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vit-dunham-carbonate-classifier

Image Classification
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PyTorch
vit
generated_from_trainer
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metadata
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 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.

image/png (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 image/png

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