# YOLOS [YOLOS](https://huggingface.co/papers/2106.00666) uses a [Vision Transformer (ViT)](./vit) for object detection with minimal modifications and region priors. It can achieve performance comparable to specialized object detection models and frameworks with knowledge about 2D spatial structures. You can find all the original YOLOS checkpoints under the [HUST Vision Lab](https://huggingface.co/hustvl/models?search=yolos) organization. YOLOS architecture. Taken from the original paper. > [!TIP] > This model wasa contributed by [nielsr](https://huggingface.co/nielsr). > Click on the YOLOS models in the right sidebar for more examples of how to apply YOLOS to different object detection tasks. The example below demonstrates how to detect objects with [Pipeline](/docs/transformers/main/en/main_classes/pipelines#transformers.Pipeline) or the [AutoModel](/docs/transformers/main/en/model_doc/auto#transformers.AutoModel) class. ```python from transformers import pipeline detector = pipeline( task="object-detection", model="hustvl/yolos-base", device=0 ) detector("https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png") ``` ```python import requests import torch from PIL import Image from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("hustvl/yolos-base") model = AutoModelForObjectDetection.from_pretrained("hustvl/yolos-base", attn_implementation="sdpa", device_map="auto") url = "https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png" image = Image.open(requests.get(url, stream=True).raw).convert("RGB") inputs = processor(images=image, return_tensors="pt").to(model.device) with torch.no_grad(): outputs = model(**inputs) logits = outputs.logits.softmax(-1) scores, labels = logits[..., :-1].max(-1) boxes = outputs.pred_boxes threshold = 0.3 keep = scores[0] > threshold filtered_scores = scores[0][keep] filtered_labels = labels[0][keep] filtered_boxes = boxes[0][keep] width, height = image.size pixel_boxes = filtered_boxes * torch.tensor([width, height, width, height], device=boxes.device) for score, label, box in zip(filtered_scores, filtered_labels, pixel_boxes): x0, y0, x1, y1 = box.tolist() print(f"Label {model.config.id2label[label.item()]}: {score:.2f} at [{x0:.0f}, {y0:.0f}, {x1:.0f}, {y1:.0f}]") ``` ## Notes - Use [YolosImageProcessor](/docs/transformers/main/en/model_doc/yolos#transformers.YolosImageProcessor) for preparing images (and optional targets) for the model. Contrary to [DETR](./detr), YOLOS doesn't require a `pixel_mask`. ## Resources - Refer to these [notebooks](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/YOLOS) for inference and fine-tuning with [YolosForObjectDetection](/docs/transformers/main/en/model_doc/yolos#transformers.YolosForObjectDetection) on a custom dataset. ## YolosConfig[[transformers.YolosConfig]] #### transformers.YolosConfig[[transformers.YolosConfig]] [Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/yolos/configuration_yolos.py#L24) This is the configuration class to store the configuration of a YolosModel. It is used to instantiate a Yolos 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 [hustvl/yolos-base](https://huggingface.co/hustvl/yolos-base) Configuration objects inherit from [PreTrainedConfig](/docs/transformers/main/en/main_classes/configuration#transformers.PreTrainedConfig) and can be used to control the model outputs. Read the documentation from [PreTrainedConfig](/docs/transformers/main/en/main_classes/configuration#transformers.PreTrainedConfig) for more information. Example: ```python >>> from transformers import YolosConfig, YolosModel >>> # Initializing a YOLOS hustvl/yolos-base style configuration >>> configuration = YolosConfig() >>> # Initializing a model (with random weights) from the hustvl/yolos-base style configuration >>> model = YolosModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` **Parameters:** hidden_size (`int`, *optional*, defaults to `768`) : Dimension of the hidden representations. num_hidden_layers (`int`, *optional*, defaults to `12`) : Number of hidden layers in the Transformer decoder. num_attention_heads (`int`, *optional*, defaults to `12`) : Number of attention heads for each attention layer in the Transformer decoder. intermediate_size (`int`, *optional*, defaults to `3072`) : Dimension of the MLP representations. hidden_act (`str`, *optional*, defaults to `gelu`) : The non-linear activation function (function or string) in the decoder. For example, `"gelu"`, `"relu"`, `"silu"`, etc. hidden_dropout_prob (`Union[float, int]`, *optional*, defaults to `0.0`) : The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_probs_dropout_prob (`Union[float, int]`, *optional*, defaults to `0.0`) : The dropout ratio for the attention probabilities. initializer_range (`float`, *optional*, defaults to `0.02`) : The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to `1e-12`) : The epsilon used by the layer normalization layers. image_size (`Union[list[int], tuple[int, ...]]