---
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-denoised-04Dec24-001
  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: 0.9696969696969697
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# vit-large-patch16-224-in21k-dungeon-geo-morphs-denoised-04Dec24-001

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: 0.0863
- Accuracy: 0.9697

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3958        | 4.0   | 10   | 0.9619          | 0.8384   |
| 0.4596        | 8.0   | 20   | 0.3876          | 0.9333   |
| 0.1012        | 12.0  | 30   | 0.1928          | 0.9616   |
| 0.022         | 16.0  | 40   | 0.1181          | 0.9636   |
| 0.0066        | 20.0  | 50   | 0.0936          | 0.9677   |
| 0.0036        | 24.0  | 60   | 0.0863          | 0.9697   |
| 0.0028        | 28.0  | 70   | 0.0848          | 0.9697   |
| 0.0025        | 32.0  | 80   | 0.0826          | 0.9697   |


### Framework versions

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