
 
Conditional DETR
	
Overview
	
The Conditional DETR model was proposed in Conditional DETR for Fast Training Convergence by Depu Meng, Xiaokang Chen, Zejia Fan, Gang Zeng, Houqiang Li, Yuhui Yuan, Lei Sun, Jingdong Wang. Conditional DETR presents a conditional cross-attention mechanism for fast DETR training. Conditional DETR converges 6.7× to 10× faster than DETR.
The abstract from the paper is the following:
The recently-developed DETR approach applies the transformer encoder and decoder architecture to object detection and achieves promising performance. In this paper, we handle the critical issue, slow training convergence, and present a conditional cross-attention mechanism for fast DETR training. Our approach is motivated by that the cross-attention in DETR relies highly on the content embeddings for localizing the four extremities and predicting the box, which increases the need for high-quality content embeddings and thus the training difficulty. Our approach, named conditional DETR, learns a conditional spatial query from the decoder embedding for decoder multi-head cross-attention. The benefit is that through the conditional spatial query, each cross-attention head is able to attend to a band containing a distinct region, e.g., one object extremity or a region inside the object box. This narrows down the spatial range for localizing the distinct regions for object classification and box regression, thus relaxing the dependence on the content embeddings and easing the training. Empirical results show that conditional DETR converges 6.7× faster for the backbones R50 and R101 and 10× faster for stronger backbones DC5-R50 and DC5-R101. Code is available at https://github.com/Atten4Vis/ConditionalDETR.
Conditional DETR shows much faster convergence compared to the original DETR. Taken from the original paper.
This model was contributed by DepuMeng. The original code can be found here.
Documentation resources
	
Object detection task guide
ConditionalDetrConfig
	
class transformers.ConditionalDetrConfig
<
source
>
(
use_timm_backbone = True
backbone_config = None
num_channels = 3
num_queries = 300
encoder_layers = 6
encoder_ffn_dim = 2048
encoder_attention_heads = 8
decoder_layers = 6
decoder_ffn_dim = 2048
decoder_attention_heads = 8
encoder_layerdrop = 0.0
decoder_layerdrop = 0.0
is_encoder_decoder = True
activation_function = 'relu'
d_model = 256
dropout = 0.1
attention_dropout = 0.0
activation_dropout = 0.0
init_std = 0.02
init_xavier_std = 1.0
auxiliary_loss = False
position_embedding_type = 'sine'
backbone = 'resnet50'
use_pretrained_backbone = True
dilation = False
class_cost = 2
bbox_cost = 5
giou_cost = 2
mask_loss_coefficient = 1
dice_loss_coefficient = 1
cls_loss_coefficient = 2
bbox_loss_coefficient = 5
giou_loss_coefficient = 2
focal_alpha = 0.25
**kwargs
)
Parameters 
use_timm_backbone (bool, optional, defaults to True) —
Whether or not to use the timm library for the backbone. If set to False, will use the AutoBackbone
API.
backbone_config (PretrainedConfig or dict, optional) —
The configuration of the backbone model. Only used in case use_timm_backbone is set to False in which
case it will default to ResNetConfig().
num_channels (int, optional, defaults to 3) —
The number of input channels.
num_queries (int, optional, defaults to 100) —
Number of object queries, i.e. detection slots. This is the maximal number of objects
ConditionalDetrModel can detect in a single image. For COCO, we recommend 100 queries.
d_model (int, optional, defaults to 256) —
Dimension of the layers.
encoder_layers (int, optional, defaults to 6) —
Number of encoder layers.
decoder_layers (int, optional, defaults to 6) —
Number of decoder layers.
encoder_attention_heads (int, optional, defaults to 8) —
Number of attention heads for each attention layer in the Transformer encoder.
decoder_attention_heads (int, optional, defaults to 8) —
Number of attention heads for each attention layer in the Transformer decoder.
decoder_ffn_dim (int, optional, defaults to 2048) —
Dimension of the “intermediate” (often named feed-forward) layer in decoder.
encoder_ffn_dim (int, optional, defaults to 2048) —
Dimension of the “intermediate” (often named feed-forward) layer in decoder.