`, *optional*, defaults to `(512, 864)`) : The size (resolution) of each image. patch_size (`Union[int, list[int], tuple[int, int]]`, *optional*, defaults to `16`) : The size (resolution) of each patch. num_channels (`int`, *optional*, defaults to `3`) : The number of input channels. qkv_bias (`bool`, *optional*, defaults to `True`) : Whether to add a bias to the queries, keys and values. num_detection_tokens (`int`, *optional*, defaults to 100) : The number of detection tokens. use_mid_position_embeddings (`bool`, *optional*, defaults to `True`) : Whether to use the mid-layer position encodings. auxiliary_loss (`bool`, *optional*, defaults to `False`) : Whether auxiliary decoding losses (losses at each decoder layer) are to be used. class_cost (`int`, *optional*, defaults to `1`) : Relative weight of the classification error in the Hungarian matching cost. bbox_cost (`int`, *optional*, defaults to `5`) : Relative weight of the L1 bounding box error in the Hungarian matching cost. giou_cost (`int`, *optional*, defaults to `2`) : Relative weight of the generalized IoU loss in the Hungarian matching cost. bbox_loss_coefficient (`int`, *optional*, defaults to `5`) : Relative weight of the L1 bounding box loss in the panoptic segmentation loss. giou_loss_coefficient (`int`, *optional*, defaults to `2`) : Relative weight of the generalized IoU loss in the panoptic segmentation loss. eos_coefficient (`float`, *optional*, defaults to `0.1`) : Relative classification weight of the 'no-object' class in the object detection loss. ## YolosImageProcessor[[transformers.YolosImageProcessor]] #### transformers.YolosImageProcessor[[transformers.YolosImageProcessor]] [Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/yolos/image_processing_yolos.py#L292) Constructs a YolosImageProcessor image processor. preprocesstransformers.YolosImageProcessor.preprocesshttps://github.com/huggingface/transformers/blob/main/src/transformers/models/yolos/image_processing_yolos.py#L537[{"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": ": dict[str, int | str | list[dict]] | list[dict[str, int | str | list[dict]]] | None = None"}, {"name": "return_segmentation_masks", "val": ": bool | None = None"}, {"name": "masks_path", "val": ": str | pathlib.Path | None = None"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.models.yolos.image_processing_yolos.YolosImageProcessorKwargs]"}]- **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*) -- Annotations to transform according to the padding that is applied to the images. - **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. - **format** (`str`, *kwargs*, *optional*, defaults to `AnnotationFormat.COCO_DETECTION`) -- Data format of the annotations. One of "coco_detection" or "coco_panoptic". - **do_convert_annotations** (`bool`, *kwargs*, *optional*, defaults to `True`) -- Controls whether to convert the annotations to the format expected by the YOLOS 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_tensors** (`str` or [TensorType](/docs/transformers/main/en/internal/file_utils#transformers.TensorType), *optional*) -- Returns stacked tensors if set to `'pt'`, otherwise returns a list of tensors. - ****kwargs** ([ImagesKwargs](/docs/transformers/main/en/main_classes/processors#transformers.ImagesKwargs), *optional*) -- Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class for the complete list of supported arguments.0`~image_processing_base.BatchFeature`- **data** (`dict`) -- Dictionary of lists/arrays/tensors returned by the __call__ method ('pixel_values', etc.). - **tensor_type** (`Union[None, str, TensorType]`, *optional*) -- You can give a tensor_type here to convert the lists of integers in PyTorch/Numpy Tensors at initialization. **Parameters:** format (`str`, *kwargs*, *optional*, defaults to `AnnotationFormat.COCO_DETECTION`) : Data format of the annotations. One of "coco_detection" or "coco_panoptic". do_convert_annotations (`bool`, *kwargs*, *optional*, defaults to `True`) : Controls whether to convert the annotations to the format expected by the YOLOS 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. - ****kwargs** ([ImagesKwargs](/docs/transformers/main/en/main_classes/processors#transformers.ImagesKwargs), *optional*) : Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class for the complete list of supported arguments. **Returns:** ``~image_processing_base.BatchFeature`` - **data** (`dict`) -- Dictionary of lists/arrays/tensors returned by the __call__ method ('pixel_values', etc.). - **tensor_type** (`Union[None, str, TensorType]`, *optional*) -- You can give a tensor_type here to convert the lists of integers in PyTorch/Numpy Tensors at initialization. ## YolosImageProcessorPil[[transformers.YolosImageProcessorPil]] #### transformers.YolosImageProcessorPil[[transformers.YolosImageProcessorPil]] [Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/yolos/image_processing_pil_yolos.py#L291) Constructs a YolosImageProcessor image processor. preprocesstransformers.YolosImageProcessorPil.preprocesshttps://github.com/huggingface/transformers/blob/main/src/transformers/models/yolos/image_processing_pil_yolos.py#L559[{"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": ": dict[str, int | str | list[dict]] | list[dict[str, int | str | list[dict]]] | None = None"}, {"name": "return_segmentation_masks", "val": ": bool | None = None"}, {"name": "masks_path", "val": ": str | pathlib.Path | None = None"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.models.yolos.image_processing_pil_yolos.YolosImageProcessorKwargs]"}]- **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*) -- Annotations to transform according to the padding that is applied to the