Instructions to use Aditya2162/ivus-segmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use Aditya2162/ivus-segmentation with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://Aditya2162/ivus-segmentation") - Notebooks
- Google Colab
- Kaggle
| """Processing subpackage: preprocessing and fusion helpers.""" | |
| from .fusion import ( | |
| build_sam_label_masks, | |
| empty_contours, | |
| fill_empty_lumen_contours_temporally, | |
| fill_empty_masks_temporally, | |
| merge_sam_with_tensorflow, | |
| propagate_previous_mask_on_zero_score, | |
| top_k_lumen_exemplar_indices, | |
| ) | |
| from .preprocessing import apply_center_black_circle, ensure_uint8 | |
| __all__ = [ | |
| "ensure_uint8", | |
| "apply_center_black_circle", | |
| "top_k_lumen_exemplar_indices", | |
| "build_sam_label_masks", | |
| "empty_contours", | |
| "merge_sam_with_tensorflow", | |
| "propagate_previous_mask_on_zero_score", | |
| "fill_empty_masks_temporally", | |
| "fill_empty_lumen_contours_temporally", | |
| ] | |