에서는 트랜스포머 기반 탐지기 DINO를 기반 사전 학습과 결합하여 Grounding DINO라는 개방형 객체 탐지기를 제시합니다. 이는 카테고리 이름이나 참조 표현 등의 사용자 입력으로 임의의 객체를 탐지할 수 있습니다. 개방형 객체 탐지의 핵심 해결책은 개방형 개념 일반화를 위해 폐쇄형 탐지기에 언어를 도입하는 것입니다. 언어와 비전 모달리티를 효과적으로 융합하기 위해, 폐쇄형 탐지기를 개념적으로 세 단계로 나누어 특성 강화기, 언어 기반 쿼리 선택, 교차 모달리티 융합을 위한 교차 모달리티 디코더를 포함하는 긴밀한 융합 솔루션을 제안합니다. 이전 연구들이 주로 새로운 카테고리에 대한 개방형 객체 탐지를 평가한 반면, 우리는 속성으로 지정된 객체에 대한 참조 표현 이해에 대한 평가도 수행할 것을 제안합니다. Grounding DINO는 COCO, LVIS, ODinW, RefCOCO/+/g 벤치마크를 포함한 세 가지 설정 모두에서 놀라운 성능을 보입니다. Grounding DINO는 COCO 탐지 제로샷 전이 벤치마크에서 52.5 AP(Average Precision, 평균 정밀도)를 달성했습니다. 즉, COCO의 학습 데이터 없이도 이러한 성과를 얻었습니다. 평균 26.1 AP로 ODinW 제로샷 벤치마크에서 새로운 기록을 세웠습니다.* drawing Grounding DINO 개요. 원본 논문에서 가져왔습니다. 이 모델은 [EduardoPacheco](https://huggingface.co/EduardoPacheco)와 [nielsr](https://huggingface.co/nielsr)에 의해 기여되었습니다. 원본 코드는 [여기](https://github.com/IDEA-Research/GroundingDINO)에서 찾을 수 있습니다. ## 사용 팁[[usage-tips]] - [GroundingDinoProcessor](/docs/transformers/v4.57.0/ko/model_doc/grounding-dino#transformers.GroundingDinoProcessor)를 사용하여 모델을 위한 이미지-텍스트 쌍을 준비할 수 있습니다. - 텍스트에서 클래스를 구분할 때는 마침표를 사용하세요. 예: "a cat. a dog." - 여러 클래스를 사용할 때(예: `"a cat. a dog."`), [GroundingDinoProcessor](/docs/transformers/v4.57.0/ko/model_doc/grounding-dino#transformers.GroundingDinoProcessor)의 `post_process_grounded_object_detection`을 사용해 출력을 후처리해야 합니다. `post_process_object_detection`에서 반환되는 레이블은 prob > threshold인 모델 차원의 인덱스를 나타내기 때문입니다. 다음은 제로샷 객체 탐지에 모델을 사용하는 방법입니다: ```python >>> import requests >>> import torch >>> from PIL import Image >>> from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection >>> model_id = "IDEA-Research/grounding-dino-tiny" >>> device = "cuda" >>> processor = AutoProcessor.from_pretrained(model_id) >>> model = AutoModelForZeroShotObjectDetection.from_pretrained(model_id).to(device) >>> image_url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(image_url, stream=True).raw) >>> # 고양이와 리모컨 확인 >>> text_labels = [["a cat", "a remote control"]] >>> inputs = processor(images=image, text=text_labels, return_tensors="pt").to(device) >>> with torch.no_grad(): ... outputs = model(**inputs) >>> results = processor.post_process_grounded_object_detection( ... outputs, ... inputs.input_ids, ... box_threshold=0.4, ... text_threshold=0.3, ... target_sizes=[image.size[::-1]] ... ) # 첫 번째 이미지 결과 가져오기 >>> result = results[0] >>> for box, score, labels in zip(result["boxes"], result["scores"], result["labels"]): ... box = [round(x, 2) for x in box.tolist()] ... print(f"Detected {labels} with confidence {round(score.item(), 3)} at location {box}") Detected a cat with confidence 0.468 at location [344.78, 22.9, 637.3, 373.62] Detected a cat with confidence 0.426 at location [11.74, 51.55, 316.51, 473.22] ``` ## Grounded SAM[[grounded-sam]] [Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks](https://huggingface.co/papers/2401.14159)에서 소개된 대로 Grounding DINO를 [Segment Anything](sam) 모델과 결합하여 텍스트 기반 마스크 생성을 할 수 있습니다. 자세한 내용은 이 [데모 노트북](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/Grounding%20DINO/GroundingDINO_with_Segment_Anything.ipynb) 🌍을 참조하세요. drawing Grounded SAM 개요. 원본 저장소에서 가져왔습니다. ## 리소스[[resources]] Grounding DINO를 시작하는 데 도움이 되는 공식 Hugging Face 및 커뮤니티(🌎로 표시) 리소스 목록입니다. 여기에 포함될 리소스를 제출하고 싶다면 Pull Request를 자유롭게 열어주세요. 검토해드리겠습니다! 리소스는 기존 리소스를 복제하는 대신 새로운 것을 보여주는 것이 이상적입니다. - Grounding DINO로 추론하고 [SAM](sam)과 결합하는 데모 노트북은 [여기](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/Grounding%20DINO)에서 찾을 수 있습니다. 🌎 ## GroundingDinoImageProcessor[[transformers.GroundingDinoImageProcessor]]
class transformers.GroundingDinoImageProcessortransformers.GroundingDinoImageProcessorhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/grounding_dino/image_processing_grounding_dino.py#L842[{"name": "format", "val": ": typing.Union[str, transformers.models.grounding_dino.image_processing_grounding_dino.AnnotationFormat] = "}, {"name": "do_resize", "val": ": bool = True"}, {"name": "size", "val": ": typing.Optional[dict[str, int]] = None"}, {"name": "resample", "val": ": Resampling = "}, {"name": "do_rescale", "val": ": bool = True"}, {"name": "rescale_factor", "val": ": typing.Union[int, float] = 0.00392156862745098"}, {"name": "do_normalize", "val": ": bool = True"}, {"name": "image_mean", "val": ": typing.Union[float, list[float], NoneType] = None"}, {"name": "image_std", "val": ": typing.Union[float, list[float], NoneType] = None"}, {"name": "do_convert_annotations", "val": ": typing.Optional[bool] = None"}, {"name": "do_pad", "val": ": bool = True"}, {"name": "pad_size", "val": ": typing.Optional[dict[str, int]] = None"}, {"name": "**kwargs", "val": ""}]- **format** (`str`, *optional*, defaults to `AnnotationFormat.COCO_DETECTION`) -- Data format of the annotations. One of "coco_detection" or "coco_panoptic". - **do_resize** (`bool`, *optional*, defaults to `True`) -- Controls 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. - **size** (`dict[str, int]` *optional*, defaults