images. - **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. - **format** (`str`, *kwargs*, *optional*, defaults to `AnnotationFormat.COCO_DETECTION`) -- Data format of the annotations. One of "coco_detection" or "coco_panoptic". - **do_convert_annotations** (`bool`, *kwargs*, *optional*, defaults to `True`) -- Controls whether to convert the annotations to the format expected by the YOLOS 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_tensors** (`str` or [TensorType](/docs/transformers/main/en/internal/file_utils#transformers.TensorType), *optional*) -- Returns stacked tensors if set to `'pt'`, otherwise returns a list of tensors. - ****kwargs** ([ImagesKwargs](/docs/transformers/main/en/main_classes/processors#transformers.ImagesKwargs), *optional*) -- Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class for the complete list of supported arguments.0`~image_processing_base.BatchFeature`- **data** (`dict`) -- Dictionary of lists/arrays/tensors returned by the __call__ method ('pixel_values', etc.). - **tensor_type** (`Union[None, str, TensorType]`, *optional*) -- You can give a tensor_type here to convert the lists of integers in PyTorch/Numpy Tensors at initialization. **Parameters:** format (`str`, *kwargs*, *optional*, defaults to `AnnotationFormat.COCO_DETECTION`) : Data format of the annotations. One of "coco_detection" or "coco_panoptic". do_convert_annotations (`bool`, *kwargs*, *optional*, defaults to `True`) : Controls whether to convert the annotations to the format expected by the YOLOS 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. - ****kwargs** ([ImagesKwargs](/docs/transformers/main/en/main_classes/processors#transformers.ImagesKwargs), *optional*) : Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class for the complete list of supported arguments. **Returns:** ``~image_processing_base.BatchFeature`` - **data** (`dict`) -- Dictionary of lists/arrays/tensors returned by the __call__ method ('pixel_values', etc.). - **tensor_type** (`Union[None, str, TensorType]`, *optional*) -- You can give a tensor_type here to convert the lists of integers in PyTorch/Numpy Tensors at initialization. #### pad[[transformers.YolosImageProcessorPil.pad]] [Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/yolos/image_processing_pil_yolos.py#L521) #### post_process_object_detection[[transformers.YolosImageProcessorPil.post_process_object_detection]] [Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/yolos/image_processing_pil_yolos.py#L704) Converts the raw output of [YolosForObjectDetection](/docs/transformers/main/en/model_doc/yolos#transformers.YolosForObjectDetection) into final bounding boxes in (top_left_x, top_left_y, bottom_right_x, bottom_right_y) format. Only supports PyTorch. **Parameters:** outputs (`YolosObjectDetectionOutput`) : Raw outputs of the model. threshold (`float`, *optional*) : 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. **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. ## YolosModel[[transformers.YolosModel]] #### transformers.YolosModel[[transformers.YolosModel]] [Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/yolos/modeling_yolos.py#L461) The bare Yolos Model outputting raw hidden-states without any specific head on top. This model inherits from [PreTrainedModel](/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. forwardtransformers.YolosModel.forwardhttps://github.com/huggingface/transformers/blob/main/src/transformers/models/yolos/modeling_yolos.py#L482[{"name": "pixel_values", "val": ": torch.Tensor | None = None"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]- **pixel_values** (`torch.Tensor` of shape `(batch_size, num_channels, image_size, image_size)`, *optional*) -- The tensors corresponding to the input images. Pixel values can be obtained using [YolosImageProcessor](/docs/transformers/main/en/model_doc/yolos#transformers.YolosImageProcessor). See `YolosImageProcessor.__call__()` for details (`processor_class` uses [YolosImageProcessor](/docs/transformers/main/en/model_doc/yolos#transformers.YolosImageProcessor) for processing images).0[BaseModelOutputWithPooling](/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)`A [BaseModelOutputWithPooling](/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration ([YolosConfig](/docs/transformers/main/en/model_doc/yolos#transformers.YolosConfig)) and inputs. The [YolosModel](/docs/transformers/main/en/model_doc/yolos#transformers.YolosModel) forward method, overrides the `__call__` special method. Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them. - **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the model. - **pooler_output** (`torch.FloatTensor` of shape `(batch_size, hidden_size)`) -- Last layer hidden-state of the first token of the sequence (classification token) after further processing through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns the classification token after processing through a linear layer and a tanh activation function. The linear layer weights are trained from the next sentence prediction (classification) objective during pretraining. - **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. - **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Example: ```python ``` **Parameters:** config ([YolosConfig](/docs/transformers/main/en/model_doc/yolos#transformers.YolosConfig)) : Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from_pretrained()](/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights. add_pooling_layer (`bool`, *optional*, defaults to `True`) : Whether to add a pooling layer **Returns:** `[BaseModelOutputWithPooling](/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)`` A [BaseModelOutputWithPooling](/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration ([YolosConfig](/docs/transformers/main/en/model_doc/yolos#transformers.YolosConfig)) and inputs. ## YolosForObjectDetection[[transformers.YolosForObjectDetection]] #### transformers.YolosForObjectDetection[[transformers.YolosForObjectDetection]] [Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/yolos/modeling_yolos.py#L544) YOLOS Model (consisting of a ViT encoder) with object detection heads on top, for tasks such as COCO detection. This model inherits from [PreTrainedModel](/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. forwardtransformers.YolosForObjectDetection.forwardhttps://github.com/huggingface/transformers/blob/main/src/transformers/models/yolos/modeling_yolos.py#L567[{"name": "pixel_values", "val": ": FloatTensor"}, {"name": "labels", "val": ": list[dict] | None = None"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}]- **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 [YolosImageProcessor](/docs/transformers/main/en/model_doc/yolos#transformers.YolosImageProcessor). See `YolosImageProcessor.__call__()` for details (`processor_class` uses [YolosImageProcessor](/docs/transformers/main/en/model_doc/yolos#transformers.YolosImageProcessor) for processing images). - **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`YolosObjectDetectionOutput` or `tuple(torch.FloatTensor)`A `YolosObjectDetectionOutput` 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 ([YolosConfig](/docs/transformers/main/en/model_doc/yolos#transformers.YolosConfig)) and inputs. The [YolosForObjectDetection](/docs/transformers/main/en/model_doc/yolos#transformers.YolosForObjectDetection) forward method, overrides the `__call__` special method. Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them. - **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `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. - **loss_dict** (`Dict`, *optional*) -- A dictionary containing the individual losses. Useful for logging. - **logits** (`torch.FloatTensor` of shape `(batch_size, num_queries, num_classes + 1)`) -- Classification logits (including no-object) for all queries. - **pred_boxes** (`torch.FloatTensor` of shape `(batch_size, num_queries, 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 `~YolosImageProcessor.post_process` to retrieve the unnormalized bounding boxes. - **auxiliary_outputs** (`list[Dict]`, *optional*) -- Optional, only returned when auxiliary losses are activated (i.e. `config.auxiliary_loss` is set to `True`) and labels are provided. It is a list of dictionaries containing the two above keys (`logits` and `pred_boxes`) for each decoder layer. - **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) -- Sequence of hidden-states at the output of the last layer of the decoder of the model. - **hidden_states** (`tuple[torch.FloatTensor]`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. - **attentions** (`tuple[torch.FloatTensor]`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Examples: ```python >>> from transformers import AutoImageProcessor, AutoModelForObjectDetection >>> import torch >>> from PIL import Image >>> import httpx >>> from io import BytesIO >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> with httpx.stream("GET", url) as response: ... image = Image.open(BytesIO(response.read())) >>> image_processor = AutoImageProcessor.from_pretrained("hustvl/yolos-tiny") >>> model = AutoModelForObjectDetection.from_pretrained("hustvl/yolos-tiny") >>> inputs = image_processor(images=image, return_tensors="pt") >>> outputs = model(**inputs) >>> # convert outputs (bounding boxes and class logits) to Pascal VOC format (xmin, ymin, xmax, ymax) >>> target_sizes = torch.tensor([image.size[::-1]]) >>> results = image_processor.post_process_object_detection(outputs, threshold=0.9, target_sizes=target_sizes)[ ... 0 ... ] >>> for score, label, box in zip(results["scores"], results["labels"], results["boxes"]): ... box = [round(i, 2) for i in box.tolist()] ... print( ... f"Detected {model.config.id2label[label.item()]} with confidence " ... f"{round(score.item(), 3)} at location {box}" ... ) Detected remote with confidence 0.991 at location [46.48, 72.78, 178.98, 119.3] Detected remote with confidence 0.908 at location [336.48, 79.27, 368.23, 192.36] Detected cat with confidence 0.934 at location [337.18, 18.06, 638.14, 373.09] Detected cat with confidence 0.979 at location [10.93, 53.74, 313.41, 470.67] Detected remote with confidence 0.974 at location [41.63, 72.23, 178.09, 119.99] ``` **Parameters:** config ([YolosConfig](/docs/transformers/main/en/model_doc/yolos#transformers.YolosConfig)) : Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from_pretrained()](/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights. **Returns:** ``YolosObjectDetectionOutput` or `tuple(torch.FloatTensor)`` A `YolosObjectDetectionOutput` 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 ([YolosConfig](/docs/transformers/main/en/model_doc/yolos#transformers.YolosConfig)) and inputs.