206 lines
5.0 KiB
Markdown
206 lines
5.0 KiB
Markdown
---
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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> |