---
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_rms_00001_fold5
  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: 0.9166666666666666
---

<!-- 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_rms_00001_fold5

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: 0.9311
- Accuracy: 0.9167

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1779        | 1.0   | 750   | 0.2473          | 0.905    |
| 0.139         | 2.0   | 1500  | 0.3262          | 0.8817   |
| 0.0937        | 3.0   | 2250  | 0.2997          | 0.9133   |
| 0.0255        | 4.0   | 3000  | 0.4034          | 0.9033   |
| 0.0426        | 5.0   | 3750  | 0.4840          | 0.9133   |
| 0.0099        | 6.0   | 4500  | 0.7148          | 0.9017   |
| 0.0272        | 7.0   | 5250  | 0.7135          | 0.9      |
| 0.0013        | 8.0   | 6000  | 0.7156          | 0.91     |
| 0.0252        | 9.0   | 6750  | 0.7066          | 0.9      |
| 0.0           | 10.0  | 7500  | 0.7258          | 0.91     |
| 0.0281        | 11.0  | 8250  | 0.8120          | 0.8967   |
| 0.0           | 12.0  | 9000  | 0.7428          | 0.91     |
| 0.0001        | 13.0  | 9750  | 0.7455          | 0.9183   |
| 0.0272        | 14.0  | 10500 | 0.7891          | 0.92     |
| 0.0           | 15.0  | 11250 | 0.8803          | 0.8967   |
| 0.0           | 16.0  | 12000 | 0.8867          | 0.9      |
| 0.0025        | 17.0  | 12750 | 0.8600          | 0.9067   |
| 0.0           | 18.0  | 13500 | 0.7993          | 0.9183   |
| 0.0           | 19.0  | 14250 | 0.8779          | 0.9133   |
| 0.0           | 20.0  | 15000 | 0.8996          | 0.9117   |
| 0.0004        | 21.0  | 15750 | 0.9765          | 0.8917   |
| 0.0157        | 22.0  | 16500 | 0.7715          | 0.92     |
| 0.0           | 23.0  | 17250 | 0.7227          | 0.91     |
| 0.0           | 24.0  | 18000 | 0.7725          | 0.9167   |
| 0.0           | 25.0  | 18750 | 0.8320          | 0.9117   |
| 0.0004        | 26.0  | 19500 | 0.9795          | 0.8967   |
| 0.0           | 27.0  | 20250 | 0.8537          | 0.9183   |
| 0.0           | 28.0  | 21000 | 0.8796          | 0.9033   |
| 0.0           | 29.0  | 21750 | 0.8896          | 0.9067   |
| 0.0035        | 30.0  | 22500 | 0.9700          | 0.9033   |
| 0.0           | 31.0  | 23250 | 0.8273          | 0.9117   |
| 0.0           | 32.0  | 24000 | 0.8778          | 0.91     |
| 0.0           | 33.0  | 24750 | 0.8576          | 0.9117   |
| 0.0           | 34.0  | 25500 | 0.8235          | 0.9167   |
| 0.0           | 35.0  | 26250 | 0.8389          | 0.9133   |
| 0.0           | 36.0  | 27000 | 0.8611          | 0.9133   |
| 0.0052        | 37.0  | 27750 | 0.9201          | 0.91     |
| 0.0           | 38.0  | 28500 | 0.9394          | 0.9117   |
| 0.0           | 39.0  | 29250 | 0.9985          | 0.91     |
| 0.0           | 40.0  | 30000 | 0.9682          | 0.9133   |
| 0.0           | 41.0  | 30750 | 0.9333          | 0.915    |
| 0.0           | 42.0  | 31500 | 0.9270          | 0.9167   |
| 0.0           | 43.0  | 32250 | 0.9299          | 0.915    |
| 0.0           | 44.0  | 33000 | 0.9241          | 0.9133   |
| 0.0           | 45.0  | 33750 | 0.9269          | 0.9133   |
| 0.0           | 46.0  | 34500 | 0.9286          | 0.915    |
| 0.0           | 47.0  | 35250 | 0.9293          | 0.915    |
| 0.0           | 48.0  | 36000 | 0.9293          | 0.915    |
| 0.0           | 49.0  | 36750 | 0.9307          | 0.915    |
| 0.0           | 50.0  | 37500 | 0.9311          | 0.9167   |


### Framework versions

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