Model Card for detr-doc-table-detection

Model Details

detr-doc-table-detection is a model trained to detect both Bordered and Borderless tables in documents, based on facebook/detr-resnet-50.

• Developed by: Taha Douaji
• Shared by [Optional]: Taha Douaji
• Model type: Object Detection
• Language(s) (NLP): More information needed
• License: More information needed
• Parent Model: facebook/detr-resnet-50
• Resources for more information:
  • Model Demo Space
  • Associated Paper

Uses

Direct Use

This model can be used for the task of object detection.

Out-of-Scope Use

The model should not be used to intentionally create hostile or alienating environments for people.

Bias, Risks, and Limitations

Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

Training Details

Training Data

The model was trained on ICDAR2019 Table Dataset

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Citation

BibTeX:

@article{DBLP:journals/corr/abs-2005-12872,
  author    = {Nicolas Carion and
               Francisco Massa and
               Gabriel Synnaeve and
               Nicolas Usunier and
               Alexander Kirillov and
               Sergey Zagoruyko},
  title     = {End-to-End Object Detection with Transformers},
  journal   = {CoRR},
  volume    = {abs/2005.12872},
  year      = {2020},
  url       = {https://arxiv.org/abs/2005.12872},
  archivePrefix = {arXiv},
  eprint    = {2005.12872},
  timestamp = {Thu, 28 May 2020 17:38:09 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2005-12872.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Model Card Authors [optional]

Taha Douaji in collaboration with Ezi Ozoani and the Model Database team

Model Card Contact

More information needed

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import DetrImageProcessor, DetrForObjectDetection
import torch
from PIL import Image
import requests

image = Image.open("IMAGE_PATH")

processor = DetrImageProcessor.from_pretrained("TahaDouaji/detr-doc-table-detection")
model = DetrForObjectDetection.from_pretrained("TahaDouaji/detr-doc-table-detection")

inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)

# convert outputs (bounding boxes and class logits) to COCO API
# let's only keep detections with score > 0.9
target_sizes = torch.tensor([image.size[::-1]])
results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[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}"
    )