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aviandito/
vit-dunham-carbonate-classifier

Name: vit-dunham-carbonate-classifier
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generated_from_trainer

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google/vit-base-patch16-224-in21k

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vit-dunham-carbonate-classifier

Cite this model

You may cite this model as follows:

Mohammad Aviandito. (2025). vit-dunham-carbonate-classifier (Version b5946db). Hugging Face. https://doi.org/10.57967/HF/7237

Model description

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the Lokier & Al Junaibi (2016) 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) carbonate classification.

(Source)

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

Training and evaluation data

Source: Lokier & Al Junaibi (2016), Data S1

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

aviandito/vit-dunham-carbonate-classifier