---
license: apache-2.0
base_model: facebook/deit-tiny-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-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2648
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7316        | 1.0   | 375   | 0.7849          | 0.6383   |
| 0.5408        | 2.0   | 750   | 0.5416          | 0.805    |
| 0.4144        | 3.0   | 1125  | 0.4477          | 0.8483   |
| 0.4149        | 4.0   | 1500  | 0.3929          | 0.8533   |
| 0.413         | 5.0   | 1875  | 0.3596          | 0.865    |
| 0.347         | 6.0   | 2250  | 0.3405          | 0.87     |
| 0.3766        | 7.0   | 2625  | 0.3237          | 0.885    |
| 0.3353        | 8.0   | 3000  | 0.3140          | 0.8833   |
| 0.2912        | 9.0   | 3375  | 0.3069          | 0.8817   |
| 0.2983        | 10.0  | 3750  | 0.3022          | 0.8883   |
| 0.2271        | 11.0  | 4125  | 0.2992          | 0.8867   |
| 0.2804        | 12.0  | 4500  | 0.2892          | 0.8917   |
| 0.2434        | 13.0  | 4875  | 0.2876          | 0.89     |
| 0.2434        | 14.0  | 5250  | 0.2819          | 0.8883   |
| 0.2457        | 15.0  | 5625  | 0.2817          | 0.8967   |
| 0.2178        | 16.0  | 6000  | 0.2772          | 0.9      |
| 0.2586        | 17.0  | 6375  | 0.2753          | 0.9      |
| 0.2424        | 18.0  | 6750  | 0.2760          | 0.8967   |
| 0.2316        | 19.0  | 7125  | 0.2730          | 0.8967   |
| 0.236         | 20.0  | 7500  | 0.2701          | 0.9033   |
| 0.1785        | 21.0  | 7875  | 0.2679          | 0.9017   |
| 0.1868        | 22.0  | 8250  | 0.2698          | 0.9      |
| 0.2515        | 23.0  | 8625  | 0.2683          | 0.8983   |
| 0.2504        | 24.0  | 9000  | 0.2635          | 0.8967   |
| 0.2044        | 25.0  | 9375  | 0.2645          | 0.9033   |
| 0.2051        | 26.0  | 9750  | 0.2668          | 0.8983   |
| 0.2231        | 27.0  | 10125 | 0.2645          | 0.9033   |
| 0.2003        | 28.0  | 10500 | 0.2627          | 0.8983   |
| 0.1423        | 29.0  | 10875 | 0.2631          | 0.9033   |
| 0.2099        | 30.0  | 11250 | 0.2641          | 0.9      |
| 0.2023        | 31.0  | 11625 | 0.2642          | 0.9      |
| 0.2174        | 32.0  | 12000 | 0.2642          | 0.9      |
| 0.198         | 33.0  | 12375 | 0.2636          | 0.8967   |
| 0.1518        | 34.0  | 12750 | 0.2625          | 0.9033   |
| 0.1375        | 35.0  | 13125 | 0.2629          | 0.9017   |
| 0.1414        | 36.0  | 13500 | 0.2638          | 0.9017   |
| 0.1599        | 37.0  | 13875 | 0.2634          | 0.9033   |
| 0.164         | 38.0  | 14250 | 0.2642          | 0.9      |
| 0.1442        | 39.0  | 14625 | 0.2626          | 0.8983   |
| 0.1928        | 40.0  | 15000 | 0.2641          | 0.9017   |
| 0.1643        | 41.0  | 15375 | 0.2643          | 0.9017   |
| 0.1534        | 42.0  | 15750 | 0.2642          | 0.9017   |
| 0.1818        | 43.0  | 16125 | 0.2644          | 0.9017   |
| 0.1596        | 44.0  | 16500 | 0.2650          | 0.9      |
| 0.1441        | 45.0  | 16875 | 0.2645          | 0.9017   |
| 0.1513        | 46.0  | 17250 | 0.2643          | 0.9      |
| 0.1221        | 47.0  | 17625 | 0.2647          | 0.9      |
| 0.1853        | 48.0  | 18000 | 0.2646          | 0.9      |
| 0.1404        | 49.0  | 18375 | 0.2649          | 0.9      |
| 0.1644        | 50.0  | 18750 | 0.2648          | 0.9      |


### Framework versions

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