
 
Table Transformer
	
Overview
	
The Table Transformer model was proposed in PubTables-1M: Towards comprehensive table extraction from unstructured documents 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 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.
Tips:
The authors released 2 models, one for table detection in documents, one for table structure recognition (the task of recognizing the individual rows, columns etc. in a table).
One can use the AutoImageProcessor API to prepare images and optional targets for the model. This will load a DetrImageProcessor behind the scenes.
Table detection and table structure recognition clarified. Taken from the original paper. 
This model was contributed by nielsr. The original code can be
found here.
Resources
	
Object Detection
A demo notebook for the Table Transformer can be found here.
It turns out padding of images is quite important for detection. An interesting Github thread with replies from the authors can be found here.
TableTransformerConfig
	
class transformers.TableTransformerConfig
<
source
>
(
use_timm_backbone = True
backbone_config = None
num_channels = 3
num_queries = 100
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 = 1
bbox_cost = 5
giou_cost = 2
mask_loss_coefficient = 1
dice_loss_coefficient = 1
bbox_loss_coefficient = 5
giou_loss_coefficient = 2
eos_coefficient = 0.1
**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
TableTransformerModel 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.
This is the configuration class to store the configuration of a TableTransformerModel. 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 Table Transformer
microsoft/table-transformer-detection 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 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
TableTransformerModel
	
class transformers.TableTransformerModel
<
source
>
(
config: TableTransformerConfig
)
Parameters 
config (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() method to load the model weights.
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. 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.table_transformer.modeling_table_transformer.TableTransformerModelOutput 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 DetrImageProcessor. See DetrImageProcessor.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.table_transformer.modeling_table_transformer.TableTransformerModelOutput or tuple(torch.FloatTensor)
A transformers.models.table_transformer.modeling_table_transformer.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) 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 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.
Examples:
	Copied
 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]
TableTransformerForObjectDetection
	
class transformers.TableTransformerForObjectDetection
<
source
>
(
config: TableTransformerConfig
)
Parameters 
config (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() method to load the model weights.
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. 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.table_transformer.modeling_table_transformer.TableTransformerObjectDetectionOutput 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 DetrImageProcessor. See DetrImageProcessor.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.table_transformer.modeling_table_transformer.TableTransformerObjectDetectionOutput or tuple(torch.FloatTensor)
A transformers.models.table_transformer.modeling_table_transformer.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) 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 ~TableTransformerImageProcessor.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 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.
Examples:
	Copied
 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 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 table with confidence 1.0 at location [202.1, 210.59, 1119.22, 385.09]
←Swin2SR
TimeSformer→
Table Transformer
Overview
Resources
TableTransformerConfig
TableTransformerModel
TableTransformerForObjectDetection
