---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_keras_callback
model-index:
- name: Deexit/custom_ViT
  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. -->

# Deexit/custom_ViT

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co./google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.9353
- Validation Loss: 1.0343
- Train Accuracy: 0.8667
- Epoch: 9

## 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': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 1680, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32

### Training results

| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 2.2697     | 2.1984          | 0.4667         | 0     |
| 2.1245     | 2.0728          | 0.6            | 1     |
| 1.9780     | 1.9057          | 0.8            | 2     |
| 1.8135     | 1.7702          | 0.8667         | 3     |
| 1.6516     | 1.6121          | 0.8667         | 4     |
| 1.4854     | 1.4733          | 0.8667         | 5     |
| 1.3306     | 1.3294          | 0.8667         | 6     |
| 1.1829     | 1.2269          | 0.8333         | 7     |
| 1.0596     | 1.1176          | 0.8667         | 8     |
| 0.9353     | 1.0343          | 0.8667         | 9     |


### Framework versions

- Transformers 4.31.0
- TensorFlow 2.12.0
- Datasets 2.14.4
- Tokenizers 0.13.3