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_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: 0.39
---

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# smids_3x_deit_base_sgd_00001_fold5

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: 1.0839
- Accuracy: 0.39

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1036        | 1.0   | 225   | 1.1105          | 0.345    |
| 1.1373        | 2.0   | 450   | 1.1092          | 0.3483   |
| 1.1157        | 3.0   | 675   | 1.1081          | 0.345    |
| 1.0955        | 4.0   | 900   | 1.1069          | 0.345    |
| 1.1099        | 5.0   | 1125  | 1.1058          | 0.3467   |
| 1.1089        | 6.0   | 1350  | 1.1047          | 0.345    |
| 1.1067        | 7.0   | 1575  | 1.1037          | 0.35     |
| 1.0974        | 8.0   | 1800  | 1.1027          | 0.3483   |
| 1.103         | 9.0   | 2025  | 1.1017          | 0.3483   |
| 1.0886        | 10.0  | 2250  | 1.1008          | 0.35     |
| 1.1042        | 11.0  | 2475  | 1.0999          | 0.3467   |
| 1.0817        | 12.0  | 2700  | 1.0990          | 0.3483   |
| 1.0974        | 13.0  | 2925  | 1.0981          | 0.35     |
| 1.0843        | 14.0  | 3150  | 1.0973          | 0.3533   |
| 1.0853        | 15.0  | 3375  | 1.0965          | 0.3567   |
| 1.0875        | 16.0  | 3600  | 1.0957          | 0.355    |
| 1.101         | 17.0  | 3825  | 1.0950          | 0.3517   |
| 1.0772        | 18.0  | 4050  | 1.0943          | 0.3533   |
| 1.0926        | 19.0  | 4275  | 1.0936          | 0.355    |
| 1.1029        | 20.0  | 4500  | 1.0929          | 0.3567   |
| 1.0868        | 21.0  | 4725  | 1.0923          | 0.3567   |
| 1.0978        | 22.0  | 4950  | 1.0917          | 0.3617   |
| 1.0872        | 23.0  | 5175  | 1.0911          | 0.3633   |
| 1.0922        | 24.0  | 5400  | 1.0905          | 0.3717   |
| 1.0864        | 25.0  | 5625  | 1.0900          | 0.3717   |
| 1.0678        | 26.0  | 5850  | 1.0895          | 0.3733   |
| 1.0684        | 27.0  | 6075  | 1.0890          | 0.3767   |
| 1.0793        | 28.0  | 6300  | 1.0885          | 0.3767   |
| 1.0972        | 29.0  | 6525  | 1.0881          | 0.38     |
| 1.0711        | 30.0  | 6750  | 1.0877          | 0.38     |
| 1.0882        | 31.0  | 6975  | 1.0873          | 0.3783   |
| 1.0634        | 32.0  | 7200  | 1.0869          | 0.38     |
| 1.0851        | 33.0  | 7425  | 1.0865          | 0.3783   |
| 1.0775        | 34.0  | 7650  | 1.0862          | 0.38     |
| 1.0604        | 35.0  | 7875  | 1.0859          | 0.3783   |
| 1.0657        | 36.0  | 8100  | 1.0856          | 0.38     |
| 1.0791        | 37.0  | 8325  | 1.0854          | 0.3817   |
| 1.0734        | 38.0  | 8550  | 1.0851          | 0.3817   |
| 1.0719        | 39.0  | 8775  | 1.0849          | 0.3867   |
| 1.0762        | 40.0  | 9000  | 1.0847          | 0.3883   |
| 1.074         | 41.0  | 9225  | 1.0846          | 0.3883   |
| 1.0744        | 42.0  | 9450  | 1.0844          | 0.3883   |
| 1.0769        | 43.0  | 9675  | 1.0843          | 0.3883   |
| 1.079         | 44.0  | 9900  | 1.0842          | 0.3883   |
| 1.0661        | 45.0  | 10125 | 1.0841          | 0.3883   |
| 1.0565        | 46.0  | 10350 | 1.0840          | 0.3883   |
| 1.071         | 47.0  | 10575 | 1.0840          | 0.3883   |
| 1.0641        | 48.0  | 10800 | 1.0839          | 0.39     |
| 1.0708        | 49.0  | 11025 | 1.0839          | 0.39     |
| 1.0689        | 50.0  | 11250 | 1.0839          | 0.39     |


### Framework versions

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