Instructions to use ErnestBeckham/BreastResViT-II with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use ErnestBeckham/BreastResViT-II 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/BreastResViT-II") - Notebooks
- Google Colab
- Kaggle
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Download README.md from ErnestBeckham/BreastResViT-II: direct link, hf CLI and curl.
- Browser
- Download file 839 Bytes
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https://huggingface.co/ErnestBeckham/BreastResViT-II/resolve/main/README.md
- Command line
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hf download hf://ErnestBeckham/BreastResViT-II/README.md
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curl -L -o README.md https://huggingface.co/ErnestBeckham/BreastResViT-II/resolve/main/README.md
839 Bytes
metadata
library_name: keras
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
| Hyperparameters | Value |
|---|---|
| name | Adam |
| weight_decay | None |
| clipnorm | None |
| global_clipnorm | None |
| clipvalue | 1.0 |
| use_ema | False |
| ema_momentum | 0.99 |
| ema_overwrite_frequency | None |
| jit_compile | True |
| is_legacy_optimizer | False |
| learning_rate | 9.999999747378752e-05 |
| beta_1 | 0.9 |
| beta_2 | 0.999 |
| epsilon | 1e-07 |
| amsgrad | False |
| training_precision | float32 |
