Instructions to use SeyedAli/Melanoma-Classification-EfficientNetB0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use SeyedAli/Melanoma-Classification-EfficientNetB0 with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://SeyedAli/Melanoma-Classification-EfficientNetB0") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- dcea93e8b80740eb3db2a4c45bbef23c612f540f401aa1a8a17786c6a0ad2615
- Size of remote file:
- 1.27 MB
- SHA256:
- e6e9ea984ddd489b31dfabff0f47f574e04bbd0f7279e736096af81b256c7351
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.