transformers pytorch vit image-classification generated_from_trainer dataset:imagefolder base_model:facebook/deit-base-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: facebook/deit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_3x_deit_base_sgd_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: 0.39600665557404324
---

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# smids_3x_deit_base_sgd_00001_fold2

This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0800
- Accuracy: 0.3960

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1076        | 1.0   | 225   | 1.1068          | 0.3378   |
| 1.1268        | 2.0   | 450   | 1.1056          | 0.3344   |
| 1.1054        | 3.0   | 675   | 1.1044          | 0.3378   |
| 1.1083        | 4.0   | 900   | 1.1032          | 0.3361   |
| 1.1053        | 5.0   | 1125  | 1.1021          | 0.3344   |
| 1.0996        | 6.0   | 1350  | 1.1010          | 0.3378   |
| 1.1167        | 7.0   | 1575  | 1.0999          | 0.3461   |
| 1.1117        | 8.0   | 1800  | 1.0989          | 0.3478   |
| 1.1001        | 9.0   | 2025  | 1.0979          | 0.3494   |
| 1.0985        | 10.0  | 2250  | 1.0970          | 0.3527   |
| 1.088         | 11.0  | 2475  | 1.0961          | 0.3561   |
| 1.1055        | 12.0  | 2700  | 1.0952          | 0.3594   |
| 1.1011        | 13.0  | 2925  | 1.0943          | 0.3661   |
| 1.0919        | 14.0  | 3150  | 1.0935          | 0.3727   |
| 1.097         | 15.0  | 3375  | 1.0927          | 0.3727   |
| 1.1083        | 16.0  | 3600  | 1.0919          | 0.3744   |
| 1.099         | 17.0  | 3825  | 1.0911          | 0.3727   |
| 1.0873        | 18.0  | 4050  | 1.0904          | 0.3710   |
| 1.0775        | 19.0  | 4275  | 1.0897          | 0.3694   |
| 1.1063        | 20.0  | 4500  | 1.0890          | 0.3677   |
| 1.0977        | 21.0  | 4725  | 1.0884          | 0.3710   |
| 1.0779        | 22.0  | 4950  | 1.0878          | 0.3727   |
| 1.0723        | 23.0  | 5175  | 1.0872          | 0.3694   |
| 1.0778        | 24.0  | 5400  | 1.0866          | 0.3727   |
| 1.0917        | 25.0  | 5625  | 1.0861          | 0.3727   |
| 1.0896        | 26.0  | 5850  | 1.0856          | 0.3744   |
| 1.0859        | 27.0  | 6075  | 1.0851          | 0.3760   |
| 1.0933        | 28.0  | 6300  | 1.0846          | 0.3760   |
| 1.0926        | 29.0  | 6525  | 1.0842          | 0.3794   |
| 1.0799        | 30.0  | 6750  | 1.0838          | 0.3810   |
| 1.0805        | 31.0  | 6975  | 1.0834          | 0.3827   |
| 1.078         | 32.0  | 7200  | 1.0830          | 0.3810   |
| 1.0795        | 33.0  | 7425  | 1.0827          | 0.3860   |
| 1.0907        | 34.0  | 7650  | 1.0823          | 0.3860   |
| 1.0893        | 35.0  | 7875  | 1.0820          | 0.3860   |
| 1.0658        | 36.0  | 8100  | 1.0818          | 0.3894   |
| 1.0887        | 37.0  | 8325  | 1.0815          | 0.3910   |
| 1.0779        | 38.0  | 8550  | 1.0813          | 0.3910   |
| 1.0768        | 39.0  | 8775  | 1.0810          | 0.3927   |
| 1.089         | 40.0  | 9000  | 1.0809          | 0.3927   |
| 1.0766        | 41.0  | 9225  | 1.0807          | 0.3927   |
| 1.0713        | 42.0  | 9450  | 1.0805          | 0.3927   |
| 1.0786        | 43.0  | 9675  | 1.0804          | 0.3927   |
| 1.0932        | 44.0  | 9900  | 1.0803          | 0.3927   |
| 1.0641        | 45.0  | 10125 | 1.0802          | 0.3943   |
| 1.0516        | 46.0  | 10350 | 1.0801          | 0.3943   |
| 1.0624        | 47.0  | 10575 | 1.0801          | 0.3960   |
| 1.0722        | 48.0  | 10800 | 1.0801          | 0.3960   |
| 1.0714        | 49.0  | 11025 | 1.0800          | 0.3960   |
| 1.0822        | 50.0  | 11250 | 1.0800          | 0.3960   |


### Framework versions

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