to `{"shortest_edge" -- 800, "longest_edge": 1333}`): Size of the image's `(height, width)` dimensions after resizing. Can be overridden by the `size` parameter in the `preprocess` method. Available options are: - `{"height": int, "width": int}`: The image will be resized to the exact size `(height, width)`. Do NOT keep the aspect ratio. - `{"shortest_edge": int, "longest_edge": int}`: The image will be resized to a maximum size respecting the aspect ratio and keeping the shortest edge less or equal to `shortest_edge` and the longest edge less or equal to `longest_edge`. - `{"max_height": int, "max_width": int}`: The image will be resized to the maximum size respecting the aspect ratio and keeping the height less or equal to `max_height` and the width less or equal to `max_width`. - **resample** (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`) -- Resampling filter to use if resizing the image. - **do_rescale** (`bool`, *optional*, defaults to `True`) -- Controls whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale` parameter in the `preprocess` method. - **rescale_factor** (`int` or `float`, *optional*, defaults to `1/255`) -- Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the `preprocess` method. Controls whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess` method. - **do_normalize** (`bool`, *optional*, defaults to `True`) -- Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess` method. - **image_mean** (`float` or `list[float]`, *optional*, defaults to `IMAGENET_DEFAULT_MEAN`) -- Mean values to use when normalizing the image. Can be a single value or a list of values, one for each channel. Can be overridden by the `image_mean` parameter in the `preprocess` method. - **image_std** (`float` or `list[float]`, *optional*, defaults to `IMAGENET_DEFAULT_STD`) -- Standard deviation values to use when normalizing the image. Can be a single value or a list of values, one for each channel. Can be overridden by the `image_std` parameter in the `preprocess` method. - **do_convert_annotations** (`bool`, *optional*, defaults to `True`) -- Controls whether to convert the annotations to the format expected by the DETR model. Converts the bounding boxes to the format `(center_x, center_y, width, height)` and in the range `[0, 1]`. Can be overridden by the `do_convert_annotations` parameter in the `preprocess` method. - **do_pad** (`bool`, *optional*, defaults to `True`) -- Controls whether to pad the image. Can be overridden by the `do_pad` parameter in the `preprocess` method. If `True`, padding will be applied to the bottom and right of the image with zeros. If `pad_size` is provided, the image will be padded to the specified dimensions. Otherwise, the image will be padded to the maximum height and width of the batch. - **pad_size** (`dict[str, int]`, *optional*) -- The size `{"height": int, "width" int}` to pad the images to. Must be larger than any image size provided for preprocessing. If `pad_size` is not provided, images will be padded to the largest height and width in the batch.0 Constructs a Grounding DINO image processor.
preprocesstransformers.GroundingDinoImageProcessor.preprocesshttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/grounding_dino/image_processing_grounding_dino.py#L1302[{"name": "images", "val": ": typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']]"}, {"name": "annotations", "val": ": typing.Union[dict[str, typing.Union[int, str, list[dict]]], list[dict[str, typing.Union[int, str, list[dict]]]], NoneType] = None"}, {"name": "return_segmentation_masks", "val": ": typing.Optional[bool] = None"}, {"name": "masks_path", "val": ": typing.Union[str, pathlib.Path, NoneType] = None"}, {"name": "do_resize", "val": ": typing.Optional[bool] = None"}, {"name": "size", "val": ": typing.Optional[dict[str, int]] = None"}, {"name": "resample", "val": " = None"}, {"name": "do_rescale", "val": ": typing.Optional[bool] = None"}, {"name": "rescale_factor", "val": ": typing.Union[int, float, NoneType] = None"}, {"name": "do_normalize", "val": ": typing.Optional[bool] = None"}, {"name": "do_convert_annotations", "val": ": typing.Optional[bool] = None"}, {"name": "image_mean", "val": ": typing.Union[float, list[float], NoneType] = None"}, {"name": "image_std", "val": ": typing.Union[float, list[float], NoneType] = None"}, {"name": "do_pad", "val": ": typing.Optional[bool] = None"}, {"name": "format", "val": ": typing.Union[str, transformers.models.grounding_dino.image_processing_grounding_dino.AnnotationFormat, NoneType] = None"}, {"name": "return_tensors", "val": ": typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None"}, {"name": "data_format", "val": ": typing.Union[str, transformers.image_utils.ChannelDimension] = "}, {"name": "input_data_format", "val": ": typing.Union[str, transformers.image_utils.ChannelDimension, NoneType] = None"}, {"name": "pad_size", "val": ": typing.Optional[dict[str, int]] = None"}, {"name": "**kwargs", "val": ""}]- **images** (`ImageInput`) -- Image