Instructions to use ErnestBeckham/ViT-Lungs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use ErnestBeckham/ViT-Lungs with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://ErnestBeckham/ViT-Lungs") - Notebooks
- Google Colab
- Kaggle
Model description
This mode for classify the lung cancer using histopathology images. This is Transformer based model (ViT).
Intended uses & limitations
More information needed
Training and evaluation data
Dataset Source: https://www.kaggle.com/datasets/scipygaurav/lung-and-colon-cancer-dataset-splitted
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
| Hyperparameters | Value |
|---|---|
| name | Adam |
| learning_rate | 0.0001 |
| decay | 0.0 |
| beta_1 | 0.9 |
| beta_2 | 0.999 |
| epsilon | 1e-07 |
| amsgrad | False |
| training_precision | float32 |
Model Acccuracy
Model Plot
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