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---
tags:
- object-detection
- '- vision'
license: apache-2.0
base_model: facebook/detr-resnet-50
datasets:
- MohamedExperio/ICDAR2019
---
# 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](https://huggingface.co/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](https://huggingface.co/facebook/detr-resnet-50)
- **Resources for more information:**
- [Model Demo Space](https://huggingface.co/spaces/trevbeers/pdf-table-extraction)
- [Associated Paper](https://arxiv.org/abs/2005.12872)
# 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)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). 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](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
# Citation
**BibTeX:**
```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 Hugging Face team
# Model Card Contact
More information needed
# How to Get Started with the Model
Use the code below to get started with the model.
```python
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}"
)
``` |