timm

README.md

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+---
+license: mit
+widget:
+- src: https://www.invoicesimple.com/wp-content/uploads/2018/06/Sample-Invoice-printable.png
+ example_title: Invoice
+---
+
+# Table Transformer (fine-tuned for Table Detection) 
+
+Table Transformer (DETR) model trained on PubTables1M. It was introduced in the paper [PubTables-1M: Towards Comprehensive Table Extraction From Unstructured Documents](https://arxiv.org/abs/2110.00061) by Smock et al. and first released in [this repository](https://github.com/microsoft/table-transformer). 
+
+Disclaimer: The team releasing Table Transformer did not write a model card for this model so this model card has been written by the Hugging Face team.
+
+## Model description
+
+The Table Transformer is equivalent to [DETR](https://huggingface.co/docs/transformers/model_doc/detr), a Transformer-based object detection model. Note that the authors decided to use the "normalize before" setting of DETR, which means that layernorm is applied before self- and cross-attention.
+
+## Usage
+
+You can use the raw model for detecting tables in documents. See the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/table-transformer) for more info.

config.json

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+{
+ "activation_dropout": 0.0,
+ "activation_function": "relu",
+ "architectures": [
+ "TableTransformerForObjectDetection"
+ ],
+ "attention_dropout": 0.0,
+ "auxiliary_loss": false,
+ "backbone": "resnet18",
+ "bbox_cost": 5,
+ "bbox_loss_coefficient": 5,
+ "ce_loss_coefficient": 1,
+ "class_cost": 1,
+ "d_model": 256,
+ "decoder_attention_heads": 8,
+ "decoder_ffn_dim": 2048,
+ "decoder_layerdrop": 0.0,
+ "decoder_layers": 6,
+ "dice_loss_coefficient": 1,
+ "dilation": false,
+ "dropout": 0.1,
+ "encoder_attention_heads": 8,
+ "encoder_ffn_dim": 2048,
+ "encoder_layerdrop": 0.0,
+ "encoder_layers": 6,
+ "eos_coefficient": 0.4,
+ "giou_cost": 2,
+ "giou_loss_coefficient": 2,
+ "id2label": {
+ "0": "table",
+ "1": "table rotated"
+ },
+ "init_std": 0.02,
+ "init_xavier_std": 1.0,
+ "is_encoder_decoder": true,
+ "label2id": {
+ "table": 0,
+ "table rotated": 1
+ },
+ "mask_loss_coefficient": 1,
+ "max_position_embeddings": 1024,
+ "model_type": "table-transformer",
+ "num_channels": 3,
+ "num_hidden_layers": 6,
+ "num_queries": 15,
+ "position_embedding_type": "sine",
+ "scale_embedding": false,
+ "torch_dtype": "float32",
+ "transformers_version": "4.24.0.dev0",
+ "use_pretrained_backbone": true
+}

preprocessor_config.json

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+{
+ "do_normalize": true,
+ "do_resize": true,
+ "feature_extractor_type": "DetrFeatureExtractor",
+ "format": "coco_detection",
+ "image_mean": [
+ 0.485,
+ 0.456,
+ 0.406
+ ],
+ "image_std": [
+ 0.229,
+ 0.224,
+ 0.225
+ ],
+ "max_size": 800,
+ "size": 800
+}

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1d9babb11711211dd9fea2f477b7ba11f3656623505b07f9e30bfd41f143a9c7
+size 115393245

requirements.txt

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+timm