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--- |
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library_name: transformers |
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tags: [] |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. |
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- **Developed by:** [More Information Needed] |
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- **Funded by [optional]:** [More Information Needed] |
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- **Shared by [optional]:** [More Information Needed] |
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- **Model type:** [More Information Needed] |
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- **Language(s) (NLP):** [More Information Needed] |
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- **License:** [More Information Needed] |
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- **Finetuned from model [optional]:** [More Information Needed] |
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### Model Sources [optional] |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** [More Information Needed] |
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- **Paper [optional]:** [More Information Needed] |
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- **Demo [optional]:** [More Information Needed] |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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### Direct Use |
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```python |
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import torch |
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from transformers import AutoImageProcessor, AutoModel |
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img_proc = AutoImageProcessor.from_pretrained( |
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"ArkeaIAF/dit-base-layout-detection" |
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) |
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model = AutoModel.from_pretrained( |
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"ArkeaIAF/dit-base-layout-detection" |
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) |
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with torch.inference_mode(): |
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input_ids = img_proc(img, return_tensors='pt') |
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segmentation = model(**input_ids) |
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segmentation_mask = img_proc.post_process_semantic_segmentation( |
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segmentation, |
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target_sizes=[img.size[::-1]] |
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) |
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``` |
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