Instructions to use Vedmani/Transfer_Learning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TF-Keras
How to use Vedmani/Transfer_Learning 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("Vedmani/Transfer_Learning") - Notebooks
- Google Colab
- Kaggle
Download Models/ResNet101/saved_model.pb from Vedmani/Transfer_Learning: direct link, hf CLI and curl.
- Browser
- Download file 7.21 MB
-
https://huggingface.co/Vedmani/Transfer_Learning/resolve/main/Models/ResNet101/saved_model.pb
- Command line
-
hf download hf://Vedmani/Transfer_Learning/Models/ResNet101/saved_model.pb
-
curl -L -o saved_model.pb https://huggingface.co/Vedmani/Transfer_Learning/resolve/main/Models/ResNet101/saved_model.pb
7.21 MB
- Xet hash:
- f21ccf9c4649c9289d805cadc4c3c11b29573c7b395ccf83ce259bc06dc86709
- Size of remote file:
- 7.21 MB
- SHA256:
- 4106e45ac9c2a69bdc5c9f207f87e93f50b596f9da4c866785745533aba17306
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.