Instructions to use J1gA/EfficientNetB4_zernike with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TF-Keras
How to use J1gA/EfficientNetB4_zernike with TF-Keras:
# Note: 'keras<3.x' or 'tf_keras' must be installed (legacy), and from_pretrained_keras was removed in huggingface_hub 1.0. # See https://github.com/keras-team/tf-keras for more details. # !pip install "huggingface_hub<1.0" tf_keras from huggingface_hub import from_pretrained_keras model = from_pretrained_keras("J1gA/EfficientNetB4_zernike") - Notebooks
- Google Colab
- Kaggle
Download saved_model.pb from J1gA/EfficientNetB4_zernike: direct link, hf CLI and curl.
- Browser
- Download file 8.6 MB
-
https://huggingface.co/J1gA/EfficientNetB4_zernike/resolve/main/saved_model.pb
- Command line
-
hf download hf://J1gA/EfficientNetB4_zernike/saved_model.pb
-
curl -L -o saved_model.pb https://huggingface.co/J1gA/EfficientNetB4_zernike/resolve/main/saved_model.pb
8.6 MB
- Xet hash:
- e39709c1571fa419cac2b4fa93050a34517697df74a20b95bdf94899b5b39f61
- Size of remote file:
- 8.6 MB
- SHA256:
- 6fe8533b59f8d8086482fa3ad7e1c0d10d17a436d3cfe0983c6ce7b8da6d2f32
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.