Instructions to use OKTAYBBS/kaggle-mastery-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use OKTAYBBS/kaggle-mastery-models with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://OKTAYBBS/kaggle-mastery-models") - Notebooks
- Google Colab
- Kaggle
Download leaf_classification/model_leaf_classification.pkl from OKTAYBBS/kaggle-mastery-models: direct link, hf CLI and curl.
- Browser
- Download file 192 kB
-
https://huggingface.co/OKTAYBBS/kaggle-mastery-models/resolve/main/leaf_classification/model_leaf_classification.pkl
- Command line
-
hf download hf://OKTAYBBS/kaggle-mastery-models/leaf_classification/model_leaf_classification.pkl
-
curl -L -o model_leaf_classification.pkl https://huggingface.co/OKTAYBBS/kaggle-mastery-models/resolve/main/leaf_classification/model_leaf_classification.pkl
192 kB
- Xet hash:
- 4605647de6328040f95c26946cb9692bd4db46512e134a74147fd973aedede5c
- Size of remote file:
- 192 kB
- SHA256:
- 5ba42cd54f5bb701726caead11a3e20f2ac89bba108300b652f0a065cbe32530
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