# Table Transformer ## Overview The Table Transformer model was proposed in [PubTables-1M: Towards comprehensive table extraction from unstructured documents](https://huggingface.co/papers/2110.00061) by Brandon Smock, Rohith Pesala, Robin Abraham. The authors introduce a new dataset, PubTables-1M, to benchmark progress in table extraction from unstructured documents, as well as table structure recognition and functional analysis. The authors train 2 [DETR](detr) models, one for table detection and one for table structure recognition, dubbed Table Transformers. The abstract from the paper is the following: *Recently, significant progress has been made applying machine learning to the problem of table structure inference and extraction from unstructured documents. However, one of the greatest challenges remains the creation of datasets with complete, unambiguous ground truth at scale. To address this, we develop a new, more comprehensive dataset for table extraction, called PubTables-1M. PubTables-1M contains nearly one million tables from scientific articles, supports multiple input modalities, and contains detailed header and location information for table structures, making it useful for a wide variety of modeling approaches. It also addresses a significant source of ground truth inconsistency observed in prior datasets called oversegmentation, using a novel canonicalization procedure. We demonstrate that these improvements lead to a significant increase in training performance and a more reliable estimate of model performance at evaluation for table structure recognition. Further, we show that transformer-based object detection models trained on PubTables-1M produce excellent results for all three tasks of detection, structure recognition, and functional analysis without the need for any special customization for these tasks.* drawing Table detection and table structure recognition clarified. Taken from the original paper. The authors released 2 models, one for [table detection](https://huggingface.co/microsoft/table-transformer-detection) in documents, one for [table structure recognition](https://huggingface.co/microsoft/table-transformer-structure-recognition) (the task of recognizing the individual rows, columns etc. in a table). This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/microsoft/table-transformer). ## Resources - A demo notebook for the Table Transformer can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/Table%20Transformer). - It turns out padding of images is quite important for detection. An interesting Github thread with replies from the authors can be found [here](https://github.com/microsoft/table-transformer/issues/68). ## TableTransformerConfig[[transformers.TableTransformerConfig]] #### transformers.TableTransformerConfig[[transformers.TableTransformerConfig]] [Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/table_transformer/configuration_table_transformer.py#L26) This is the configuration class to store the configuration of a Table TransformerModel. It is used to instantiate a Table Transformer 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 [microsoft/table-transformer-detection](https://huggingface.co/microsoft/table-transformer-detection) 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. Examples: ```python >>> from transformers import TableTransformerModel, TableTransformerConfig >>> # Initializing a Table Transformer microsoft/table-transformer-detection style configuration >>> configuration = TableTransformerConfig() >>> # Initializing a model from the microsoft/table-transformer-detection style configuration >>> model = TableTransformerModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` **Parameters:** is_encoder_decoder (`bool`, *optional*, defaults to `True`) : Whether the model is used as an encoder/decoder or not. backbone_config (`Union[dict, ~configuration_utils.PreTrainedConfig]`, *optional*) : The configuration of the backbone model. 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 [TableTransformerModel](/docs/transformers/main/en/model_doc/table-transformer#transformers.TableTransformerModel) can detect in a single image. For COCO, we recommend 100 queries. encoder_layers (`int`, *optional*, defaults to `6`) : Number of hidden layers in the Transformer encoder. Will use the same value as `num_layers` if not set. encoder_ffn_dim (`int`, *optional*, defaults to `2048`) : Dimensionality of the "intermediate" (often named feed-forward) layer in encoder. encoder_attention_heads (`int`, *optional*, defaults to `8`) : Number of attention heads for each attention layer in the Transformer encoder. decoder_layers (`int`, *optional*, defaults to `6`) : Number of hidden layers in the Transformer decoder. Will use the same value as `num_layers` if not set. decoder_ffn_dim (`int`, *optional*, defaults to `2048`) : Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. decoder_attention_heads (`int`, *optional*, defaults to `8`) : Number of attention heads for each attention layer in the Transformer decoder. encoder_layerdrop (`Union[float, int]`, *optional*, defaults to `0.0`) : The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://huggingface.co/papers/1909.11556) for more details. decoder_layerdrop (`Union[float, int]`, *optional*, defaults to `0.0`) : The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://huggingface.co/papers/1909.11556) for more details. activation_function (`str`, *optional*, defaults to `relu`) : The non-linear activation function (function or string) in the decoder. For example, `"gelu"`, `"relu"`, `"silu"`, etc. d_model (`int`, *optional*, defaults to `256`) : Size of the encoder layers and the pooler layer. dropout (`Union[float, int]`, *optional*, defaults to `0.1`) : The ratio for all dropout layers. attention_dropout (`Union[float, int]`, *optional*, defaults to `0.0`) : The dropout ratio for the attention probabilities. activation_dropout (`Union[float, int]`, *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 `1.0`) : The scaling factor used for the Xavier initialization of the cross-attention weights. 