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.
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.
init_std (float, optional, defaults to 0.02) —
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
init_xavier_std (float, optional, defaults to 1) —
The scaling factor used for the Xavier initialization gain in the HM Attention map module.
encoder_layerdrop (float, optional, defaults to 0.0) —
The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
for more details.
decoder_layerdrop (float, optional, defaults to 0.0) —
The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
for more details.
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".
backbone (str, optional, defaults to "resnet50") —
Name of convolutional backbone to use in case use_timm_backbone = True. Supports any convolutional
backbone from the timm package. For a list of all available models, see this
page.
use_pretrained_backbone (bool, optional, defaults to True) —
Whether to use pretrained weights for the backbone. Only supported when use_timm_backbone = True.
dilation (bool, optional, defaults to False) —
Whether to replace stride with dilation in the last convolutional block (DC5). Only supported when
use_timm_backbone = True.
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.
mask_loss_coefficient (float, optional, defaults to 1) —
Relative weight of the Focal loss in the panoptic segmentation loss.
dice_loss_coefficient (float, optional, defaults to 1) —
Relative weight of the DICE/F-1 loss in the panoptic segmentation loss.
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.
focal_alpha (float, optional, defaults to 0.25) —
Alpha parameter in the focal loss.
This is the configuration class to store the configuration of a ConditionalDetrModel. It is used to instantiate
a Conditional DETR 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 Conditional DETR
microsoft/conditional-detr-resnet-50 architecture.
Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the
documentation from PretrainedConfig for more information.
Examples:
	Copied
 from transformers import ConditionalDetrConfig, ConditionalDetrModel
 # Initializing a Conditional DETR microsoft/conditional-detr-resnet-50 style configuration
 configuration = ConditionalDetrConfig()
 # Initializing a model (with random weights) from the microsoft/conditional-detr-resnet-50 style configuration
 model = ConditionalDetrModel(configuration)
 # Accessing the model configuration
 configuration = model.config
to_dict
<
source
>
(
)
→
Dict[str, any]
Returns
Dict[str, any]
Dictionary of all the attributes that make up this configuration instance,
Serializes this instance to a Python dictionary. Override the default to_dict().
ConditionalDetrImageProcessor
	
class transformers.ConditionalDetrImageProcessor
<
source
>
(
format: typing.Union[str, transformers.models.conditional_detr.image_processing_conditional_detr.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 Conditional 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.conditional_detr.image_processing_conditional_detr.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 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 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.
post_process_object_detection
<
source
>
(
outputs
threshold: float = 0.5
target_sizes: typing.Union[transformers.utils.generic.TensorType, typing.List[typing.Tuple]] = None
top_k: int = 100
)
→
List[Dict]
Parameters 
outputs (DetrObjectDetectionOutput) —
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 left to None, predictions will not be resized.
top_k (int, optional, defaults to 100) —
Keep only top k bounding boxes before filtering by thresholding.
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 ConditionalDetrForObjectDetection into final bounding boxes in (top_left_x,
top_left_y, bottom_right_x, bottom_right_y) format. Only supports PyTorch.
post_process_instance_segmentation
<
source
>
(
outputs
threshold: float = 0.5
mask_threshold: float = 0.5
overlap_mask_area_threshold: float = 0.8
target_sizes: typing.Union[typing.List[typing.Tuple[int, int]], NoneType] = None
return_coco_annotation: typing.Optional[bool] = False
)
→
List[Dict]
Parameters 
outputs (ConditionalDetrForSegmentation) —
Raw outputs of the model.
threshold (float, optional, defaults to 0.5) —
The probability score threshold to keep predicted instance masks.
mask_threshold (float, optional, defaults to 0.5) —
Threshold to use when turning the predicted masks into binary values.
overlap_mask_area_threshold (float, optional, defaults to 0.8) —
The overlap mask area threshold to merge or discard small disconnected parts within each binary
instance mask.
target_sizes (List[Tuple], optional) —
List of length (batch_size), where each list item (Tuple[int, int]]) corresponds to the requested
final size (height, width) of each prediction. If unset, predictions will not be resized.
return_coco_annotation (bool, optional) —
Defaults to False. If set to True, segmentation maps are returned in COCO run-length encoding (RLE)
format.
