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tags: |
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- object-detection |
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--- |
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# Model Card for detr-doc-table-detection |
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# Model Details |
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## Model Description |
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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). |
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- **Developed by:** Taha Douaji |
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- **Shared by [Optional]:** Taha Douaji |
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- **Model type:** Object Detection |
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- **Language(s) (NLP):** More information needed |
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- **License:** More information needed |
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- **Parent Model:** [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) |
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- **Resources for more information:** |
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- [Model Demo Space](https://huggingface.co/spaces/trevbeers/pdf-table-extraction) |
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- [Associated Paper](https://arxiv.org/abs/2005.12872) |
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# Uses |
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## Direct Use |
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This model can be used for the task of object detection. |
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## Downstream Use [Optional] |
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More information needed. |
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## Out-of-Scope Use |
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The model should not be used to intentionally create hostile or alienating environments for people. |
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# Bias, Risks, and Limitations |
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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. |
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## Recommendations |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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# Training Details |
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## Training Data |
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The model was trained on ICDAR2019 Table Dataset |
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## Training Procedure |
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### Preprocessing |
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More information needed |
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### Speeds, Sizes, Times |
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More information needed |
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# Evaluation |
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## Testing Data, Factors & Metrics |
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### Testing Data |
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More information needed |
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### Factors |
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More information needed |
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### Metrics |
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More information needed |
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## Results |
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More information needed |
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# Model Examination |
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More information needed |
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# Environmental Impact |
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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). |
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- **Hardware Type:** More information needed |
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- **Hours used:** More information needed |
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- **Cloud Provider:** More information needed |
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- **Compute Region:** More information needed |
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- **Carbon Emitted:** More information needed |
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# Technical Specifications [optional] |
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## Model Architecture and Objective |
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More information needed |
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## Compute Infrastructure |
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More information needed |
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### Hardware |
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More information needed |
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### Software |
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More information needed. |
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# Citation |
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**BibTeX:** |
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```bibtex |
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@article{DBLP:journals/corr/abs-2005-12872, |
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author = {Nicolas Carion and |
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Francisco Massa and |
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Gabriel Synnaeve and |
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Nicolas Usunier and |
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Alexander Kirillov and |
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Sergey Zagoruyko}, |
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title = {End-to-End Object Detection with Transformers}, |
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journal = {CoRR}, |
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volume = {abs/2005.12872}, |
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year = {2020}, |
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url = {https://arxiv.org/abs/2005.12872}, |
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archivePrefix = {arXiv}, |
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eprint = {2005.12872}, |
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timestamp = {Thu, 28 May 2020 17:38:09 +0200}, |
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biburl = {https://dblp.org/rec/journals/corr/abs-2005-12872.bib}, |
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bibsource = {dblp computer science bibliography, https://dblp.org} |
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} |
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``` |
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# Glossary [optional] |
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More information needed |
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# More Information [optional] |
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More information needed |
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# Model Card Authors [optional] |
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Taha Douaji in collaboration with Ezi Ozoani and the Hugging Face team |
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# Model Card Contact |
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More information needed |
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# How to Get Started with the Model |
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Use the code below to get started with the model. |
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<details> |
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<summary> Click to expand </summary> |
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```python |
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from transformers import DetrImageProcessor, DetrForObjectDetection |
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import torch |
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from PIL import Image |
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import requests |
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url = "http://images.cocodataset.org/val2017/000000039769.jpg" |
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image = Image.open(requests.get(url, stream=True).raw) |
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processor = DetrImageProcessor.from_pretrained("TahaDouaji/detr-doc-table-detection") |
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model = DetrForObjectDetection.from_pretrained("TahaDouaji/detr-doc-table-detection") |
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inputs = processor(images=image, return_tensors="pt") |
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outputs = model(**inputs) |
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# convert outputs (bounding boxes and class logits) to COCO API |
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# let's only keep detections with score > 0.9 |
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target_sizes = torch.tensor([image.size[::-1]]) |
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results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0] |
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for score, label, box in zip(results["scores"], results["labels"], results["boxes"]): |
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box = [round(i, 2) for i in box.tolist()] |
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print( |
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f"Detected {model.config.id2label[label.item()]} with confidence " |
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f"{round(score.item(), 3)} at location {box}" |
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) |
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``` |
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</details> |