or batch of images 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`. - **annotations** (`AnnotationType` or `list[AnnotationType]`, *optional*) -- List of annotations associated with the image or batch of images. If annotation is for object detection, the annotations should be a dictionary with the following keys: - "image_id" (`int`): The image id. - "annotations" (`list[Dict]`): List of annotations for an image. Each annotation should be a dictionary. An image can have no annotations, in which case the list should be empty. If annotation is for segmentation, the annotations should be a dictionary with the following keys: - "image_id" (`int`): The image id. - "segments_info" (`list[Dict]`): List of segments for an image. Each segment should be a dictionary. An image can have no segments, in which case the list should be empty. - "file_name" (`str`): The file name of the image. - **return_segmentation_masks** (`bool`, *optional*, defaults to self.return_segmentation_masks) -- Whether to return segmentation masks. - **masks_path** (`str` or `pathlib.Path`, *optional*) -- Path to the directory containing the segmentation masks. - **do_resize** (`bool`, *optional*, defaults to self.do_resize) -- Whether to resize the image. - **size** (`dict[str, int]`, *optional*, defaults to self.size) -- Size of the image's `(height, width)` dimensions after resizing. Available options are: - `{"height": int, "width": int}`: The image will be resized to the exact size `(height, width)`. Do NOT keep the aspect ratio. - `{"shortest_edge": int, "longest_edge": int}`: The image will be resized to a maximum size respecting the aspect ratio and keeping the shortest edge less or equal to `shortest_edge` and the longest edge less or equal to `longest_edge`. - `{"max_height": int, "max_width": int}`: The image will be resized to the maximum size respecting the aspect ratio and keeping the height less or equal to `max_height` and the width less or equal to `max_width`. - **resample** (`PILImageResampling`, *optional*, defaults to self.resample) -- Resampling filter to use when resizing the image. - **do_rescale** (`bool`, *optional*, defaults to self.do_rescale) -- Whether to rescale the image. - **rescale_factor** (`float`, *optional*, defaults to self.rescale_factor) -- Rescale factor to use when rescaling the image. - **do_normalize** (`bool`, *optional*, defaults to self.do_normalize) -- Whether to normalize the image. - **do_convert_annotations** (`bool`, *optional*, defaults to self.do_convert_annotations) -- Whether to convert the annotations to the format expected by the model. Converts the bounding boxes from the format `(top_left_x, top_left_y, width, height)` to `(center_x, center_y, width, height)` and in relative coordinates. - **image_mean** (`float` or `list[float]`, *optional*, defaults to self.image_mean) -- Mean to use when normalizing the image. - **image_std** (`float` or `list[float]`, *optional*, defaults to self.image_std) -- Standard deviation to use when normalizing the image. - **do_pad** (`bool`, *optional*, defaults to self.do_pad) -- Whether to pad the image. If `True`, padding will be applied to the bottom and right of the image with zeros. If `pad_size` is provided, the image will be padded to the specified dimensions. Otherwise, the image will be padded to the maximum height and width of the batch. - **format** (`str` or `AnnotationFormat`, *optional*, defaults to self.format) -- Format of the annotations. - **return_tensors** (`str` or `TensorType`, *optional*, defaults to self.return_tensors) -- Type of tensors to return. If `None`, will return the list of images. - **data_format** (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`) -- The channel dimension format for the output 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. - Unset: Use the channel dimension format of the input image. - **input_data_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. - **pad_size** (`dict[str, int]`, *optional*) -- The size `{"height": int, "width" int}` to pad the images to. Must be larger than any image size provided for preprocessing. If `pad_size` is not provided, images will be padded to the largest height and width in the batch.0 Preprocess an image or a batch of images so that it can be used by the model.
## GroundingDinoImageProcessorFast[[transformers.GroundingDinoImageProcessorFast]]
class transformers.GroundingDinoImageProcessorFasttransformers.GroundingDinoImageProcessorFasthttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/grounding_dino/image_processing_grounding_dino_fast.py#L295[{"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.models.grounding_dino.image_processing_grounding_dino_fast.GroundingDinoFastImageProcessorKwargs]"}] Constructs a fast Grounding Dino image processor.
