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/ResNet152/keras_metadata.pb from Vedmani/Transfer_Learning: direct link, hf CLI and curl.
- Browser
- Download file 1.62 MB
-
https://huggingface.co/Vedmani/Transfer_Learning/resolve/main/Models/ResNet152/keras_metadata.pb
- Command line
-
hf download hf://Vedmani/Transfer_Learning/Models/ResNet152/keras_metadata.pb
-
curl -L -o keras_metadata.pb https://huggingface.co/Vedmani/Transfer_Learning/resolve/main/Models/ResNet152/keras_metadata.pb
1.62 MB
- Xet hash:
- e0cda1a973b9466b9bbcef8844eeff1ef6e274905cad3568f010ba6d1a69bd26
- Size of remote file:
- 1.62 MB
- SHA256:
- 7a7661880c11e9acd4e8344f5968d3ae4f29de48198d78e1b84b60f40eb83e4e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.