
 
YOLOS
	
Overview
	
The YOLOS model was proposed in You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection by Yuxin Fang, Bencheng Liao, Xinggang Wang, Jiemin Fang, Jiyang Qi, Rui Wu, Jianwei Niu, Wenyu Liu.
YOLOS proposes to just leverage the plain Vision Transformer (ViT) for object detection, inspired by DETR. It turns out that a base-sized encoder-only Transformer can also achieve 42 AP on COCO, similar to DETR and much more complex frameworks such as Faster R-CNN.
The abstract from the paper is the following:
Can Transformer perform 2D object- and region-level recognition from a pure sequence-to-sequence perspective with minimal knowledge about the 2D spatial structure? To answer this question, we present You Only Look at One Sequence (YOLOS), a series of object detection models based on the vanilla Vision Transformer with the fewest possible modifications, region priors, as well as inductive biases of the target task. We find that YOLOS pre-trained on the mid-sized ImageNet-1k dataset only can already achieve quite competitive performance on the challenging COCO object detection benchmark, e.g., YOLOS-Base directly adopted from BERT-Base architecture can obtain 42.0 box AP on COCO val. We also discuss the impacts as well as limitations of current pre-train schemes and model scaling strategies for Transformer in vision through YOLOS.
Tips:
One can use YolosImageProcessor for preparing images (and optional targets) for the model. Contrary to DETR, YOLOS doesn’t require a pixel_mask to be created.
YOLOS architecture. Taken from the original paper.
This model was contributed by nielsr. The original code can be found here.
Resources
	
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with YOLOS.
Object Detection
All example notebooks illustrating inference + fine-tuning YolosForObjectDetection on a custom dataset can be found here.
See also: Object detection task guide
If you’re interested in submitting a resource to be included here, please feel free to open a Pull Request and we’ll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
YolosConfig
	
class transformers.YolosConfig
<
source
>
(
hidden_size = 768
num_hidden_layers = 12
num_attention_heads = 12
intermediate_size = 3072
hidden_act = 'gelu'
hidden_dropout_prob = 0.0
attention_probs_dropout_prob = 0.0
initializer_range = 0.02
layer_norm_eps = 1e-12
image_size = [512, 864]
patch_size = 16
num_channels = 3
qkv_bias = True
num_detection_tokens = 100
use_mid_position_embeddings = True
auxiliary_loss = False
class_cost = 1
bbox_cost = 5
giou_cost = 2
bbox_loss_coefficient = 5
giou_loss_coefficient = 2
eos_coefficient = 0.1
**kwargs
)
Parameters 
hidden_size (int, optional, defaults to 768) —
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (int, optional, defaults to 12) —
Number of hidden layers in the Transformer encoder.
num_attention_heads (int, optional, defaults to 12) —
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (int, optional, defaults to 3072) —
Dimensionality of the “intermediate” (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (str or function, optional, defaults to "gelu") —
The non-linear activation function (function or string) in the encoder and pooler. If string, "gelu",
"relu", "selu" and "gelu_new" are supported.
hidden_dropout_prob (float, optional, defaults to 0.1) —
The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (float, optional, defaults to 0.1) —
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 (List[int], optional, defaults to [512, 864]) —
The size (resolution) of each image.
patch_size (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 (loss at each decoder layer) are to be used.
class_cost (float, optional, defaults to 1) —
Relative weight of the classification error in the Hungarian matching cost.
bbox_cost (float, optional, defaults to 5) —
Relative weight of the L1 error of the bounding box coordinates in the Hungarian matching cost.
giou_cost (float, optional, defaults to 2) —
Relative weight of the generalized IoU loss of the bounding box in the Hungarian matching cost.
bbox_loss_coefficient (float, optional, defaults to 5) —
Relative weight of the L1 bounding box loss in the object detection loss.
giou_loss_coefficient (float, optional, defaults to 2) —
Relative weight of the generalized IoU loss in the object detection loss.
eos_coefficient (float, optional, defaults to 0.1) —
Relative classification weight of the ‘no-object’ class in the object detection loss.
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 YOLOS
hustvl/yolos-base architecture.
Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the
documentation from PretrainedConfig for more information.
Example:
	Copied
 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
YolosImageProcessor
	
