Instructions to use shivalikasingh/shiftViT-Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use shivalikasingh/shiftViT-Model with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://shivalikasingh/shiftViT-Model") - Notebooks
- Google Colab
- Kaggle
metadata
library_name: keras
tags:
- ShiftVit
- Image Classification
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 | AdamW |
| learning_rate.class_name | WarmUpCosine |
| learning_rate.config.lr_start | 1e-05 |
| learning_rate.config.lr_max | 0.001 |
| learning_rate.config.total_steps | 15625 |
| learning_rate.config.warmup_steps | 2343 |
| decay | 0.0 |
| beta_1 | 0.8999999761581421 |
| beta_2 | 0.9990000128746033 |
| epsilon | 1e-07 |
| amsgrad | False |
| weight_decay | 9.999999747378752e-05 |
| exclude_from_weight_decay | None |
| training_precision | float32 |