preprocesstransformers.GroundingDinoImageProcessorFast.preprocesshttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/grounding_dino/image_processing_grounding_dino_fast.py#L567[{"name": "images", "val": ": typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']]"}, {"name": "annotations", "val": ": typing.Union[dict[str, typing.Union[int, str, list[dict]]], list[dict[str, typing.Union[int, str, list[dict]]]], NoneType] = None"}, {"name": "masks_path", "val": ": typing.Union[str, pathlib.Path, NoneType] = None"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.models.grounding_dino.image_processing_grounding_dino_fast.GroundingDinoFastImageProcessorKwargs]"}]- **images** (`Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']]`) -- Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If passing in images with pixel values between 0 and 1, set `do_rescale=False`. - **annotations** (`AnnotationType` or `list[AnnotationType]`, *optional*) -- List of annotations associated with the image or batch of images. If annotation is for object detection, the annotations should be a dictionary with the following keys: - "image_id" (`int`): The image id. - "annotations" (`list[Dict]`): List of annotations for an image. Each annotation should be a dictionary. An image can have no annotations, in which case the list should be empty. If annotation is for segmentation, the annotations should be a dictionary with the following keys: - "image_id" (`int`): The image id. - "segments_info" (`list[Dict]`): List of segments for an image. Each segment should be a dictionary. An image can have no segments, in which case the list should be empty. - "file_name" (`str`): The file name of the image. - **masks_path** (`str` or `pathlib.Path`, *optional*) -- Path to the directory containing the segmentation masks. - **do_resize** (`bool`, *optional*) -- Whether to resize the image. - **size** (`dict[str, int]`, *optional*) -- Describes the maximum input dimensions to the model. - **default_to_square** (`bool`, *optional*) -- Whether to default to a square image when resizing, if size is an int. - **resample** (`Union[PILImageResampling, F.InterpolationMode, NoneType]`) -- Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`. Only has an effect if `do_resize` is set to `True`. - **do_center_crop** (`bool`, *optional*) -- Whether to center crop the image. - **crop_size** (`dict[str, int]`, *optional*) -- Size of the output image after applying `center_crop`. - **do_rescale** (`bool`, *optional*) -- Whether to rescale the image. - **rescale_factor** (`Union[int, float, NoneType]`) -- Rescale factor to rescale the image by if `do_rescale` is set to `True`. - **do_normalize** (`bool`, *optional*) -- Whether to normalize the image. - **image_mean** (`Union[float, list[float], NoneType]`) -- Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`. - **image_std** (`Union[float, list[float], NoneType]`) -- Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to `True`. - **do_pad** (`bool`, *optional*) -- Whether to pad the image. Padding is done either to the largest size in the batch or to a fixed square size per image. The exact padding strategy depends on the model. - **pad_size** (`dict[str, int]`, *optional*) -- The size in `{"height": int, "width" int}` to pad the images to. Must be larger than any image size provided for preprocessing. If `pad_size` is not provided, images will be padded to the largest height and width in the batch. Applied only when `do_pad=True.` - **do_convert_rgb** (`bool`, *optional*) -- Whether to convert the image to RGB. - **return_tensors** (`Union[str, ~utils.generic.TensorType, NoneType]`) -- Returns stacked tensors if set to `pt, otherwise returns a list of tensors. - **data_format** (`~image_utils.ChannelDimension`, *optional*) -- Only `ChannelDimension.FIRST` is supported. Added for compatibility with slow processors. - **input_data_format** (`Union[~image_utils.ChannelDimension, str, NoneType]`) -- The channel dimension format for the input image. If unset, the channel dimension format is inferred from the input image. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. - **device** (`torch.device`, *optional*) -- The device to process the images on. If unset, the device is inferred from the input images. - **disable_grouping** (`bool`, *optional*) -- Whether to disable grouping of images by size to process them individually and not in batches. If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157 - **format** (`str`, *optional*, defaults to `AnnotationFormat.COCO_DETECTION`) -- Data format of the annotations. One of "coco_detection" or "coco_panoptic". - **do_convert_annotations** (`bool`, *optional*, defaults to `True`) -- Controls whether to convert the annotations to the format expected by the GROUNDING_DINO model. Converts the bounding boxes to the format `(center_x, center_y, width, height)` and in the range `[0, 1]`. Can be overridden by the `do_convert_annotations` parameter in the `preprocess` method. - **return_segmentation_masks** (`bool`, *optional*, defaults to `False`) -- Whether to return segmentation masks.0``- **data** (`dict`) -- Dictionary of lists/arrays/tensors returned by the __call__ method ('pixel_values', etc.). - **tensor_type** (`Union[None, str, TensorType]`, *optional*) -- You can give a tensor_type here to convert the lists of integers in PyTorch/TensorFlow/Numpy Tensors at initialization.
post_process_object_detectiontransformers.GroundingDinoImageProcessorFast.post_process_object_detectionhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/grounding_dino/image_processing_grounding_dino_fast.py#L722[{"name": "outputs", "val": ": GroundingDinoObjectDetectionOutput"}, {"name": "threshold", "val": ": float = 0.1"}, {"name": "target_sizes", "val": ": typing.Union[transformers.utils.generic.TensorType, list[tuple], NoneType] = None"}]- **outputs** (`GroundingDinoObjectDetectionOutput`) -- Raw outputs of the model. - **threshold** (`float`, *optional*, defaults to 0.1) -- Score threshold to keep object detection predictions. - **target_sizes** (`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.0`list[Dict]`A list of dictionaries, each dictionary containing the following keys: - "scores": The confidence scores for each predicted box on the image. - "labels": Indexes of the classes predicted by the model on the image. - "boxes": Image bounding boxes in (top_left_x, top_left_y, bottom_right_x, bottom_right_y) format. Converts the raw output of [GroundingDinoForObjectDetection](/docs/transformers/v4.57.0/ko/model_doc/grounding-dino#transformers.GroundingDinoForObjectDetection) into final bounding boxes in (top_left_x, top_left_y, bottom_right_x, bottom_right_y) format.
## GroundingDinoProcessor[[transformers.GroundingDinoProcessor]]
class transformers.GroundingDinoProcessortransformers.GroundingDinoProcessorhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/grounding_dino/processing_grounding_dino.py#L127[{"name": "image_processor", "val": ""}, {"name": "tokenizer", "val": ""}]- **image_processor** (`GroundingDinoImageProcessor`) -- An instance of [GroundingDinoImageProcessor](/docs/transformers/v4.57.0/ko/model_doc/grounding-dino#transformers.GroundingDinoImageProcessor). The image processor is a required input. - **tokenizer** (`AutoTokenizer`) -- An instance of ['PreTrainedTokenizer`]. The tokenizer is a required input.0 Constructs a Grounding DINO processor which wraps a Deformable DETR image processor and a BERT tokenizer into a single processor. [GroundingDinoProcessor](/docs/transformers/v4.57.0/ko/model_doc/grounding-dino#transformers.GroundingDinoProcessor) offers all the functionalities of [GroundingDinoImageProcessor](/docs/transformers/v4.57.0/ko/model_doc/grounding-dino#transformers.GroundingDinoImageProcessor) and [AutoTokenizer](/docs/transformers/v4.57.0/ko/model_doc/auto#transformers.AutoTokenizer). See the docstring of `__call__()` and [decode()](/docs/transformers/v4.57.0/ko/main_classes/processors#transformers.ProcessorMixin.decode) for more information.