class transformers.YolosImageProcessor
<
source
>
(
format: typing.Union[str, transformers.models.yolos.image_processing_yolos.AnnotionFormat] = <AnnotionFormat.COCO_DETECTION: 'coco_detection'>
do_resize: bool = True
size: typing.Dict[str, int] = None
resample: Resampling = <Resampling.BILINEAR: 2>
do_rescale: bool = True
rescale_factor: typing.Union[int, float] = 0.00392156862745098
do_normalize: bool = True
image_mean: typing.Union[float, typing.List[float]] = None
image_std: typing.Union[float, typing.List[float]] = None
do_pad: bool = True
**kwargs
)
Parameters 
format (str, optional, defaults to "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.
resample (PILImageResampling, optional, defaults to PILImageResampling.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.
do_normalize —
Controls 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_pad (bool, optional, defaults to True) —
Controls whether to pad the image to the largest image in a batch and create a pixel mask. Can be
overridden by the do_pad parameter in the preprocess method.
Constructs a Detr image processor.
preprocess
<
source
>
(
images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), typing.List[ForwardRef('PIL.Image.Image')], typing.List[numpy.ndarray], typing.List[ForwardRef('torch.Tensor')]]
annotations: typing.Union[typing.Dict[str, typing.Union[int, str, typing.List[typing.Dict]]], typing.List[typing.Dict[str, typing.Union[int, str, typing.List[typing.Dict]]]], NoneType] = None
return_segmentation_masks: bool = None
masks_path: typing.Union[str, pathlib.Path, NoneType] = None
do_resize: typing.Optional[bool] = None
size: typing.Union[typing.Dict[str, int], NoneType] = None
resample = None
do_rescale: typing.Optional[bool] = None
rescale_factor: typing.Union[int, float, NoneType] = None
do_normalize: typing.Optional[bool] = None
image_mean: typing.Union[float, typing.List[float], NoneType] = None
image_std: typing.Union[float, typing.List[float], NoneType] = None
do_pad: typing.Optional[bool] = None
format: typing.Union[str, transformers.models.yolos.image_processing_yolos.AnnotionFormat, NoneType] = None
return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None
data_format: typing.Union[str, transformers.image_utils.ChannelDimension] = <ChannelDimension.FIRST: 'channels_first'>
**kwargs
)
Parameters 
images (ImageInput) —
Image or batch of images to preprocess.
annotations (AnnotationType or List[AnnotationType], optional) —
List of annotations associated with the image or batch of images. If annotionation 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 annotionation 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 after resizing.
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.
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.
format (str or AnnotionFormat, 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 (str or ChannelDimension, optional, defaults to self.data_format) —
The channel dimension format of the image. If not provided, it will be the same as the input image.
Preprocess an image or a batch of images so that it can be used by the model.
pad
<
source
>
(
images: typing.List[numpy.ndarray]
return_pixel_mask: bool = False
return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None
data_format: typing.Optional[transformers.image_utils.ChannelDimension] = None
)
Parameters 
image (np.ndarray) —
Image to pad.
return_pixel_mask (bool, optional, defaults to True) —
Whether to return a pixel mask.
input_channel_dimension (ChannelDimension, optional) —
The channel dimension format of the image. If not provided, it will be inferred from the input image.
data_format (str or ChannelDimension, optional) —
The channel dimension format of the image. If not provided, it will be the same as the input image.
Pads a batch of images to the bottom and right of the image with zeros to the size of largest height and width
in the batch and optionally returns their corresponding pixel mask.
post_process_object_detection
<
source
>
(
outputs
threshold: float = 0.5
target_sizes: typing.Union[transformers.utils.generic.TensorType, typing.List[typing.Tuple]] = None
)
→
List[Dict]
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.
Converts the raw output of YolosForObjectDetection into final bounding boxes in (top_left_x, top_left_y,
bottom_right_x, bottom_right_y) format. Only supports PyTorch.
YolosFeatureExtractor
	
