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 EfficientNet_Architecture/efficientnet_backbone_head.png from shkelqim2026/EfficientNet: direct link, hf CLI and curl.
- Browser
- Download file 362 kB
-
https://huggingface.co/shkelqim2026/EfficientNet/resolve/main/EfficientNet_Architecture/efficientnet_backbone_head.png
- Command line
-
hf download hf://shkelqim2026/EfficientNet/EfficientNet_Architecture/efficientnet_backbone_head.png
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curl -L -o efficientnet_backbone_head.png https://huggingface.co/shkelqim2026/EfficientNet/resolve/main/EfficientNet_Architecture/efficientnet_backbone_head.png
362 kB

- Xet hash:
- f4a8b6f91c116f91940ffb3dd9fb58b0772ae15aebbc89708046341ccd379c8f
- Size of remote file:
- 362 kB
- SHA256:
- da06feb0b9e3a0f35962e5e3357aeba1b7102e693db965d3d0787c1d9e719930
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