Returns
List[Dict]
A list of dictionaries, one per image, each dictionary containing two keys:
segmentation — A tensor of shape (height, width) where each pixel represents a segment_id or
List[List] run-length encoding (RLE) of the segmentation map if return_coco_annotation is set to
True. Set to None if no mask if found above threshold.
segments_info — A dictionary that contains additional information on each segment.
id — An integer representing the segment_id.
label_id — An integer representing the label / semantic class id corresponding to segment_id.
score — Prediction score of segment with segment_id.
Converts the output of ConditionalDetrForSegmentation into instance segmentation predictions. Only supports
PyTorch.
post_process_semantic_segmentation
<
source
>
(
outputs
target_sizes: typing.List[typing.Tuple[int, int]] = None
)
→
List[torch.Tensor]
Parameters 
outputs (ConditionalDetrForSegmentation) —
Raw outputs of the model.
target_sizes (List[Tuple[int, int]], optional) —
A 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[torch.Tensor]
A list of length batch_size, where each item is a semantic segmentation map of shape (height, width)
corresponding to the target_sizes entry (if target_sizes is specified). Each entry of each
torch.Tensor correspond to a semantic class id.
Converts the output of ConditionalDetrForSegmentation into semantic segmentation maps. Only supports
PyTorch.
post_process_panoptic_segmentation
<
source
>
(
outputs
threshold: float = 0.5
mask_threshold: float = 0.5
overlap_mask_area_threshold: float = 0.8
label_ids_to_fuse: typing.Optional[typing.Set[int]] = None
target_sizes: typing.Union[typing.List[typing.Tuple[int, int]], NoneType] = None
)
→
List[Dict]
Parameters 
outputs (ConditionalDetrForSegmentation) —
The outputs from ConditionalDetrForSegmentation.
threshold (float, optional, defaults to 0.5) —
The probability score threshold to keep predicted instance masks.
mask_threshold (float, optional, defaults to 0.5) —
Threshold to use when turning the predicted masks into binary values.
overlap_mask_area_threshold (float, optional, defaults to 0.8) —
The overlap mask area threshold to merge or discard small disconnected parts within each binary
instance mask.
label_ids_to_fuse (Set[int], optional) —
The labels in this state will have all their instances be fused together. For instance we could say
there can only be one sky in an image, but several persons, so the label ID for sky would be in that
set, but not the one for person.
target_sizes (List[Tuple], optional) —
List of length (batch_size), where each list item (Tuple[int, int]]) corresponds to the requested
final size (height, width) of each prediction in batch. If unset, predictions will not be resized.
Returns
List[Dict]
A list of dictionaries, one per image, each dictionary containing two keys:
segmentation — a tensor of shape (height, width) where each pixel represents a segment_id or
None if no mask if found above threshold. If target_sizes is specified, segmentation is resized to
the corresponding target_sizes entry.
segments_info — A dictionary that contains additional information on each segment.
id — an integer representing the segment_id.
label_id — An integer representing the label / semantic class id corresponding to segment_id.
was_fused — a boolean, True if label_id was in label_ids_to_fuse, False otherwise.
Multiple instances of the same class / label were fused and assigned a single segment_id.
score — Prediction score of segment with segment_id.
Converts the output of ConditionalDetrForSegmentation into image panoptic segmentation predictions. Only
supports PyTorch.
ConditionalDetrFeatureExtractor
	
class transformers.ConditionalDetrFeatureExtractor
<
source
>
(
*args
**kwargs
)
__call__
<
source
>
(
images
**kwargs
)
Preprocess an image or a batch of images.
post_process_object_detection
<
source
>
(
outputs
threshold: float = 0.5
target_sizes: typing.Union[transformers.utils.generic.TensorType, typing.List[typing.Tuple]] = None
top_k: int = 100
)
→
List[Dict]
Parameters 
outputs (DetrObjectDetectionOutput) —
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 left to None, predictions will not be resized.
top_k (int, optional, defaults to 100) —
Keep only top k bounding boxes before filtering by thresholding.
