Instructions to use keras/sam_base_sa1b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use keras/sam_base_sa1b with KerasHub:
import keras_hub # Create a ImageSegmenter model task = keras_hub.models.ImageSegmenter.from_preset("hf://keras/sam_base_sa1b")import keras_hub # Create a Backbone model unspecialized for any task backbone = keras_hub.models.Backbone.from_preset("hf://keras/sam_base_sa1b") - Keras
How to use keras/sam_base_sa1b with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://keras/sam_base_sa1b") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0e876f6ff241075bde1b9483037590209a95297911f6c3249781a30ad55ff3d5
- Size of remote file:
- 376 MB
- SHA256:
- 3527eb2d0f0dcbde55ca790c6e3beeeffb3828899b6e0cc45d040c4c16dbda03
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