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) # See https://github.com/keras-team/tf-keras for more details. from huggingface_hub import from_pretrained_keras model = from_pretrained_keras("Vedmani/Transfer_Learning") - Notebooks
- Google Colab
- Kaggle
Download DenseNet201/saved_model.pb from Vedmani/Transfer_Learning: direct link, hf CLI and curl.
- Browser
- Download file 13.3 MB
-
https://huggingface.co/Vedmani/Transfer_Learning/resolve/main/DenseNet201/saved_model.pb
- Command line
-
hf download hf://Vedmani/Transfer_Learning/DenseNet201/saved_model.pb
-
curl -L -o saved_model.pb https://huggingface.co/Vedmani/Transfer_Learning/resolve/main/DenseNet201/saved_model.pb
13.3 MB
- Xet hash:
- d1bc0a33e51e55caae6167550ca495ff4cb10b00aae86305dba82afaaf2a1b15
- Size of remote file:
- 13.3 MB
- SHA256:
- 6fdf89a76e92c390c8a5a9d0e596151bf6333eb0e74dbbdc955a2bfe53933609
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.