Instructions to use mgiardinetti/layout-parser-newspapers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mgiardinetti/layout-parser-newspapers with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="mgiardinetti/layout-parser-newspapers")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mgiardinetti/layout-parser-newspapers", dtype="auto") - Notebooks
- Google Colab
- Kaggle
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mgiardinetti/layout-parser-newspapers", dtype="auto")Quick Links
This model has been made in order to extract the pictures of old French newspapers pages. It has been re-trained from the pre-trained Prima Analysis Layout dataset (https://www.primaresearch.org/datasets/Layout_Analysis)
The model has been trained with BNF-Gallica newspapers annotated to detect the images and trained with Layout Parser. The script you can use to extract the images from this model is available as a notebook on my Github repo.
Model Details Model Description
Developed by: Marina Giardinetti
Repository: https://github.com/marinagiardinetti/Layout_Parser_Entrainement/
Python
Marina Giardinetti marina.giardinetti@gmail.com
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="mgiardinetti/layout-parser-newspapers")