Instructions to use dataguychill/VioMobileNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TF-Keras
How to use dataguychill/VioMobileNet 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("dataguychill/VioMobileNet") - Notebooks
- Google Colab
- Kaggle
Download model/keras_metadata.pb from dataguychill/VioMobileNet: direct link, hf CLI and curl.
- Browser
- Download file 1.45 MB
-
https://huggingface.co/dataguychill/VioMobileNet/resolve/main/model/keras_metadata.pb
- Command line
-
hf download hf://dataguychill/VioMobileNet/model/keras_metadata.pb
-
curl -L -o keras_metadata.pb https://huggingface.co/dataguychill/VioMobileNet/resolve/main/model/keras_metadata.pb
1.45 MB
- Xet hash:
- a3719af521e51f81777105fb6a1190033f70a3995056a9cb0581fb4a536e34c3
- Size of remote file:
- 1.45 MB
- SHA256:
- 03692220d20fc9a57adeee4baec8da7c0998c532fe5771528c2a249ad0444307
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.