Instructions to use ZenithsArch/Avalanche_Classification_Weights with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use ZenithsArch/Avalanche_Classification_Weights 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://ZenithsArch/Avalanche_Classification_Weights") - Notebooks
- Google Colab
- Kaggle
Download models/B_Sobel_EfficientNetV2S.keras from ZenithsArch/Avalanche_Classification_Weights: direct link, hf CLI and curl.
- Browser
- Download file 108 MB
-
https://huggingface.co/ZenithsArch/Avalanche_Classification_Weights/resolve/main/models/B_Sobel_EfficientNetV2S.keras
- Command line
-
hf download hf://ZenithsArch/Avalanche_Classification_Weights/models/B_Sobel_EfficientNetV2S.keras
-
curl -L -o B_Sobel_EfficientNetV2S.keras https://huggingface.co/ZenithsArch/Avalanche_Classification_Weights/resolve/main/models/B_Sobel_EfficientNetV2S.keras
108 MB
- Xet hash:
- 1e76c4f30011a07778d1ad76958be41c805bc9572589e314bd3c664964ba9365
- Size of remote file:
- 108 MB
- SHA256:
- 99b8ff4bdd9ee06df0ec2d8c9ff41df654beeba8bc3af46ccd4c108c569f466d
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