Instructions to use johnlockejrr/sbb_pixelwise_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TF-Keras
How to use johnlockejrr/sbb_pixelwise_v1 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("johnlockejrr/sbb_pixelwise_v1") - Notebooks
- Google Colab
- Kaggle
Download saved_model.pb from johnlockejrr/sbb_pixelwise_v1: direct link, hf CLI and curl.
- Browser
- Download file 2.26 MB
-
https://huggingface.co/johnlockejrr/sbb_pixelwise_v1/resolve/main/saved_model.pb
- Command line
-
hf download hf://johnlockejrr/sbb_pixelwise_v1/saved_model.pb
-
curl -L -o saved_model.pb https://huggingface.co/johnlockejrr/sbb_pixelwise_v1/resolve/main/saved_model.pb
2.26 MB
- Xet hash:
- df1d6479bd458106ec8d3be6be16671056ac485a9a7c5f1b7992ba7c60264075
- Size of remote file:
- 2.26 MB
- SHA256:
- a2a996d629ed2288194c2563474f3b042462ca2431ceec07b39cb42e39cc1340
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.