Instructions to use cmarkea/dit-base-layout-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cmarkea/dit-base-layout-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="cmarkea/dit-base-layout-detection")# Load model directly from transformers import AutoImageProcessor, BeitForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("cmarkea/dit-base-layout-detection") model = BeitForSemanticSegmentation.from_pretrained("cmarkea/dit-base-layout-detection") - Inference
- Notebooks
- Google Colab
- Kaggle
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README.md
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bbx = detect_bboxes(mm.numpy())
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boxes.extend(bbx)
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labels.extend([ii]*len(bbx))
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bbox_pred.append(dict(boxes=boxes, labels=
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```
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### Example
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bbx = detect_bboxes(mm.numpy())
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boxes.extend(bbx)
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labels.extend([ii]*len(bbx))
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bbox_pred.append(dict(boxes=boxes, labels=labels))
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```
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### Example
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