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 ConditionalDetrForObjectDetection into final bounding boxes in (top_left_x,
top_left_y, bottom_right_x, bottom_right_y) format. Only supports PyTorch.
post_process_instance_segmentation
<
source
>
(
outputs
threshold: float = 0.5
mask_threshold: float = 0.5
overlap_mask_area_threshold: float = 0.8
target_sizes: typing.Union[typing.List[typing.Tuple[int, int]], NoneType] = None
return_coco_annotation: typing.Optional[bool] = False
)
→
List[Dict]
Parameters 
outputs (ConditionalDetrForSegmentation) —
Raw outputs of the model.
threshold (float, optional, defaults to 0.5) —
The probability score threshold to keep predicted instance masks.
mask_threshold (float, optional, defaults to 0.5) —
Threshold to use when turning the predicted masks into binary values.
overlap_mask_area_threshold (float, optional, defaults to 0.8) —
The overlap mask area threshold to merge or discard small disconnected parts within each binary
instance mask.
target_sizes (List[Tuple], optional) —
List of length (batch_size), where each list item (Tuple[int, int]]) corresponds to the requested
final size (height, width) of each prediction. If unset, predictions will not be resized.
return_coco_annotation (bool, optional) —
Defaults to False. If set to True, segmentation maps are returned in COCO run-length encoding (RLE)
format.
Returns
List[Dict]
A list of dictionaries, one per image, each dictionary containing two keys:
segmentation — A tensor of shape (height, width) where each pixel represents a segment_id or
List[List] run-length encoding (RLE) of the segmentation map if return_coco_annotation is set to
True. Set to None if no mask if found above threshold.
segments_info — A dictionary that contains additional information on each segment.
id — An integer representing the segment_id.
label_id — An integer representing the label / semantic class id corresponding to segment_id.
score — Prediction score of segment with segment_id.
Converts the output of ConditionalDetrForSegmentation into instance segmentation predictions. Only supports
PyTorch.
post_process_semantic_segmentation
<
source
>
(
outputs
target_sizes: typing.List[typing.Tuple[int, int]] = None
)
→
List[torch.Tensor]
Parameters 
outputs (ConditionalDetrForSegmentation) —
Raw outputs of the model.
target_sizes (List[Tuple[int, int]], optional) —
A 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[torch.Tensor]
A list of length batch_size, where each item is a semantic segmentation map of shape (height, width)
corresponding to the target_sizes entry (if target_sizes is specified). Each entry of each
torch.Tensor correspond to a semantic class id.
Converts the output of ConditionalDetrForSegmentation into semantic segmentation maps. Only supports
PyTorch.
post_process_panoptic_segmentation
<
source
>
(
outputs
threshold: float = 0.5
mask_threshold: float = 0.5
overlap_mask_area_threshold: float = 0.8
label_ids_to_fuse: typing.Optional[typing.Set[int]] = None
target_sizes: typing.Union[typing.List[typing.Tuple[int, int]], NoneType] = None
)
→
List[Dict]
Parameters 
outputs (ConditionalDetrForSegmentation) —
The outputs from ConditionalDetrForSegmentation.
threshold (float, optional, defaults to 0.5) —
The probability score threshold to keep predicted instance masks.
mask_threshold (float, optional, defaults to 0.5) —
Threshold to use when turning the predicted masks into binary values.
overlap_mask_area_threshold (float, optional, defaults to 0.8) —
The overlap mask area threshold to merge or discard small disconnected parts within each binary
instance mask.
label_ids_to_fuse (Set[int], optional) —
The labels in this state will have all their instances be fused together. For instance we could say
there can only be one sky in an image, but several persons, so the label ID for sky would be in that
set, but not the one for person.
target_sizes (List[Tuple], optional) —
List of length (batch_size), where each list item (Tuple[int, int]]) corresponds to the requested
final size (height, width) of each prediction in batch. If unset, predictions will not be resized.
