---
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_sgd_001_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: 0.9
---

<!-- 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_sgd_001_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: 0.2814
- Accuracy: 0.9

## 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: 0.001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7088        | 1.0   | 375   | 0.7565          | 0.725    |
| 0.5465        | 2.0   | 750   | 0.5503          | 0.8      |
| 0.3877        | 3.0   | 1125  | 0.4580          | 0.8417   |
| 0.403         | 4.0   | 1500  | 0.4113          | 0.86     |
| 0.4127        | 5.0   | 1875  | 0.3842          | 0.8633   |
| 0.3531        | 6.0   | 2250  | 0.3637          | 0.8717   |
| 0.3784        | 7.0   | 2625  | 0.3477          | 0.8717   |
| 0.3315        | 8.0   | 3000  | 0.3357          | 0.88     |
| 0.281         | 9.0   | 3375  | 0.3284          | 0.8817   |
| 0.2976        | 10.0  | 3750  | 0.3184          | 0.8783   |
| 0.2299        | 11.0  | 4125  | 0.3116          | 0.8817   |
| 0.2672        | 12.0  | 4500  | 0.3085          | 0.8867   |
| 0.268         | 13.0  | 4875  | 0.3033          | 0.89     |
| 0.2659        | 14.0  | 5250  | 0.3003          | 0.89     |
| 0.2573        | 15.0  | 5625  | 0.2955          | 0.885    |
| 0.2268        | 16.0  | 6000  | 0.2941          | 0.8917   |
| 0.2876        | 17.0  | 6375  | 0.2932          | 0.895    |
| 0.2734        | 18.0  | 6750  | 0.2907          | 0.8933   |
| 0.2415        | 19.0  | 7125  | 0.2883          | 0.8933   |
| 0.2097        | 20.0  | 7500  | 0.2877          | 0.895    |
| 0.1726        | 21.0  | 7875  | 0.2847          | 0.895    |
| 0.1753        | 22.0  | 8250  | 0.2862          | 0.8983   |
| 0.2074        | 23.0  | 8625  | 0.2848          | 0.9017   |
| 0.238         | 24.0  | 9000  | 0.2832          | 0.8983   |
| 0.2264        | 25.0  | 9375  | 0.2826          | 0.9017   |
| 0.2196        | 26.0  | 9750  | 0.2825          | 0.9017   |
| 0.1923        | 27.0  | 10125 | 0.2817          | 0.9017   |
| 0.2238        | 28.0  | 10500 | 0.2812          | 0.9033   |
| 0.1599        | 29.0  | 10875 | 0.2828          | 0.9      |
| 0.1957        | 30.0  | 11250 | 0.2818          | 0.9017   |
| 0.2026        | 31.0  | 11625 | 0.2804          | 0.905    |
| 0.2064        | 32.0  | 12000 | 0.2838          | 0.9      |
| 0.2244        | 33.0  | 12375 | 0.2835          | 0.9      |
| 0.17          | 34.0  | 12750 | 0.2797          | 0.8967   |
| 0.1503        | 35.0  | 13125 | 0.2806          | 0.9033   |
| 0.1857        | 36.0  | 13500 | 0.2802          | 0.9017   |
| 0.1698        | 37.0  | 13875 | 0.2813          | 0.9017   |
| 0.1679        | 38.0  | 14250 | 0.2797          | 0.9033   |
| 0.1579        | 39.0  | 14625 | 0.2818          | 0.9017   |
| 0.1487        | 40.0  | 15000 | 0.2812          | 0.9      |
| 0.1671        | 41.0  | 15375 | 0.2807          | 0.9      |
| 0.1398        | 42.0  | 15750 | 0.2815          | 0.9017   |
| 0.1981        | 43.0  | 16125 | 0.2800          | 0.9017   |
| 0.1508        | 44.0  | 16500 | 0.2816          | 0.9017   |
| 0.17          | 45.0  | 16875 | 0.2807          | 0.9      |
| 0.1582        | 46.0  | 17250 | 0.2810          | 0.9      |
| 0.197         | 47.0  | 17625 | 0.2812          | 0.9      |
| 0.1587        | 48.0  | 18000 | 0.2815          | 0.9017   |
| 0.1477        | 49.0  | 18375 | 0.2815          | 0.9      |
| 0.1575        | 50.0  | 18750 | 0.2814          | 0.9      |


### Framework versions

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