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_training_strategy.ai from shkelqim2026/EfficientNet: direct link, hf CLI and curl.
- Browser
- Download file 184 kB
-
https://huggingface.co/shkelqim2026/EfficientNet/resolve/main/EfficientNet_Architecture/efficientnet_training_strategy.ai
- Command line
-
hf download hf://shkelqim2026/EfficientNet/EfficientNet_Architecture/efficientnet_training_strategy.ai
-
curl -L -o efficientnet_training_strategy.ai https://huggingface.co/shkelqim2026/EfficientNet/resolve/main/EfficientNet_Architecture/efficientnet_training_strategy.ai
184 kB
- Xet hash:
- 46e99c4ea7c916f87714bbfa86f00578c17004771efdd60a511d3621660c9f35
- Size of remote file:
- 184 kB
- SHA256:
- 1a4d415e21f1636175a85f47d9bdb75adfe0800623293dc99e56486bea584bbd
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