Returns
List[Dict]
A list of dictionaries, one per image, each dictionary containing two keys:
segmentation — a tensor of shape (height, width) where each pixel represents a segment_id or
None if no mask if found above threshold. If target_sizes is specified, segmentation is resized to
the corresponding target_sizes entry.
segments_info — A dictionary that contains additional information on each segment.
id — an integer representing the segment_id.
label_id — An integer representing the label / semantic class id corresponding to segment_id.
was_fused — a boolean, True if label_id was in label_ids_to_fuse, False otherwise.
Multiple instances of the same class / label were fused and assigned a single segment_id.
score — Prediction score of segment with segment_id.
Converts the output of ConditionalDetrForSegmentation into image panoptic segmentation predictions. Only
supports PyTorch.
ConditionalDetrModel
	
class transformers.ConditionalDetrModel
<
source
>
(
config: ConditionalDetrConfig
)
Parameters 
config (ConditionalDetrConfig) —
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 Conditional DETR Model (consisting of a backbone and encoder-decoder Transformer) outputting raw
hidden-states without any specific head on top.
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch torch.nn.Module subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
forward
<
source
>
(
pixel_values
pixel_mask = None
decoder_attention_mask = None
encoder_outputs = None
inputs_embeds = None
decoder_inputs_embeds = None
output_attentions = None
output_hidden_states = None
return_dict = None
)
→
transformers.models.conditional_detr.modeling_conditional_detr.ConditionalDetrModelOutput or tuple(torch.FloatTensor)
Parameters 
pixel_values (torch.FloatTensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Padding will be ignored by default should you provide it.
Pixel values can be obtained using AutoImageProcessor. See ConditionalDetrImageProcessor.call()
for details.
pixel_mask (torch.LongTensor 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?
decoder_attention_mask (torch.LongTensor of shape (batch_size, num_queries), optional) —
Not used by default. Can be used to mask object queries.
encoder_outputs (tuple(tuple(torch.FloatTensor), optional) —
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.
inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing the flattened feature map (output of the backbone + projection layer), you
can choose to directly pass a flattened representation of an image.
decoder_inputs_embeds (torch.FloatTensor of shape (batch_size, num_queries, hidden_size), optional) —
Optionally, instead of initializing the queries with a tensor of zeros, you can choose to directly pass an
embedded representation.
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.models.conditional_detr.modeling_conditional_detr.ConditionalDetrModelOutput or tuple(torch.FloatTensor)
A transformers.models.conditional_detr.modeling_conditional_detr.ConditionalDetrModelOutput 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 (ConditionalDetrConfig) 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 decoder of the model.
decoder_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 + one for the output of each layer) of
shape (batch_size, sequence_length, hidden_size). Hidden-states of the decoder at the output of each
layer plus the initial embedding outputs.
decoder_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 of the decoder, after the attention softmax, used to compute the
weighted average in the self-attention heads.
cross_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 of the decoder’s cross-attention layer, after the attention softmax,
used to compute the weighted average in the cross-attention heads.
encoder_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 encoder of the model.
encoder_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 + one for the output of each layer) of
shape (batch_size, sequence_length, hidden_size). Hidden-states of the encoder at the output of each
layer plus the initial embedding outputs.
encoder_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 of the encoder, after the attention softmax, used to compute the
weighted average in the self-attention heads.
intermediate_hidden_states (torch.FloatTensor of shape (config.decoder_layers, batch_size, sequence_length, hidden_size), optional, returned when config.auxiliary_loss=True) — Intermediate decoder activations, i.e. the output of each decoder layer, each of them gone through a
layernorm.
The ConditionalDetrModel 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, AutoModel
 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("microsoft/conditional-detr-resnet-50")
 model = AutoModel.from_pretrained("microsoft/conditional-detr-resnet-50")
 # prepare image for the model
 inputs = image_processor(images=image, return_tensors="pt")
 # forward pass
 outputs = model(**inputs)
 # the last hidden states are the final query embeddings of the Transformer decoder
 # these are of shape (batch_size, num_queries, hidden_size)
 last_hidden_states = outputs.last_hidden_state
 list(last_hidden_states.shape)
[1, 300, 256]
ConditionalDetrForObjectDetection
	
class transformers.ConditionalDetrForObjectDetection
<
source
>
(
config: ConditionalDetrConfig
)
Parameters 
config (ConditionalDetrConfig) —
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.
