Keras
TensorBoard
image
myology
biology
histology
muscle
cells
fibers
myopathy
SDH
myoquant
classification
mitochondria
Eval Results (legacy)
Instructions to use corentinm7/MyoQuant-SDH-Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use corentinm7/MyoQuant-SDH-Model 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://corentinm7/MyoQuant-SDH-Model") - Notebooks
- Google Colab
- Kaggle
Download training_curve.png from corentinm7/MyoQuant-SDH-Model: direct link, hf CLI and curl.
- Browser
- Download file 308 kB
-
https://huggingface.co/corentinm7/MyoQuant-SDH-Model/resolve/main/training_curve.png
- Command line
-
hf download hf://corentinm7/MyoQuant-SDH-Model/training_curve.png
-
curl -L -o training_curve.png https://huggingface.co/corentinm7/MyoQuant-SDH-Model/resolve/main/training_curve.png
308 kB

- Xet hash:
- 1c29a9af13f3baf960ca434ce93563e9de52654236d3b7327f861b063eeac774
- Size of remote file:
- 308 kB
- SHA256:
- 68b08954a728fd7241a5e78a87ee73fc2bddf87f4a87bda862e7a729fe2ccdea
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