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"`. 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 (`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. mask_loss_coefficient (`int`, *optional*, defaults to `1`) : Relative weight of the focal loss in the panoptic segmentation loss. dice_loss_coefficient (`int`, *optional*, defaults to `1`) : Relative weight of the dice loss in the panoptic segmentation loss. 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. ## TableTransformerModel[[transformers.TableTransformerModel]] #### transformers.TableTransformerModel[[transformers.TableTransformerModel]] [Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/table_transformer/modeling_table_transformer.py#L982) The bare Table Transformer Model (consisting of a backbone and encoder-decoder Transformer) outputting raw hidden-states without any specific head on top. This model inherits from [PreTrainedModel](/docs/transformers/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.TableTransformerModel.forwardhttps://github.com/huggingface/transformers/blob/main/src/transformers/models/table_transformer/modeling_table_transformer.py#L1011[{"name": "pixel_values", "val": ": FloatTensor"}, {"name": "pixel_mask", "val": ": torch.FloatTensor | None = None"}, {"name": "decoder_attention_mask", "val": ": torch.FloatTensor | None = None"}, {"name": "encoder_outputs", "val": ": torch.FloatTensor | None = None"}, {"name": "inputs_embeds", "val": ": torch.FloatTensor | None = None"}, {"name": "decoder_inputs_embeds", "val": ": torch.FloatTensor | None = None"}, {"name": "output_attentions", "val": ": bool | None = None"}, {"name": "output_hidden_states", "val": ": bool | None = None"}, {"name": "return_dict", "val": ": bool | None = None"}, {"name": "**kwargs", "val": ""}]- **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 [DetrImageProcessor](/docs/transformers/main/en/model_doc/detr#transformers.DetrImageProcessor). See `DetrImageProcessor.__call__()` for details (`processor_class` uses [DetrImageProcessor](/docs/transformers/main/en/model_doc/detr#transformers.DetrImageProcessor) for processing images). - **pixel_mask** (`torch.FloatTensor` of shape `(batch_size, height, width)`, *optional*) -- Mask to avoid performing attention on padding pixel values. Mask values selected in `[0, 1]`: - 1 for pixels that are real (i.e. **not masked**), - 0 for pixels that are padding (i.e. **masked**). [What are attention masks?](../glossary#attention-mask) - **decoder_attention_mask** (`torch.FloatTensor` of shape `(batch_size, num_queries)`, *optional*) -- Not used by default. Can be used to mask object queries. - **encoder_outputs** (`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](/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple.0`TableTransformerModelOutput` or `tuple(torch.FloatTensor)`A `TableTransformerModelOutput` 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 ([TableTransformerConfig](/docs/transformers/main/en/model_doc/table-transformer#transformers.TableTransformerConfig)) and inputs. The [TableTransformerModel](/docs/transformers/main/en/model_doc/table-transformer#transformers.TableTransformerModel) 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 decoder of the model. - **past_key_values** (`~cache_utils.EncoderDecoderCache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- It is a [Cache](/docs/transformers/main/en/internal/generation_utils#transformers.Cache) instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if `config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. - **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, 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 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, 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 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. Examples: ```python >>> from transformers import AutoImageProcessor, TableTransformerModel >>> from huggingface_hub import hf_hub_download >>> from PIL import Image >>> file_path = hf_hub_download(repo_id="nielsr/example-pdf", repo_type="dataset", filename="example_pdf.png") >>> image = Image.open(file_path).convert("RGB") >>> image_processor = AutoImageProcessor.from_pretrained("microsoft/table-transformer-detection") >>> model = TableTransformerModel.from_pretrained("microsoft/table-transformer-detection") >>> # 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, 15, 256] ``` **Parameters:** config ([TableTransformerConfig](/docs/transformers/main/en/model_doc/table-transformer#transformers.TableTransformerConfig)) : 