CONDITIONAL_DETR 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. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch torch.nn.Module subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
forward
<
source
>
(
pixel_values
pixel_mask = None
decoder_attention_mask = None
encoder_outputs = None
inputs_embeds = None
decoder_inputs_embeds = None
labels = None
output_attentions = None
output_hidden_states = None
return_dict = None
)
→
transformers.models.conditional_detr.modeling_conditional_detr.ConditionalDetrObjectDetectionOutput or tuple(torch.FloatTensor)
Parameters 
pixel_values (torch.FloatTensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Padding will be ignored by default should you provide it.
Pixel values can be obtained using AutoImageProcessor. See ConditionalDetrImageProcessor.call()
for details.
pixel_mask (torch.LongTensor 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?
decoder_attention_mask (torch.LongTensor of shape (batch_size, num_queries), optional) —
Not used by default. Can be used to mask object queries.
encoder_outputs (tuple(tuple(torch.FloatTensor), optional) —
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.
inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing the flattened feature map (output of the backbone + projection layer), you
can choose to directly pass a flattened representation of an image.
decoder_inputs_embeds (torch.FloatTensor of shape (batch_size, num_queries, hidden_size), optional) —
Optionally, instead of initializing the queries with a tensor of zeros, you can choose to directly pass an
embedded representation.
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.conditional_detr.modeling_conditional_detr.ConditionalDetrObjectDetectionOutput or tuple(torch.FloatTensor)
A transformers.models.conditional_detr.modeling_conditional_detr.ConditionalDetrObjectDetectionOutput 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 (ConditionalDetrConfig) 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_object_detection() 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.
decoder_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 + one for the output of each layer) of
shape (batch_size, sequence_length, hidden_size). Hidden-states of the decoder at the output of each
layer plus the initial embedding outputs.
decoder_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 of the decoder, after the attention softmax, used to compute the
weighted average in the self-attention heads.
cross_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 of the decoder’s cross-attention layer, after the attention softmax,
used to compute the weighted average in the cross-attention heads.
encoder_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 encoder of the model.
encoder_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 + one for the output of each layer) of
shape (batch_size, sequence_length, hidden_size). Hidden-states of the encoder at the output of each
layer plus the initial embedding outputs.
encoder_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 of the encoder, after the attention softmax, used to compute the
weighted average in the self-attention heads.
The ConditionalDetrForObjectDetection 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
 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("microsoft/conditional-detr-resnet-50")
 model = AutoModelForObjectDetection.from_pretrained("microsoft/conditional-detr-resnet-50")
 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=0.5, 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 0.833 at location [38.31, 72.1, 177.63, 118.45]
Detected cat with confidence 0.831 at location [9.2, 51.38, 321.13, 469.0]
Detected cat with confidence 0.804 at location [340.3, 16.85, 642.93, 370.95]
Detected remote with confidence 0.683 at location [334.48, 73.49, 366.37, 190.01]
Detected couch with confidence 0.535 at location [0.52, 1.19, 640.35, 475.1]
ConditionalDetrForSegmentation
	
class transformers.ConditionalDetrForSegmentation
<
source
>
(
config: ConditionalDetrConfig
)
Parameters 
config (ConditionalDetrConfig) —
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.
CONDITIONAL_DETR Model (consisting of a backbone and encoder-decoder Transformer) with a segmentation head on top,
for tasks such as COCO panoptic.
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch torch.nn.Module subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
forward
<
source
>
(
pixel_values
pixel_mask = None
decoder_attention_mask = None
encoder_outputs = None
inputs_embeds = None
decoder_inputs_embeds = None
labels = None
output_attentions = None
output_hidden_states = None
return_dict = None
)
→
transformers.models.conditional_detr.modeling_conditional_detr.ConditionalDetrSegmentationOutput or tuple(torch.FloatTensor)
Parameters 
pixel_values (torch.FloatTensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Padding will be ignored by default should you provide it.
