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--- |
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tags: |
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- object-detection |
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--- |
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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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## Training data |
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The model was trained on ICDAR2019 Table Dataset |
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### How to use |
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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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``` |