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:** ``TableTransformerModelOutput` or `tuple(torch.FloatTensor)`` A `TableTransformerModelOutput` 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 ([TableTransformerConfig](/docs/transformers/main/en/model_doc/table-transformer#transformers.TableTransformerConfig)) and inputs. ## TableTransformerForObjectDetection[[transformers.TableTransformerForObjectDetection]] #### transformers.TableTransformerForObjectDetection[[transformers.TableTransformerForObjectDetection]] [Source](https://github.com/huggingface/transformers/blob/main/src/transformers/models/table_transformer/modeling_table_transformer.py#L1151) Table Transformer Model (consisting of a backbone and encoder-decoder Transformer) with object detection heads on top, for tasks such as COCO detection. This model inherits from [PreTrainedModel](/docs/transformers/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.TableTransformerForObjectDetection.forwardhttps://github.com/huggingface/transformers/blob/main/src/transformers/models/table_transformer/modeling_table_transformer.py#L1170[{"name": "pixel_values", "val": ": FloatTensor"}, {"name": "pixel_mask", "val": ": torch.FloatTensor | None = None"}, {"name": "decoder_attention_mask", "val": ": torch.FloatTensor | None = None"}, {"name": "encoder_outputs", "val": ": torch.FloatTensor | None = None"}, {"name": "inputs_embeds", "val": ": torch.FloatTensor | None = None"}, {"name": "decoder_inputs_embeds", "val": ": torch.FloatTensor | None = None"}, {"name": "labels", "val": ": list[dict] | None = None"}, {"name": "output_attentions", "val": ": bool | None = None"}, {"name": "output_hidden_states", "val": ": bool | None = None"}, {"name": "return_dict", "val": ": bool | None = None"}, {"name": "**kwargs", "val": ""}]- **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 [DetrImageProcessor](/docs/transformers/main/en/model_doc/detr#transformers.DetrImageProcessor). See `DetrImageProcessor.__call__()` for details (`processor_class` uses [DetrImageProcessor](/docs/transformers/main/en/model_doc/detr#transformers.DetrImageProcessor) for processing images). - **pixel_mask** (`torch.FloatTensor` of shape `(batch_size, height, width)`, *optional*) -- Mask to avoid performing attention on padding pixel values. Mask values selected in `[0, 1]`: - 1 for pixels that are real (i.e. **not masked**), - 0 for pixels that are padding (i.e. **masked**). [What are attention masks?](../glossary#attention-mask) - **decoder_attention_mask** (`torch.FloatTensor` of shape `(batch_size, num_queries)`, *optional*) -- Not used by default. Can be used to mask object queries. - **encoder_outputs** (`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. - **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)`. - **output_attentions** (`bool`, *optional*) -- Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. - **output_hidden_states** (`bool`, *optional*) -- Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. - **return_dict** (`bool`, *optional*) -- Whether or not to return a [ModelOutput](/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple.0`TableTransformerObjectDetectionOutput` or `tuple(torch.FloatTensor)`A `TableTransformerObjectDetectionOutput` 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 ([TableTransformerConfig](/docs/transformers/main/en/model_doc/table-transformer#transformers.TableTransformerConfig)) and inputs. The [TableTransformerForObjectDetection](/docs/transformers/main/en/model_doc/table-transformer#transformers.TableTransformerForObjectDetection) 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 `~TableTransformerImageProcessor.post_process_object_detection` 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. - **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, 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 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*, defaults to `None`) -- 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, 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 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. Examples: ```python >>> from huggingface_hub import hf_hub_download >>> from transformers import AutoImageProcessor, TableTransformerForObjectDetection >>> import torch >>> from PIL import Image >>> file_path = hf_hub_download(repo_id="nielsr/example-pdf", repo_type="dataset", filename="example_pdf.png") >>> image = Image.open(file_path).convert("RGB") >>> image_processor = AutoImageProcessor.from_pretrained("microsoft/table-transformer-detection") >>> model = TableTransformerForObjectDetection.from_pretrained("microsoft/table-transformer-detection") >>> 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 table with confidence 1.0 at location [202.1, 210.59, 1119.22, 385.09] ``` **Parameters:** config ([TableTransformerConfig](/docs/transformers/main/en/model_doc/table-transformer#transformers.TableTransformerConfig)) : 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:** ``TableTransformerObjectDetectionOutput` or `tuple(torch.FloatTensor)`` A `TableTransformerObjectDetectionOutput` 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 ([TableTransformerConfig](/docs/transformers/main/en/model_doc/table-transformer#transformers.TableTransformerConfig)) and inputs.