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
File size: 694 Bytes
1d197a4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | {
"selected_threshold": 0.5199999999999999,
"selection": {
"method": "validation_sweep",
"metric": "cls_f1",
"selected_threshold": 0.5199999999999999,
"val_best": {
"threshold": 0.5199999999999999,
"cls_accuracy": 0.8888888888888888,
"cls_precision": 0.8888888888888888,
"cls_recall": 0.9491525423728814,
"cls_f1": 0.9180327868852458,
"cls_auc": 0.9111536741256714,
"tp": 56,
"fp": 7,
"fn": 3,
"tn": 24
}
},
"source_split_json": "evals/splits/ivus_split_merged_600.json",
"source_model_path": "models/multitask/bifurcation_head.keras",
"source_base_model_dir": "models/multitask/lumen_multitask_base"
} |