class transformers.YolosFeatureExtractor
<
source
>
(
*args
**kwargs
)
__call__
<
source
>
(
images
**kwargs
)
Preprocess an image or a batch of images.
pad
<
source
>
(
images: typing.List[numpy.ndarray]
return_pixel_mask: bool = False
return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None
data_format: typing.Optional[transformers.image_utils.ChannelDimension] = None
)
Parameters 
image (np.ndarray) —
Image to pad.
return_pixel_mask (bool, optional, defaults to True) —
Whether to return a pixel mask.
input_channel_dimension (ChannelDimension, optional) —
The channel dimension format of the image. If not provided, it will be inferred from the input image.
data_format (str or ChannelDimension, optional) —
The channel dimension format of the image. If not provided, it will be the same as the input image.
Pads a batch of images to the bottom and right of the image with zeros to the size of largest height and width
in the batch and optionally returns their corresponding pixel mask.
post_process_object_detection
<
source
>
(
outputs
threshold: float = 0.5
target_sizes: typing.Union[transformers.utils.generic.TensorType, typing.List[typing.Tuple]] = None
)
→
List[Dict]
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.
Converts the raw output of YolosForObjectDetection into final bounding boxes in (top_left_x, top_left_y,
bottom_right_x, bottom_right_y) format. Only supports PyTorch.
YolosModel
	
class transformers.YolosModel
<
source
>
(
config: YolosConfig
add_pooling_layer: bool = True
)
Parameters 
config (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() method to load the model weights.
The bare YOLOS Model transformer outputting raw hidden-states without any specific head on top.
This model is 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.
forward
<
source
>
(
pixel_values: typing.Optional[torch.Tensor] = None
head_mask: typing.Optional[torch.Tensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.BaseModelOutputWithPooling or tuple(torch.FloatTensor)
Parameters 
pixel_values (torch.FloatTensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Pixel values can be obtained using AutoImageProcessor. See
YolosImageProcessor.call() for details.
head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
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 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 (YolosConfig) 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 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.
Example:
	Copied
 from transformers import AutoImageProcessor, YolosModel
 import torch
 from datasets import load_dataset
 dataset = load_dataset("huggingface/cats-image")
 image = dataset["test"]["image"][0]
 image_processor = AutoImageProcessor.from_pretrained("hustvl/yolos-small")
 model = YolosModel.from_pretrained("hustvl/yolos-small")
 inputs = image_processor(image, return_tensors="pt")
 with torch.no_grad():
...     outputs = model(**inputs)
 last_hidden_states = outputs.last_hidden_state
 list(last_hidden_states.shape)
[1, 3401, 384]
YolosForObjectDetection
	
class transformers.YolosForObjectDetection
<
source
>
(
config: YolosConfig
)
Parameters 
config (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() method to load the model weights.
YOLOS Model (consisting of a ViT encoder) with object detection heads on top, for tasks such as COCO detection.
This model is 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.
forward
<
source
>
(
pixel_values: FloatTensor
labels: typing.Optional[typing.List[typing.Dict]] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.models.yolos.modeling_yolos.YolosObjectDetectionOutput or tuple(torch.FloatTensor)
Parameters 
pixel_values (torch.FloatTensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Pixel values can be obtained using AutoImageProcessor. See
YolosImageProcessor.call() for details.
head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
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 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).
Returns
transformers.models.yolos.modeling_yolos.YolosObjectDetectionOutput or tuple(torch.FloatTensor)
A transformers.models.yolos.modeling_yolos.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) and inputs.
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 post_process() to retrieve the unnormalized bounding
boxes.
auxiliary_outputs (list[Dict], optional) — Optional, only returned when auxilary 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.
The 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.
Examples:
	Copied
 from transformers import AutoImageProcessor, AutoModelForObjectDetection
 import torch
 from PIL import Image
 import requests
 url = "http://images.cocodataset.org/val2017/000000039769.jpg"
 image = Image.open(requests.get(url, stream=True).raw)
 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 COCO API
 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 0.994 at location [46.96, 72.61, 181.02, 119.73]
Detected remote with confidence 0.975 at location [340.66, 79.19, 372.59, 192.65]
Detected cat with confidence 0.984 at location [12.27, 54.25, 319.42, 470.99]
Detected remote with confidence 0.922 at location [41.66, 71.96, 178.7, 120.33]
Detected cat with confidence 0.914 at location [342.34, 21.48, 638.64, 372.46]
←ViViT
Audio Spectrogram Transformer→
YOLOS
Overview
Resources
YolosConfig
YolosImageProcessor
YolosFeatureExtractor
YolosModel
YolosForObjectDetection
