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:
- 9b3a39018cdddae3550b6e723984d60e09ea8f3859f72f8c687c540506a10503
- Size of remote file:
- 376 MB
- SHA256:
- 5691f2ede5263c633847443e52c3b954c7c2e59c702e81272233e9d830eedc35
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