post_process_grounded_object_detectiontransformers.GroundingDinoProcessor.post_process_grounded_object_detectionhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/grounding_dino/processing_grounding_dino.py#L192[{"name": "outputs", "val": ": GroundingDinoObjectDetectionOutput"}, {"name": "input_ids", "val": ": typing.Optional[transformers.utils.generic.TensorType] = None"}, {"name": "threshold", "val": ": float = 0.25"}, {"name": "text_threshold", "val": ": float = 0.25"}, {"name": "target_sizes", "val": ": typing.Union[transformers.utils.generic.TensorType, list[tuple], NoneType] = None"}, {"name": "text_labels", "val": ": typing.Optional[list[list[str]]] = None"}]- **outputs** (`GroundingDinoObjectDetectionOutput`) -- Raw outputs of the model. - **input_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) -- The token ids of the input text. If not provided will be taken from the model output. - **threshold** (`float`, *optional*, defaults to 0.25) -- Threshold to keep object detection predictions based on confidence score. - **text_threshold** (`float`, *optional*, defaults to 0.25) -- Score threshold to keep text detection predictions. - **target_sizes** (`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. - **text_labels** (`list[list[str]]`, *optional*) -- List of candidate labels to be detected on each image. At the moment it's *NOT used*, but required to be in signature for the zero-shot object detection pipeline. Text labels are instead extracted from the `input_ids` tensor provided in `outputs`.0`list[Dict]`A list of dictionaries, each dictionary containing the - **scores**: tensor of confidence scores for detected objects - **boxes**: tensor of bounding boxes in [x0, y0, x1, y1] format - **labels**: list of text labels for each detected object (will be replaced with integer ids in v4.51.0) - **text_labels**: list of text labels for detected objects Converts the raw output of [GroundingDinoForObjectDetection](/docs/transformers/v4.57.0/ko/model_doc/grounding-dino#transformers.GroundingDinoForObjectDetection) into final bounding boxes in (top_left_x, top_left_y, bottom_right_x, bottom_right_y) format and get the associated text label.
## GroundingDinoConfig[[transformers.GroundingDinoConfig]]
class transformers.GroundingDinoConfigtransformers.GroundingDinoConfighttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/grounding_dino/configuration_grounding_dino.py#L26[{"name": "backbone_config", "val": " = None"}, {"name": "backbone", "val": " = None"}, {"name": "use_pretrained_backbone", "val": " = False"}, {"name": "use_timm_backbone", "val": " = False"}, {"name": "backbone_kwargs", "val": " = None"}, {"name": "text_config", "val": " = None"}, {"name": "num_queries", "val": " = 900"}, {"name": "encoder_layers", "val": " = 6"}, {"name": "encoder_ffn_dim", "val": " = 2048"}, {"name": "encoder_attention_heads", "val": " = 8"}, {"name": "decoder_layers", "val": " = 6"}, {"name": "decoder_ffn_dim", "val": " = 2048"}, {"name": "decoder_attention_heads", "val": " = 8"}, {"name": "is_encoder_decoder", "val": " = True"}, {"name": "activation_function", "val": " = 'relu'"}, {"name": "d_model", "val": " = 256"}, {"name": "dropout", "val": " = 0.1"}, {"name": "attention_dropout", "val": " = 0.0"}, {"name": "activation_dropout", "val": " = 0.0"}, {"name": "auxiliary_loss", "val": " = False"}, {"name": "position_embedding_type", "val": " = 'sine'"}, {"name": "num_feature_levels", "val": " = 4"}, {"name": "encoder_n_points", "val": " = 4"}, {"name": "decoder_n_points", "val": " = 4"}, {"name": "two_stage", "val": " = True"}, {"name": "class_cost", "val": " = 1.0"}, {"name": "bbox_cost", "val": " = 5.0"}, {"name": "giou_cost", "val": " = 2.0"}, {"name": "bbox_loss_coefficient", "val": " = 5.0"}, {"name": "giou_loss_coefficient", "val": " = 2.0"}, {"name": "focal_alpha", "val": " = 0.25"}, {"name": "disable_custom_kernels", "val": " = False"}, {"name": "max_text_len", "val": " = 256"}, {"name": "text_enhancer_dropout", "val": " = 0.0"}, {"name": "fusion_droppath", "val": " = 0.1"}, {"name": "fusion_dropout", "val": " = 0.0"}, {"name": "embedding_init_target", "val": " = True"}, {"name": "query_dim", "val": " = 4"}, {"name": "decoder_bbox_embed_share", "val": " = True"}, {"name": "two_stage_bbox_embed_share", "val": " = False"}, {"name": "positional_embedding_temperature", "val": " = 20"}, {"name": "init_std", "val": " = 0.02"}, {"name": "layer_norm_eps", "val": " = 1e-05"}, {"name": "**kwargs", "val": ""}]- **backbone_config** (`PretrainedConfig` or `dict`, *optional*, defaults to `ResNetConfig()`) -- The configuration of the backbone model. - **backbone** (`str`, *optional*) -- Name of backbone to use when `backbone_config` is `None`. If `use_pretrained_backbone` is `True`, this will load the corresponding pretrained weights from the timm or transformers library. If `use_pretrained_backbone` is `False`, this loads the backbone's config and uses that to initialize the backbone with random weights. - **use_pretrained_backbone** (`bool`, *optional*, defaults to `False`) -- Whether to use pretrained weights for the backbone. - **use_timm_backbone** (`bool`, *optional*, defaults to `False`) -- Whether to load `backbone` from the timm library. If `False`, the backbone is loaded from the transformers library. - **backbone_kwargs** (`dict`, *optional*) -- Keyword arguments to be passed to AutoBackbone when loading from a checkpoint e.g. `{'out_indices': (0, 1, 2, 3)}`. Cannot be specified if `backbone_config` is set. - **text_config** (`Union[AutoConfig, dict]`, *optional*, defaults to `BertConfig`) -- The config object or dictionary of the text backbone. - **num_queries** (`int`, *optional*, defaults to 