---
library_name: transformers
license: apache-2.0
base_model: google/vit-large-patch16-224-in21k
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-large-patch16-224-in21k-dungeon-geo-morphs-0-4-30Nov24-004
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: dungeon-geo-morphs
      type: imagefolder
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9625
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# vit-large-patch16-224-in21k-dungeon-geo-morphs-0-4-30Nov24-004

This model is a fine-tuned version of [google/vit-large-patch16-224-in21k](https://huggingface.co/google/vit-large-patch16-224-in21k) on the dungeon-geo-morphs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1275
- Accuracy: 0.9625

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 40
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.3834        | 3.9091  | 10   | 1.1055          | 0.7929   |
| 0.5606        | 7.9091  | 20   | 0.5141          | 0.9286   |
| 0.13          | 11.9091 | 30   | 0.2629          | 0.9518   |
| 0.0283        | 15.9091 | 40   | 0.1654          | 0.9464   |
| 0.0082        | 19.9091 | 50   | 0.1352          | 0.9554   |
| 0.0043        | 23.9091 | 60   | 0.1337          | 0.9589   |
| 0.0033        | 27.9091 | 70   | 0.1257          | 0.9607   |
| 0.0029        | 31.9091 | 80   | 0.1275          | 0.9625   |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3