---
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_10x_deit_small_adamax_00001_fold3
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: test
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9183333333333333
---

<!-- 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. -->

# smids_10x_deit_small_adamax_00001_fold3

This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8187
- Accuracy: 0.9183

## 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2585        | 1.0   | 750   | 0.2681          | 0.8983   |
| 0.2105        | 2.0   | 1500  | 0.2490          | 0.9167   |
| 0.0786        | 3.0   | 2250  | 0.2625          | 0.9167   |
| 0.0736        | 4.0   | 3000  | 0.2826          | 0.9133   |
| 0.0688        | 5.0   | 3750  | 0.3568          | 0.91     |
| 0.0468        | 6.0   | 4500  | 0.4349          | 0.9083   |
| 0.0289        | 7.0   | 5250  | 0.4645          | 0.9183   |
| 0.0394        | 8.0   | 6000  | 0.5300          | 0.9183   |
| 0.0012        | 9.0   | 6750  | 0.5842          | 0.92     |
| 0.0139        | 10.0  | 7500  | 0.6285          | 0.915    |
| 0.0002        | 11.0  | 8250  | 0.6464          | 0.9217   |
| 0.0001        | 12.0  | 9000  | 0.6757          | 0.9133   |
| 0.0           | 13.0  | 9750  | 0.7480          | 0.9167   |
| 0.0001        | 14.0  | 10500 | 0.7033          | 0.92     |
| 0.0           | 15.0  | 11250 | 0.7525          | 0.9133   |
| 0.0           | 16.0  | 12000 | 0.7472          | 0.915    |
| 0.0           | 17.0  | 12750 | 0.7380          | 0.92     |
| 0.0           | 18.0  | 13500 | 0.7432          | 0.9183   |
| 0.0           | 19.0  | 14250 | 0.7438          | 0.9217   |
| 0.0           | 20.0  | 15000 | 0.7615          | 0.92     |
| 0.0           | 21.0  | 15750 | 0.7581          | 0.9233   |
| 0.0           | 22.0  | 16500 | 0.7753          | 0.92     |
| 0.0           | 23.0  | 17250 | 0.7758          | 0.92     |
| 0.0           | 24.0  | 18000 | 0.7745          | 0.9217   |
| 0.0           | 25.0  | 18750 | 0.7780          | 0.9233   |
| 0.0           | 26.0  | 19500 | 0.7763          | 0.9217   |
| 0.0           | 27.0  | 20250 | 0.7839          | 0.9183   |
| 0.0           | 28.0  | 21000 | 0.7914          | 0.9183   |
| 0.0           | 29.0  | 21750 | 0.7935          | 0.92     |
| 0.0           | 30.0  | 22500 | 0.8320          | 0.9117   |
| 0.0           | 31.0  | 23250 | 0.8021          | 0.9183   |
| 0.0           | 32.0  | 24000 | 0.8041          | 0.9217   |
| 0.0           | 33.0  | 24750 | 0.8030          | 0.9167   |
| 0.0           | 34.0  | 25500 | 0.8170          | 0.9133   |
| 0.0           | 35.0  | 26250 | 0.8237          | 0.915    |
| 0.0           | 36.0  | 27000 | 0.8072          | 0.9167   |
| 0.0           | 37.0  | 27750 | 0.8249          | 0.915    |
| 0.0           | 38.0  | 28500 | 0.8116          | 0.9167   |
| 0.0           | 39.0  | 29250 | 0.8160          | 0.9217   |
| 0.0           | 40.0  | 30000 | 0.8158          | 0.92     |
| 0.0           | 41.0  | 30750 | 0.8164          | 0.92     |
| 0.0           | 42.0  | 31500 | 0.8163          | 0.92     |
| 0.0           | 43.0  | 32250 | 0.8169          | 0.92     |
| 0.0           | 44.0  | 33000 | 0.8174          | 0.92     |
| 0.0           | 45.0  | 33750 | 0.8182          | 0.92     |
| 0.0           | 46.0  | 34500 | 0.8186          | 0.9183   |
| 0.0           | 47.0  | 35250 | 0.8185          | 0.92     |
| 0.0           | 48.0  | 36000 | 0.8187          | 0.92     |
| 0.0           | 49.0  | 36750 | 0.8181          | 0.9183   |
| 0.0           | 50.0  | 37500 | 0.8187          | 0.9183   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2