900) -- Number of object queries, i.e. detection slots. This is the maximal number of objects [GroundingDinoModel](/docs/transformers/v4.57.0/ko/model_doc/grounding-dino#transformers.GroundingDinoModel) can detect in a single image. - **encoder_layers** (`int`, *optional*, defaults to 6) -- Number of encoder layers. - **encoder_ffn_dim** (`int`, *optional*, defaults to 2048) -- Dimension of the "intermediate" (often named feed-forward) layer in decoder. - **encoder_attention_heads** (`int`, *optional*, defaults to 8) -- Number of attention heads for each attention layer in the Transformer encoder. - **decoder_layers** (`int`, *optional*, defaults to 6) -- Number of decoder layers. - **decoder_ffn_dim** (`int`, *optional*, defaults to 2048) -- Dimension of the "intermediate" (often named feed-forward) layer in decoder. - **decoder_attention_heads** (`int`, *optional*, defaults to 8) -- Number of attention heads for each attention layer in the Transformer decoder. - **is_encoder_decoder** (`bool`, *optional*, defaults to `True`) -- Whether the model is used as an encoder/decoder or not. - **activation_function** (`str` or `function`, *optional*, defaults to `"relu"`) -- The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. - **d_model** (`int`, *optional*, defaults to 256) -- Dimension of the layers. - **dropout** (`float`, *optional*, defaults to 0.1) -- The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. - **attention_dropout** (`float`, *optional*, defaults to 0.0) -- The dropout ratio for the attention probabilities. - **activation_dropout** (`float`, *optional*, defaults to 0.0) -- The dropout ratio for activations inside the fully connected layer. - **auxiliary_loss** (`bool`, *optional*, defaults to `False`) -- Whether auxiliary decoding losses (loss at each decoder layer) are to be used. - **position_embedding_type** (`str`, *optional*, defaults to `"sine"`) -- Type of position embeddings to be used on top of the image features. One of `"sine"` or `"learned"`. - **num_feature_levels** (`int`, *optional*, defaults to 4) -- The number of input feature levels. - **encoder_n_points** (`int`, *optional*, defaults to 4) -- The number of sampled keys in each feature level for each attention head in the encoder. - **decoder_n_points** (`int`, *optional*, defaults to 4) -- The number of sampled keys in each feature level for each attention head in the decoder. - **two_stage** (`bool`, *optional*, defaults to `True`) -- Whether to apply a two-stage deformable DETR, where the region proposals are also generated by a variant of Grounding DINO, which are further fed into the decoder for iterative bounding box refinement. - **class_cost** (`float`, *optional*, defaults to 1.0) -- Relative weight of the classification error in the Hungarian matching cost. - **bbox_cost** (`float`, *optional*, defaults to 5.0) -- Relative weight of the L1 error of the bounding box coordinates in the Hungarian matching cost. - **giou_cost** (`float`, *optional*, defaults to 2.0) -- Relative weight of the generalized IoU loss of the bounding box in the Hungarian matching cost. - **bbox_loss_coefficient** (`float`, *optional*, defaults to 5.0) -- Relative weight of the L1 bounding box loss in the object detection loss. - **giou_loss_coefficient** (`float`, *optional*, defaults to 2.0) -- Relative weight of the generalized IoU loss in the object detection loss. - **focal_alpha** (`float`, *optional*, defaults to 0.25) -- Alpha parameter in the focal loss. - **disable_custom_kernels** (`bool`, *optional*, defaults to `False`) -- Disable the use of custom CUDA and CPU kernels. This option is necessary for the ONNX export, as custom kernels are not supported by PyTorch ONNX export. - **max_text_len** (`int`, *optional*, defaults to 256) -- The maximum length of the text input. - **text_enhancer_dropout** (`float`, *optional*, defaults to 0.0) -- The dropout ratio for the text enhancer. - **fusion_droppath** (`float`, *optional*, defaults to 0.1) -- The droppath ratio for the fusion module. - **fusion_dropout** (`float`, *optional*, defaults to 0.0) -- The dropout ratio for the fusion module. - **embedding_init_target** (`bool`, *optional*, defaults to `True`) -- Whether to initialize the target with Embedding weights. - **query_dim** (`int`, *optional*, defaults to 4) -- The dimension of the query vector. - **decoder_bbox_embed_share** (`bool`, *optional*, defaults to `True`) -- Whether to share the bbox regression head for all decoder layers. - **two_stage_bbox_embed_share** (`bool`, *optional*, defaults to `False`) -- Whether to share the bbox embedding between the two-stage bbox generator and the region proposal generation. - **positional_embedding_temperature** (`float`, *optional*, defaults to 20) -- The temperature for Sine Positional Embedding that is used together with vision backbone. - **init_std** (`float`, *optional*, defaults to 0.02) -- The standard deviation of the truncated_normal_initializer for initializing all weight matrices. - **layer_norm_eps** (`float`, *optional*, defaults to 1e-05) -- The epsilon used by the layer normalization layers.0 This is the configuration class to store the configuration of a [GroundingDinoModel](/docs/transformers/v4.57.0/ko/model_doc/grounding-dino#transformers.GroundingDinoModel). It is used to instantiate a Grounding DINO model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Grounding DINO [IDEA-Research/grounding-dino-tiny](https://huggingface.co/IDEA-Research/grounding-dino-tiny) architecture. Configuration objects inherit from [PretrainedConfig](/docs/transformers/v4.57.0/ko/main_classes/configuration#transformers.PretrainedConfig) and can be used to control the model outputs. Read the documentation from [PretrainedConfig](/docs/transformers/v4.57.0/ko/main_classes/configuration#transformers.PretrainedConfig) for more information. Examples: ```python >>> from transformers import GroundingDinoConfig, GroundingDinoModel >>> # Initializing a Grounding DINO IDEA-Research/grounding-dino-tiny style configuration >>> configuration = GroundingDinoConfig() >>> # Initializing a model (with random weights) from the IDEA-Research/grounding-dino-tiny style configuration >>> model = GroundingDinoModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```
