Image Segmentation
Flair
Keras
tensorflow
medical-imaging
white-matter-hyperintensities
mri
deep-learning
neurology
multiple-sclerosis
Instructions to use Bawil/wmh_leverage_normal_abnormal_segmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Flair
How to use Bawil/wmh_leverage_normal_abnormal_segmentation with Flair:
from flair.models import SequenceTagger tagger = SequenceTagger.load("Bawil/wmh_leverage_normal_abnormal_segmentation") - Keras
How to use Bawil/wmh_leverage_normal_abnormal_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://Bawil/wmh_leverage_normal_abnormal_segmentation") - Notebooks
- Google Colab
- Kaggle
Upload experiment_config.json
Browse files
unet/config/experiment_config.json
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{
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"timestamp": "20251124_152044",
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"input_shape": [
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256,
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256,
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1
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],
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"target_size": [
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256,
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256
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],
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"epochs": 50,
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"batch_size": 8,
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"learning_rate": 0.0001,
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"validation_split": 0.1,
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"random_state": 42,
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"loss_options": {
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"scenario1": "weighted_bce",
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"scenario2": "weighted_categorical"
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}
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}
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