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---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-dunham-carbonate-classifier
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8888888888888888
---
# vit-dunham-carbonate-classifier
## Model description
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 [Lokier & Al Junaibi (2016)](https://onlinelibrary.wiley.com/doi/10.1111/sed.12293) data S1.
The model captures the expertise of 177 volunteers from 33 countries with 3,270 years of academic & industry experience in classifying 14 carbonate thin section samples by using the classical [Dunham (1962)](https://en.wikipedia.org/wiki/Dunham_classification) carbonate classification.
([Source](https://commons.wikimedia.org/wiki/File:Dunham_classification_EN.svg))
In the original paper, the authors intended to objectively analyze whether these volunteers have the same standards in applying Dunham classification.
## Intended uses & limitations
- Input: Carbonate thin section image, can be either parallel-polarized (PPL) or cross-polarized (XPL)
- Output: Dunham classification (Mudstone/Wackestone/Packstone/Grainstone/Boundstone/Crystalline) and the probability value
- Limitation: The original dataset is missing Boundstone sample, hence it cannot classify a Boundstone.
Sample image source: [Grainstone - Wikipedia](https://en.wikipedia.org/wiki/Grainstone)
## Training and evaluation data
Source: [Lokier & Al Junaibi (2016), Data S1](https://onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1111%2Fsed.12293&file=sed12293-sup-0001-SupInfo.zip)
The data consists of 14 samples. Each samples has 3 magnifications (x2, x4, and x10) and taken in PPL and XPL. Hence, there are 14 samples * 3 magnifications * 2 polarizations = 84 images in the training dataset.
Classification for each sample is taken from the most popular respondent's response in Table 7.
- Sample 1: Packstone
- Sample 2: Grainstone
- Sample 3: Wackestone
- Sample 4: Packstone
- Sample 5: Wackestone
- Sample 6: Packstone
- Sample 7: Packstone
- Sample 8: Mudstone
- Sample 9: Crystalline
- Sample 10: Grainstone
- Sample 11: Wackestone
- Sample 12: Grainstone
- Sample 13: Grainstone
- Sample 14: Mudstone
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5764 | 1.0 | 5 | 1.5329 | 0.4444 |
| 1.3991 | 2.0 | 10 | 1.4253 | 0.5556 |
| 1.2792 | 3.0 | 15 | 1.2851 | 0.7778 |
| 1.0119 | 4.0 | 20 | 1.1625 | 0.8889 |
| 0.9916 | 5.0 | 25 | 1.0471 | 0.8889 |
| 0.9202 | 6.0 | 30 | 0.9836 | 0.7778 |
| 0.6994 | 7.0 | 35 | 0.8649 | 0.8889 |
| 0.526 | 8.0 | 40 | 0.7110 | 1.0 |
| 0.5383 | 9.0 | 45 | 0.6127 | 1.0 |
| 0.5128 | 10.0 | 50 | 0.5337 | 1.0 |
| 0.4312 | 11.0 | 55 | 0.4887 | 1.0 |
| 0.3827 | 12.0 | 60 | 0.4365 | 1.0 |
| 0.3452 | 13.0 | 65 | 0.3891 | 1.0 |
| 0.3164 | 14.0 | 70 | 0.3677 | 1.0 |
| 0.2899 | 15.0 | 75 | 0.3555 | 1.0 |
| 0.2878 | 16.0 | 80 | 0.3197 | 1.0 |
| 0.2884 | 17.0 | 85 | 0.3056 | 1.0 |
| 0.2633 | 18.0 | 90 | 0.3107 | 1.0 |
| 0.2669 | 19.0 | 95 | 0.3164 | 1.0 |
| 0.2465 | 20.0 | 100 | 0.2949 | 1.0 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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