transformers pytorch vit image-classification generated_from_trainer dataset:imagefolder base_model:facebook/deit-small-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
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_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: 0.8716666666666667
---

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# smids_5x_deit_tiny_sgd_001_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: 0.2848
- Accuracy: 0.8717

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7235        | 1.0   | 375   | 0.7207          | 0.7267   |
| 0.5565        | 2.0   | 750   | 0.5409          | 0.7917   |
| 0.4119        | 3.0   | 1125  | 0.4652          | 0.825    |
| 0.4334        | 4.0   | 1500  | 0.4261          | 0.825    |
| 0.3887        | 5.0   | 1875  | 0.3995          | 0.8317   |
| 0.3453        | 6.0   | 2250  | 0.3796          | 0.8483   |
| 0.3126        | 7.0   | 2625  | 0.3666          | 0.8533   |
| 0.2904        | 8.0   | 3000  | 0.3555          | 0.8533   |
| 0.2918        | 9.0   | 3375  | 0.3426          | 0.8617   |
| 0.2742        | 10.0  | 3750  | 0.3359          | 0.8583   |
| 0.2643        | 11.0  | 4125  | 0.3302          | 0.865    |
| 0.2302        | 12.0  | 4500  | 0.3258          | 0.8617   |
| 0.2469        | 13.0  | 4875  | 0.3202          | 0.8633   |
| 0.2562        | 14.0  | 5250  | 0.3189          | 0.8683   |
| 0.29          | 15.0  | 5625  | 0.3116          | 0.87     |
| 0.1812        | 16.0  | 6000  | 0.3053          | 0.87     |
| 0.297         | 17.0  | 6375  | 0.3040          | 0.8733   |
| 0.2383        | 18.0  | 6750  | 0.3060          | 0.8733   |
| 0.2162        | 19.0  | 7125  | 0.3016          | 0.8683   |
| 0.1713        | 20.0  | 7500  | 0.3023          | 0.8717   |
| 0.1959        | 21.0  | 7875  | 0.2972          | 0.8767   |
| 0.1937        | 22.0  | 8250  | 0.2967          | 0.87     |
| 0.2843        | 23.0  | 8625  | 0.2932          | 0.8683   |
| 0.199         | 24.0  | 9000  | 0.2945          | 0.8717   |
| 0.2226        | 25.0  | 9375  | 0.2906          | 0.875    |
| 0.2091        | 26.0  | 9750  | 0.2867          | 0.8683   |
| 0.2068        | 27.0  | 10125 | 0.2936          | 0.8717   |
| 0.1892        | 28.0  | 10500 | 0.2828          | 0.8767   |
| 0.2214        | 29.0  | 10875 | 0.2884          | 0.8683   |
| 0.1748        | 30.0  | 11250 | 0.2926          | 0.8683   |
| 0.2363        | 31.0  | 11625 | 0.2864          | 0.8733   |
| 0.209         | 32.0  | 12000 | 0.2852          | 0.8733   |
| 0.2053        | 33.0  | 12375 | 0.2863          | 0.8683   |
| 0.2387        | 34.0  | 12750 | 0.2806          | 0.8767   |
| 0.1656        | 35.0  | 13125 | 0.2863          | 0.87     |
| 0.1491        | 36.0  | 13500 | 0.2870          | 0.8667   |
| 0.1628        | 37.0  | 13875 | 0.2841          | 0.87     |
| 0.1814        | 38.0  | 14250 | 0.2851          | 0.8683   |
| 0.1769        | 39.0  | 14625 | 0.2852          | 0.8667   |
| 0.1855        | 40.0  | 15000 | 0.2843          | 0.8717   |
| 0.185         | 41.0  | 15375 | 0.2824          | 0.8717   |
| 0.1482        | 42.0  | 15750 | 0.2829          | 0.8733   |
| 0.2112        | 43.0  | 16125 | 0.2835          | 0.875    |
| 0.2011        | 44.0  | 16500 | 0.2835          | 0.8733   |
| 0.1775        | 45.0  | 16875 | 0.2835          | 0.8717   |
| 0.1816        | 46.0  | 17250 | 0.2845          | 0.8717   |
| 0.1826        | 47.0  | 17625 | 0.2843          | 0.8717   |
| 0.1411        | 48.0  | 18000 | 0.2851          | 0.8683   |
| 0.1734        | 49.0  | 18375 | 0.2850          | 0.8717   |
| 0.1888        | 50.0  | 18750 | 0.2848          | 0.8717   |


### Framework versions

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