## GroundingDinoModel[[transformers.GroundingDinoModel]]
class transformers.GroundingDinoModeltransformers.GroundingDinoModelhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/grounding_dino/modeling_grounding_dino.py#L1908[{"name": "config", "val": ": GroundingDinoConfig"}]- **config** ([GroundingDinoConfig](/docs/transformers/v4.57.0/ko/model_doc/grounding-dino#transformers.GroundingDinoConfig)) -- Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from_pretrained()](/docs/transformers/v4.57.0/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.0 The bare Grounding DINO Model (consisting of a backbone and encoder-decoder Transformer) outputting raw hidden-states without any specific head on top. This model inherits from [PreTrainedModel](/docs/transformers/v4.57.0/ko/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
forwardtransformers.GroundingDinoModel.forwardhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/grounding_dino/modeling_grounding_dino.py#L2050[{"name": "pixel_values", "val": ": Tensor"}, {"name": "input_ids", "val": ": Tensor"}, {"name": "token_type_ids", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "attention_mask", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "pixel_mask", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "encoder_outputs", "val": " = None"}, {"name": "output_attentions", "val": " = None"}, {"name": "output_hidden_states", "val": " = None"}, {"name": "return_dict", "val": " = None"}]- **pixel_values** (`torch.Tensor` of shape `(batch_size, num_channels, image_size, image_size)`) -- The tensors corresponding to the input images. Pixel values can be obtained using [GroundingDinoImageProcessor](/docs/transformers/v4.57.0/ko/model_doc/grounding-dino#transformers.GroundingDinoImageProcessor). See [GroundingDinoImageProcessor.__call__()](/docs/transformers/v4.57.0/ko/model_doc/vit#transformers.ViTFeatureExtractor.__call__) for details ([GroundingDinoProcessor](/docs/transformers/v4.57.0/ko/model_doc/grounding-dino#transformers.GroundingDinoProcessor) uses [GroundingDinoImageProcessor](/docs/transformers/v4.57.0/ko/model_doc/grounding-dino#transformers.GroundingDinoImageProcessor) for processing images). - **input_ids** (`torch.LongTensor` of shape `(batch_size, text_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](/docs/transformers/v4.57.0/ko/model_doc/auto#transformers.AutoTokenizer). See [BertTokenizer.__call__()](/docs/transformers/v4.57.0/ko/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details. - **token_type_ids** (`torch.LongTensor` of shape `(batch_size, text_sequence_length)`, *optional*) -- Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: 0 corresponds to a `sentence A` token, 1 corresponds to a `sentence B` token [What are token type IDs?](../glossary#token-type-ids) - **attention_mask** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) -- Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) - **pixel_mask** (`torch.Tensor` of shape `(batch_size, height, width)`, *optional*) -- Mask to avoid performing attention on padding pixel values. Mask values selected in `[0, 1]`: - 1 for pixels that are real (i.e. **not masked**), - 0 for pixels that are padding (i.e. **masked**). [What are attention masks?](../glossary#attention-mask) - **encoder_outputs** (``) -- Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`) `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. - **output_attentions** (``) -- Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. - **output_hidden_states** (``) -- Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. - **return_dict** (``) -- Whether or not to return a [ModelOutput](/docs/transformers/v4.57.0/ko/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple.0 The [GroundingDinoModel](/docs/transformers/v4.57.0/ko/model_doc/grounding-dino#transformers.GroundingDinoModel) 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: ```python >>> from transformers import AutoProcessor, AutoModel >>> from PIL import Image >>> import requests >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> text = "a cat." >>> processor = AutoProcessor.from_pretrained("IDEA-Research/grounding-dino-tiny") >>> model = AutoModel.from_pretrained("IDEA-Research/grounding-dino-tiny") >>> inputs = processor(images=image, text=text, return_tensors="pt") >>> outputs = model(**inputs) >>> last_hidden_states = outputs.last_hidden_state >>> list(last_hidden_states.shape) [1, 900, 256] ```
## GroundingDinoForObjectDetection[[transformers.GroundingDinoForObjectDetection]]
class transformers.GroundingDinoForObjectDetectiontransformers.GroundingDinoForObjectDetectionhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/grounding_dino/modeling_grounding_dino.py#L2413[{"name": "config", "val": ": GroundingDinoConfig"}]- **config** ([GroundingDinoConfig](/docs/transformers/v4.57.0/ko/model_doc/grounding-dino#transformers.GroundingDinoConfig)) -- Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from_pretrained()](/docs/transformers/v4.57.0/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.0 Grounding DINO Model (consisting of a backbone and encoder-decoder Transformer) with object detection heads on top, for tasks such as COCO detection. This model inherits from [PreTrainedModel](/docs/transformers/v4.57.0/ko/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
