Instructions to use shkelqim2026/EfficientNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use shkelqim2026/EfficientNet 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://shkelqim2026/EfficientNet") - Notebooks
- Google Colab
- Kaggle
Download Figures/Classification Head..png from shkelqim2026/EfficientNet: direct link, hf CLI and curl.
- Browser
- Download file 666 kB
-
https://huggingface.co/shkelqim2026/EfficientNet/resolve/main/Figures/Classification%20Head..png
- Command line
-
hf download 'hf://shkelqim2026/EfficientNet/Figures/Classification Head..png'
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curl -L -o 'Classification Head..png' https://huggingface.co/shkelqim2026/EfficientNet/resolve/main/Figures/Classification%20Head..png
666 kB

- Xet hash:
- f4d162f2dccb803ee4b76d86b26ad31bff44ed432fb4a57203dac3de303ce45d
- Size of remote file:
- 666 kB
- SHA256:
- 4a073179ecf0c27d3c34fa45d96a0967237ea1d17ba288f8db255845c4ad43d0
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