---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-rocks
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: rocks
      type: imagefolder
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7777777777777778
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# vit-base-rocks

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 rocks dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7099
- Accuracy: 0.7778

## Model description

This model is a fine-tuned version of Google's vit-base-patch16-224-in21k designed to identify geological hand samples.

## Intended uses & limitations

Currently the VIT is fine-tuned on 10 classes:

['Andesite', 'Basalt', 'Chalk', 'Dolomite', 'Flint', 'Gneiss', 'Granite', 'Limestone', 'Sandstone', 'Slate']

Future iteartions of the model will feature an expanded breadth of rock categories.


## Training and evaluation data

The model performs relatively well on 10 classes of rocks - with some confusion between limestone and other carbonates.

![image/png](https://cdn-uploads.huggingface.co/production/uploads/67b218c9f745d44676c938cb/zZXcIybZLvUtEKpb8Lk8u.png)


## Training procedure

495 images of geological hand samples were selected with an 80:20 train-test/validation split.

Classes were roughly equally represented across the 495 samples.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 25

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 2.0408        | 1.4286  | 10   | 1.7371          | 0.6111   |
| 1.4489        | 2.8571  | 20   | 1.3254          | 0.7407   |
| 0.9469        | 4.2857  | 30   | 1.0768          | 0.7407   |
| 0.586         | 5.7143  | 40   | 0.9118          | 0.7778   |
| 0.3757        | 7.1429  | 50   | 0.9902          | 0.6852   |
| 0.2798        | 8.5714  | 60   | 0.8498          | 0.7778   |
| 0.2087        | 10.0    | 70   | 0.7939          | 0.7407   |
| 0.176         | 11.4286 | 80   | 0.8220          | 0.7222   |
| 0.1613        | 12.8571 | 90   | 0.7288          | 0.8148   |
| 0.1337        | 14.2857 | 100  | 0.7178          | 0.7963   |
| 0.1326        | 15.7143 | 110  | 0.7403          | 0.7778   |
| 0.119         | 17.1429 | 120  | 0.7099          | 0.7778   |
| 0.1193        | 18.5714 | 130  | 0.7626          | 0.7778   |
| 0.1227        | 20.0    | 140  | 0.7125          | 0.7963   |
| 0.1102        | 21.4286 | 150  | 0.7493          | 0.7963   |
| 0.1134        | 22.8571 | 160  | 0.7396          | 0.7963   |
| 0.1173        | 24.2857 | 170  | 0.7187          | 0.7963   |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.6.0
- Datasets 3.3.0
- Tokenizers 0.21.0