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
Download transunet/config/experiment_config.json from Bawil/wmh_leverage_normal_abnormal_segmentation: direct link, hf CLI and curl.
- Browser
- Download file 334 Bytes
-
https://huggingface.co/Bawil/wmh_leverage_normal_abnormal_segmentation/resolve/f4d9c79455fcf4b46331a1ac32fcc23a0646afd4/transunet/config/experiment_config.json
- Command line
-
hf download hf://Bawil/wmh_leverage_normal_abnormal_segmentation@f4d9c79455fcf4b46331a1ac32fcc23a0646afd4/transunet/config/experiment_config.json
-
curl -L -o experiment_config.json https://huggingface.co/Bawil/wmh_leverage_normal_abnormal_segmentation/resolve/f4d9c79455fcf4b46331a1ac32fcc23a0646afd4/transunet/config/experiment_config.json
334 Bytes
| { | |
| "timestamp": "20251124_171430", | |
| "input_shape": [ | |
| 256, | |
| 256, | |
| 1 | |
| ], | |
| "target_size": [ | |
| 256, | |
| 256 | |
| ], | |
| "epochs": 50, | |
| "batch_size": 8, | |
| "learning_rate": 0.0001, | |
| "validation_split": 0.1, | |
| "random_state": 42, | |
| "loss_options": { | |
| "scenario1": "weighted_bce", | |
| "scenario2": "weighted_categorical" | |
| } | |
| } |