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

# volvoDon/petro-daemon

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on a [DataSet of petrologic cross sections](https://huggingface.co/datasets/volvoDon/petrology-sections).
It achieves the following results on the evaluation set:
- Train Loss: 0.8890
- Validation Loss: 1.1803
- Train Accuracy: 0.6
- Epoch: 19

## Model description

More information needed

## Intended uses & limitations

Currently it is just a proof of concept and does a great job identifiying Olivine 
It currently is not ready for a production enviroment but the results are promising, with an improved dataset I'm confident better results could be acheived.

### 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': 300, '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 |
|:----------:|:---------------:|:--------------:|:-----:|
| 1.6519     | 1.7095          | 0.2            | 0     |
| 1.5905     | 1.6747          | 0.2            | 1     |
| 1.5690     | 1.6342          | 0.2            | 2     |
| 1.5170     | 1.5931          | 0.2            | 3     |
| 1.4764     | 1.5528          | 0.6            | 4     |
| 1.3835     | 1.5079          | 0.6            | 5     |
| 1.3420     | 1.4717          | 0.6            | 6     |
| 1.3171     | 1.4232          | 0.6            | 7     |
| 1.2897     | 1.3905          | 0.6            | 8     |
| 1.2702     | 1.3794          | 0.6            | 9     |
| 1.2023     | 1.3351          | 0.6            | 10    |
| 1.1480     | 1.3384          | 0.6            | 11    |
| 1.1434     | 1.3419          | 0.6            | 12    |
| 1.0499     | 1.3226          | 0.6            | 13    |
| 1.0672     | 1.2647          | 0.6            | 14    |
| 1.0526     | 1.1533          | 0.6            | 15    |
| 1.0184     | 1.1546          | 0.6            | 16    |
| 0.9505     | 1.2491          | 0.6            | 17    |
| 0.9578     | 1.2809          | 0.4            | 18    |
| 0.8890     | 1.1803          | 0.6            | 19    |


### Framework versions

- Transformers 4.32.1
- TensorFlow 2.12.0
- Datasets 2.14.4
- Tokenizers 0.13.3