forwardtransformers.GroundingDinoForObjectDetection.forwardhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/grounding_dino/modeling_grounding_dino.py#L2453[{"name": "pixel_values", "val": ": FloatTensor"}, {"name": "input_ids", "val": ": LongTensor"}, {"name": "token_type_ids", "val": ": typing.Optional[torch.LongTensor] = None"}, {"name": "attention_mask", "val": ": typing.Optional[torch.LongTensor] = None"}, {"name": "pixel_mask", "val": ": typing.Optional[torch.BoolTensor] = None"}, {"name": "encoder_outputs", "val": ": typing.Union[transformers.models.grounding_dino.modeling_grounding_dino.GroundingDinoEncoderOutput, tuple, NoneType] = None"}, {"name": "output_attentions", "val": ": typing.Optional[bool] = None"}, {"name": "output_hidden_states", "val": ": typing.Optional[bool] = None"}, {"name": "return_dict", "val": ": typing.Optional[bool] = None"}, {"name": "labels", "val": ": typing.Optional[list[dict[str, typing.Union[torch.LongTensor, torch.FloatTensor]]]] = None"}]- **pixel_values** (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`) -- The tensors corresponding to the input images. Pixel values can be obtained using [GroundingDinoImageProcessor](/docs/transformers/v4.57.0/ko/model_doc/grounding-dino#transformers.GroundingDinoImageProcessor). See [GroundingDinoImageProcessor.__call__()](/docs/transformers/v4.57.0/ko/model_doc/vit#transformers.ViTFeatureExtractor.__call__) for details ([GroundingDinoProcessor](/docs/transformers/v4.57.0/ko/model_doc/grounding-dino#transformers.GroundingDinoProcessor) uses [GroundingDinoImageProcessor](/docs/transformers/v4.57.0/ko/model_doc/grounding-dino#transformers.GroundingDinoImageProcessor) for processing images). - **input_ids** (`torch.LongTensor` of shape `(batch_size, text_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](/docs/transformers/v4.57.0/ko/model_doc/auto#transformers.AutoTokenizer). See [BertTokenizer.__call__()](/docs/transformers/v4.57.0/ko/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details. - **token_type_ids** (`torch.LongTensor` of shape `(batch_size, text_sequence_length)`, *optional*) -- Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: 0 corresponds to a `sentence A` token, 1 corresponds to a `sentence B` token [What are token type IDs?](../glossary#token-type-ids) - **attention_mask** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) -- Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) - **pixel_mask** (`torch.BoolTensor` of shape `(batch_size, height, width)`, *optional*) -- Mask to avoid performing attention on padding pixel values. Mask values selected in `[0, 1]`: - 1 for pixels that are real (i.e. **not masked**), - 0 for pixels that are padding (i.e. **masked**). [What are attention masks?](../glossary#attention-mask) - **encoder_outputs** (`Union[~models.grounding_dino.modeling_grounding_dino.GroundingDinoEncoderOutput, tuple, NoneType]`) -- Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`) `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. - **output_attentions** (`bool`, *optional*) -- Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. - **output_hidden_states** (`bool`, *optional*) -- Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. - **return_dict** (`bool`, *optional*) -- Whether or not to return a [ModelOutput](/docs/transformers/v4.57.0/ko/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple. - **labels** (`list[Dict]` of len `(batch_size,)`, *optional*) -- Labels for computing the bipartite matching loss. List of dicts, each dictionary containing at least the following 2 keys: 'class_labels' and 'boxes' (the class labels and bounding boxes of an image in the batch respectively). The class labels themselves should be a `torch.LongTensor` of len `(number of bounding boxes in the image,)` and the boxes a `torch.FloatTensor` of shape `(number of bounding boxes in the image, 4)`.0 The [GroundingDinoForObjectDetection](/docs/transformers/v4.57.0/ko/model_doc/grounding-dino#transformers.GroundingDinoForObjectDetection) 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: ```python >>> import requests >>> import torch >>> from PIL import Image >>> from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection >>> model_id = "IDEA-Research/grounding-dino-tiny" >>> device = "cuda" >>> processor = AutoProcessor.from_pretrained(model_id) >>> model = AutoModelForZeroShotObjectDetection.from_pretrained(model_id).to(device) >>> image_url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(image_url, stream=True).raw) >>> # Check for cats and remote controls >>> text_labels = [["a cat", "a remote control"]] >>> inputs = processor(images=image, text=text_labels, return_tensors="pt").to(device) >>> with torch.no_grad(): ... outputs = model(**inputs) >>> results = processor.post_process_grounded_object_detection( ... outputs, ... threshold=0.4, ... text_threshold=0.3, ... target_sizes=[(image.height, image.width)] ... ) >>> # Retrieve the first image result >>> result = results[0] >>> for box, score, text_label in zip(result["boxes"], result["scores"], result["text_labels"]): ... box = [round(x, 2) for x in box.tolist()] ... print(f"Detected {text_label} with confidence {round(score.item(), 3)} at location {box}") Detected a cat with confidence 0.479 at location [344.7, 23.11, 637.18, 374.28] Detected a cat with confidence 0.438 at location [12.27, 51.91, 316.86, 472.44] Detected a remote control with confidence 0.478 at location [38.57, 70.0, 176.78, 118.18] ```