Instructions to use jzju/dit-doclaynet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jzju/dit-doclaynet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="jzju/dit-doclaynet")# Load model directly from transformers import AutoImageProcessor, BeitForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("jzju/dit-doclaynet") model = BeitForSemanticSegmentation.from_pretrained("jzju/dit-doclaynet", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "crop_size": 224, | |
| "do_center_crop": false, | |
| "do_normalize": true, | |
| "do_resize": true, | |
| "feature_extractor_type": "BeitFeatureExtractor", | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "reduce_labels": false, | |
| "resample": 2, | |
| "size": 224 | |
| } | |