---
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_deit_tiny_adamax_00001_fold2
  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: 0.8752079866888519
---

<!-- 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_5x_deit_tiny_adamax_00001_fold2

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: 1.0539
- Accuracy: 0.8752

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3245        | 1.0   | 375   | 0.3357          | 0.8586   |
| 0.2435        | 2.0   | 750   | 0.3012          | 0.8802   |
| 0.1837        | 3.0   | 1125  | 0.3092          | 0.8802   |
| 0.0922        | 4.0   | 1500  | 0.3362          | 0.8719   |
| 0.064         | 5.0   | 1875  | 0.4063          | 0.8619   |
| 0.0948        | 6.0   | 2250  | 0.4674          | 0.8619   |
| 0.0452        | 7.0   | 2625  | 0.5334          | 0.8602   |
| 0.0373        | 8.0   | 3000  | 0.6077          | 0.8619   |
| 0.0111        | 9.0   | 3375  | 0.6364          | 0.8769   |
| 0.0018        | 10.0  | 3750  | 0.7083          | 0.8636   |
| 0.0038        | 11.0  | 4125  | 0.7404          | 0.8752   |
| 0.0175        | 12.0  | 4500  | 0.8300          | 0.8719   |
| 0.0012        | 13.0  | 4875  | 0.8986          | 0.8652   |
| 0.0087        | 14.0  | 5250  | 0.8825          | 0.8686   |
| 0.004         | 15.0  | 5625  | 0.8822          | 0.8785   |
| 0.0001        | 16.0  | 6000  | 0.9237          | 0.8735   |
| 0.0162        | 17.0  | 6375  | 0.9830          | 0.8619   |
| 0.0           | 18.0  | 6750  | 1.0120          | 0.8702   |
| 0.0           | 19.0  | 7125  | 1.0192          | 0.8719   |
| 0.0001        | 20.0  | 7500  | 0.9781          | 0.8735   |
| 0.0           | 21.0  | 7875  | 1.0188          | 0.8702   |
| 0.0           | 22.0  | 8250  | 0.9776          | 0.8735   |
| 0.0           | 23.0  | 8625  | 1.0494          | 0.8702   |
| 0.0           | 24.0  | 9000  | 0.9531          | 0.8752   |
| 0.0           | 25.0  | 9375  | 1.0293          | 0.8719   |
| 0.0           | 26.0  | 9750  | 1.0427          | 0.8652   |
| 0.0           | 27.0  | 10125 | 1.0483          | 0.8719   |
| 0.0           | 28.0  | 10500 | 1.0202          | 0.8735   |
| 0.0           | 29.0  | 10875 | 1.0779          | 0.8686   |
| 0.0           | 30.0  | 11250 | 1.0065          | 0.8719   |
| 0.0018        | 31.0  | 11625 | 1.0762          | 0.8702   |
| 0.0202        | 32.0  | 12000 | 1.0874          | 0.8669   |
| 0.0024        | 33.0  | 12375 | 1.0366          | 0.8735   |
| 0.0           | 34.0  | 12750 | 1.1165          | 0.8686   |
| 0.0           | 35.0  | 13125 | 1.0244          | 0.8752   |
| 0.0           | 36.0  | 13500 | 1.1014          | 0.8719   |
| 0.0           | 37.0  | 13875 | 1.0995          | 0.8702   |
| 0.0           | 38.0  | 14250 | 1.1070          | 0.8719   |
| 0.0           | 39.0  | 14625 | 1.0209          | 0.8769   |
| 0.0048        | 40.0  | 15000 | 1.0540          | 0.8752   |
| 0.0           | 41.0  | 15375 | 1.0624          | 0.8752   |
| 0.0015        | 42.0  | 15750 | 1.0637          | 0.8752   |
| 0.0013        | 43.0  | 16125 | 1.0536          | 0.8752   |
| 0.0013        | 44.0  | 16500 | 1.0479          | 0.8752   |
| 0.0013        | 45.0  | 16875 | 1.0540          | 0.8752   |
| 0.0           | 46.0  | 17250 | 1.0694          | 0.8752   |
| 0.0016        | 47.0  | 17625 | 1.0601          | 0.8752   |
| 0.0           | 48.0  | 18000 | 1.0596          | 0.8752   |
| 0.0013        | 49.0  | 18375 | 1.0574          | 0.8752   |
| 0.0012        | 50.0  | 18750 | 1.0539          | 0.8752   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2