Instructions to use DevBhuyan/Digit-Recognition-Lightweight with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use DevBhuyan/Digit-Recognition-Lightweight 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://DevBhuyan/Digit-Recognition-Lightweight") - Notebooks
- Google Colab
- Kaggle
An ultra-lightweight model useful for distinguishing between different handwritten digits. All in less than 1.8 MiB, and gives a validation accuracy of 98.60% on Kaggle's private testing dataset.
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