Pixel values can be obtained using AutoImageProcessor. See ConditionalDetrImageProcessor.call()
for details.
pixel_mask (torch.LongTensor 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?
decoder_attention_mask (torch.LongTensor of shape (batch_size, num_queries), optional) —
Not used by default. Can be used to mask object queries.
encoder_outputs (tuple(tuple(torch.FloatTensor), optional) —
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.
inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing the flattened feature map (output of the backbone + projection layer), you
can choose to directly pass a flattened representation of an image.
decoder_inputs_embeds (torch.FloatTensor of shape (batch_size, num_queries, hidden_size), optional) —
Optionally, instead of initializing the queries with a tensor of zeros, you can choose to directly pass an
embedded representation.
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, DICE/F-1 loss and Focal loss. List of dicts, each
dictionary containing at least the following 3 keys: ‘class_labels’, ‘boxes’ and ‘masks’ (the class labels,
bounding boxes and segmentation masks 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,), the boxes a
torch.FloatTensor of shape (number of bounding boxes in the image, 4) and the masks a
torch.FloatTensor of shape (number of bounding boxes in the image, height, width).
Returns
transformers.models.conditional_detr.modeling_conditional_detr.ConditionalDetrSegmentationOutput or tuple(torch.FloatTensor)
A transformers.models.conditional_detr.modeling_conditional_detr.ConditionalDetrSegmentationOutput 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 (ConditionalDetrConfig) 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_object_detection() to retrieve
the unnormalized bounding boxes.
pred_masks (torch.FloatTensor of shape (batch_size, num_queries, height/4, width/4)) — Segmentation masks logits for all queries. See also
post_process_semantic_segmentation() or
post_process_instance_segmentation()
post_process_panoptic_segmentation() to evaluate semantic, instance and
panoptic segmentation masks respectively.
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.
decoder_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 + one for the output of each layer) of
shape (batch_size, sequence_length, hidden_size). Hidden-states of the decoder at the output of each
layer plus the initial embedding outputs.
decoder_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 of the decoder, after the attention softmax, used to compute the
weighted average in the self-attention heads.
cross_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 of the decoder’s cross-attention layer, after the attention softmax,
used to compute the weighted average in the cross-attention heads.
encoder_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 encoder of the model.
encoder_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 + one for the output of each layer) of
shape (batch_size, sequence_length, hidden_size). Hidden-states of the encoder at the output of each
layer plus the initial embedding outputs.
encoder_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 of the encoder, after the attention softmax, used to compute the
weighted average in the self-attention heads.
The ConditionalDetrForSegmentation 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
 import io
 import requests
 from PIL import Image
 import torch
 import numpy
 from transformers import (
...     AutoImageProcessor,
...     ConditionalDetrConfig,
...     ConditionalDetrForSegmentation,
... )
 from transformers.image_transforms import rgb_to_id
 url = "http://images.cocodataset.org/val2017/000000039769.jpg"
 image = Image.open(requests.get(url, stream=True).raw)
 image_processor = AutoImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50")
 # randomly initialize all weights of the model
 config = ConditionalDetrConfig()
 model = ConditionalDetrForSegmentation(config)
 # prepare image for the model
 inputs = image_processor(images=image, return_tensors="pt")
 # forward pass
 outputs = model(**inputs)
 # Use the `post_process_panoptic_segmentation` method of the `image_processor` to retrieve post-processed panoptic segmentation maps
 # Segmentation results are returned as a list of dictionaries
 result = image_processor.post_process_panoptic_segmentation(outputs, target_sizes=[(300, 500)])
 # A tensor of shape (height, width) where each value denotes a segment id, filled with -1 if no segment is found
 panoptic_seg = result[0]["segmentation"]
 # Get prediction score and segment_id to class_id mapping of each segment
 panoptic_segments_info = result[0]["segments_info"]
←BiT
ConvNeXT→
Conditional DETR
Overview
Documentation resources
ConditionalDetrConfig
ConditionalDetrImageProcessor
ConditionalDetrFeatureExtractor
ConditionalDetrModel
ConditionalDetrForObjectDetection
ConditionalDetrForSegmentation
