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_fold4
  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: 0.43333333333333335
---

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# smids_3x_deit_base_sgd_00001_fold4

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: 1.0826
- Accuracy: 0.4333

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1201        | 1.0   | 225   | 1.1112          | 0.3333   |
| 1.1056        | 2.0   | 450   | 1.1099          | 0.34     |
| 1.0987        | 3.0   | 675   | 1.1086          | 0.3433   |
| 1.1099        | 4.0   | 900   | 1.1074          | 0.355    |
| 1.0994        | 5.0   | 1125  | 1.1062          | 0.3517   |
| 1.106         | 6.0   | 1350  | 1.1051          | 0.3583   |
| 1.1031        | 7.0   | 1575  | 1.1040          | 0.3633   |
| 1.1065        | 8.0   | 1800  | 1.1029          | 0.37     |
| 1.0902        | 9.0   | 2025  | 1.1018          | 0.3683   |
| 1.0803        | 10.0  | 2250  | 1.1008          | 0.3717   |
| 1.0894        | 11.0  | 2475  | 1.0998          | 0.375    |
| 1.095         | 12.0  | 2700  | 1.0989          | 0.3817   |
| 1.0882        | 13.0  | 2925  | 1.0979          | 0.3867   |
| 1.0908        | 14.0  | 3150  | 1.0971          | 0.39     |
| 1.1022        | 15.0  | 3375  | 1.0962          | 0.3917   |
| 1.0922        | 16.0  | 3600  | 1.0954          | 0.395    |
| 1.0943        | 17.0  | 3825  | 1.0946          | 0.3967   |
| 1.0851        | 18.0  | 4050  | 1.0938          | 0.4017   |
| 1.0874        | 19.0  | 4275  | 1.0931          | 0.405    |
| 1.0966        | 20.0  | 4500  | 1.0924          | 0.4083   |
| 1.0868        | 21.0  | 4725  | 1.0917          | 0.4083   |
| 1.0765        | 22.0  | 4950  | 1.0910          | 0.4083   |
| 1.0918        | 23.0  | 5175  | 1.0904          | 0.41     |
| 1.0777        | 24.0  | 5400  | 1.0898          | 0.4183   |
| 1.0939        | 25.0  | 5625  | 1.0892          | 0.42     |
| 1.0798        | 26.0  | 5850  | 1.0886          | 0.4217   |
| 1.0858        | 27.0  | 6075  | 1.0881          | 0.425    |
| 1.061         | 28.0  | 6300  | 1.0876          | 0.4233   |
| 1.083         | 29.0  | 6525  | 1.0871          | 0.425    |
| 1.0868        | 30.0  | 6750  | 1.0867          | 0.425    |
| 1.0886        | 31.0  | 6975  | 1.0862          | 0.4267   |
| 1.0841        | 32.0  | 7200  | 1.0858          | 0.4267   |
| 1.0853        | 33.0  | 7425  | 1.0855          | 0.4283   |
| 1.0704        | 34.0  | 7650  | 1.0851          | 0.4283   |
| 1.0702        | 35.0  | 7875  | 1.0848          | 0.4267   |
| 1.0848        | 36.0  | 8100  | 1.0845          | 0.4283   |
| 1.0671        | 37.0  | 8325  | 1.0842          | 0.4283   |
| 1.0578        | 38.0  | 8550  | 1.0840          | 0.43     |
| 1.0817        | 39.0  | 8775  | 1.0837          | 0.43     |
| 1.0866        | 40.0  | 9000  | 1.0835          | 0.4317   |
| 1.083         | 41.0  | 9225  | 1.0833          | 0.4333   |
| 1.0747        | 42.0  | 9450  | 1.0832          | 0.4333   |
| 1.0816        | 43.0  | 9675  | 1.0830          | 0.4333   |
| 1.0657        | 44.0  | 9900  | 1.0829          | 0.4333   |
| 1.0619        | 45.0  | 10125 | 1.0828          | 0.4333   |
| 1.067         | 46.0  | 10350 | 1.0827          | 0.4333   |
| 1.0593        | 47.0  | 10575 | 1.0827          | 0.4333   |
| 1.0587        | 48.0  | 10800 | 1.0826          | 0.4333   |
| 1.0675        | 49.0  | 11025 | 1.0826          | 0.4333   |
| 1.0632        | 50.0  | 11250 | 1.0826          | 0.4333   |


### Framework versions

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