Instructions to use keras/sam_huge_sa1b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use keras/sam_huge_sa1b with KerasHub:
import keras_hub # Create a ImageSegmenter model task = keras_hub.models.ImageSegmenter.from_preset("hf://keras/sam_huge_sa1b")import keras_hub # Create a Backbone model unspecialized for any task backbone = keras_hub.models.Backbone.from_preset("hf://keras/sam_huge_sa1b") - Keras
How to use keras/sam_huge_sa1b with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://keras/sam_huge_sa1b") - Notebooks
- Google Colab
- Kaggle
Download task.weights.h5 from keras/sam_huge_sa1b: direct link, hf CLI and curl.
- Browser
- Download file 2.57 GB
-
https://huggingface.co/keras/sam_huge_sa1b/resolve/main/task.weights.h5
- Command line
-
hf download hf://keras/sam_huge_sa1b/task.weights.h5
-
curl -L -o task.weights.h5 https://huggingface.co/keras/sam_huge_sa1b/resolve/main/task.weights.h5
2.57 GB
- Xet hash:
- 15fa690812696c42b4fb3b1e10765b6ce3300edebd17facac6c9e515a5bba08d
- Size of remote file:
- 2.57 GB
- SHA256:
- 0b125d3155c5403d6d1521f10b640ad9144954b76786ef24418d62c0c8196cfc
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