Instructions to use aParadigmP/table-transformer-detection-custom-ale with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aParadigmP/table-transformer-detection-custom-ale with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="aParadigmP/table-transformer-detection-custom-ale")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("aParadigmP/table-transformer-detection-custom-ale") model = AutoModelForObjectDetection.from_pretrained("aParadigmP/table-transformer-detection-custom-ale", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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[Clean-up] Planned removal of the `max_size` argument
Verified signature- Replace deprecated `"feature_extractor_type": "DetrFeatureExtractor"` with `"image_processor_type": "DetrImageProcessor"` - Remove deprecated `max_size` and replace `size` with a `Dict`75b3e49
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Duplicate from microsoft/table-transformer-detection
Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>4879326