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-B2/test_features.npy from shkelqim2026/EfficientNet: direct link, hf CLI and curl.
- Browser
- Download file 11.2 MB
-
https://huggingface.co/shkelqim2026/EfficientNet/resolve/main/EfficientNet-B2/test_features.npy
- Command line
-
hf download hf://shkelqim2026/EfficientNet/EfficientNet-B2/test_features.npy
-
curl -L -o test_features.npy https://huggingface.co/shkelqim2026/EfficientNet/resolve/main/EfficientNet-B2/test_features.npy
11.2 MB
- Xet hash:
- 7bfeef20ff9bcb5a33fc2de56ca0458b3515e7a6ba33fe8fc682887355b7e8f6
- Size of remote file:
- 11.2 MB
- SHA256:
- b71a0a90903d9a5bf32604b714cada2869952489a70a500e02371891921d4292
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