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