Keras
English
remote-sensing
multispectral
hyperspectral
uncertainty-quantification
conformal-prediction
Instructions to use DaneshSelwal/Classification-6Bands-Models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use DaneshSelwal/Classification-6Bands-Models 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://DaneshSelwal/Classification-6Bands-Models") - Notebooks
- Google Colab
- Kaggle
Download baseline/models/AlexNet_CNN_final.keras from DaneshSelwal/Classification-6Bands-Models: direct link, hf CLI and curl.
- Browser
- Download file 272 MB
-
https://huggingface.co/DaneshSelwal/Classification-6Bands-Models/resolve/main/baseline/models/AlexNet_CNN_final.keras
- Command line
-
hf download hf://DaneshSelwal/Classification-6Bands-Models/baseline/models/AlexNet_CNN_final.keras
-
curl -L -o AlexNet_CNN_final.keras https://huggingface.co/DaneshSelwal/Classification-6Bands-Models/resolve/main/baseline/models/AlexNet_CNN_final.keras
272 MB
- Xet hash:
- 06d99cab322c19166c0f27aa08c2f79a31cfce53ffccc1311aa8ad176e09f3f2
- Size of remote file:
- 272 MB
- SHA256:
- 46556c3ab353637cff398036d4729fce22d7da28a2c92532a2cab489f9628758
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