---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- tensorflow
- vision
- generated_from_keras_callback
model-index:
- name: RenSurii/vit-base-patch16-224-in21k-finetuned-image-classification
  results: []
---

<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->

# RenSurii/vit-base-patch16-224-in21k-finetuned-image-classification

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co./google/vit-base-patch16-224-in21k) on the mnist dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.0110
- Train Accuracy: 0.8531
- Validation Loss: 1.5291
- Validation Accuracy: 0.8531
- Epoch: 0

## 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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': 1.0, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 5000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32

### Training results

| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 2.0110     | 0.8531         | 1.5291          | 0.8531              | 0     |


### Framework versions

- Transformers 4.47.0.dev0
- TensorFlow 2.18.0
- Datasets 3.1.0
- Tokenizers 0.20.3