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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from transformers import AutoImageProcessor, AutoModel
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img_proc = AutoImageProcessor.from_pretrained(
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model = AutoModel.from_pretrained(
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with torch.inference_mode():
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from transformers import AutoImageProcessor, AutoModel
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img_proc = AutoImageProcessor.from_pretrained(
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"cmarkea/dit-base-layout-detection"
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model = AutoModel.from_pretrained(
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"cmarkea/dit-base-layout-detection"
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with torch.inference_mode():
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