Image Segmentation
Keras
ONNX
English
tensorflow
medical-imaging
segmentation
in-context-learning
interactive-segmentation
ct
mri
Instructions to use machauer-p/lisp-net with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use machauer-p/lisp-net with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://machauer-p/lisp-net") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 153145ed9c3c969134049eb097b7e7a5ba80ded134c9e306c1aad6a801ccde55
- Size of remote file:
- 112 MB
- SHA256:
- e7c884e5af7a992ba39e43a9e88d188d20185e9f3160ef1f29c9211d8f9e353a
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