Keras
English
remote-sensing
hyperspectral
uncertainty-quantification
conformal-prediction
mamba
foundation-models
Instructions to use DaneshSelwal/Classification-DIAS-65Bands with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use DaneshSelwal/Classification-DIAS-65Bands 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-DIAS-65Bands") - Notebooks
- Google Colab
- Kaggle
Download multicp/models/AlexNet_CNN_MultiHead_final.keras from DaneshSelwal/Classification-DIAS-65Bands: direct link, hf CLI and curl.
- Browser
- Download file 273 MB
-
https://huggingface.co/DaneshSelwal/Classification-DIAS-65Bands/resolve/main/multicp/models/AlexNet_CNN_MultiHead_final.keras
- Command line
-
hf download hf://DaneshSelwal/Classification-DIAS-65Bands/multicp/models/AlexNet_CNN_MultiHead_final.keras
-
curl -L -o AlexNet_CNN_MultiHead_final.keras https://huggingface.co/DaneshSelwal/Classification-DIAS-65Bands/resolve/main/multicp/models/AlexNet_CNN_MultiHead_final.keras
273 MB
- Xet hash:
- 753031e175a18e7cb62e983febd0ea8708020dec23081d714c305f46b01be696
- Size of remote file:
- 273 MB
- SHA256:
- e483d107f635cc68fc3e5d89a9fdba7fc7b32f08ec3348b3b1100785c0381ca9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.