transformers pytorch vit image-classification generated_from_trainer dataset:imagefolder base_model:facebook/deit-tiny-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_3x_deit_tiny_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: 0.40266222961730447
---

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# smids_3x_deit_tiny_sgd_00001_fold2

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: 1.1011
- Accuracy: 0.4027

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2975        | 1.0   | 225   | 1.3313          | 0.3461   |
| 1.3535        | 2.0   | 450   | 1.3098          | 0.3411   |
| 1.364         | 3.0   | 675   | 1.2899          | 0.3411   |
| 1.2928        | 4.0   | 900   | 1.2721          | 0.3411   |
| 1.2627        | 5.0   | 1125  | 1.2559          | 0.3378   |
| 1.2073        | 6.0   | 1350  | 1.2414          | 0.3461   |
| 1.3184        | 7.0   | 1575  | 1.2280          | 0.3527   |
| 1.2058        | 8.0   | 1800  | 1.2162          | 0.3527   |
| 1.2305        | 9.0   | 2025  | 1.2057          | 0.3511   |
| 1.2453        | 10.0  | 2250  | 1.1960          | 0.3478   |
| 1.1822        | 11.0  | 2475  | 1.1875          | 0.3461   |
| 1.1856        | 12.0  | 2700  | 1.1797          | 0.3561   |
| 1.1979        | 13.0  | 2925  | 1.1728          | 0.3661   |
| 1.1589        | 14.0  | 3150  | 1.1665          | 0.3644   |
| 1.1625        | 15.0  | 3375  | 1.1608          | 0.3677   |
| 1.1751        | 16.0  | 3600  | 1.1557          | 0.3744   |
| 1.1846        | 17.0  | 3825  | 1.1510          | 0.3760   |
| 1.1541        | 18.0  | 4050  | 1.1466          | 0.3744   |
| 1.1807        | 19.0  | 4275  | 1.1426          | 0.3727   |
| 1.1744        | 20.0  | 4500  | 1.1389          | 0.3710   |
| 1.1694        | 21.0  | 4725  | 1.1356          | 0.3710   |
| 1.1819        | 22.0  | 4950  | 1.1325          | 0.3727   |
| 1.1574        | 23.0  | 5175  | 1.1297          | 0.3794   |
| 1.159         | 24.0  | 5400  | 1.1270          | 0.3760   |
| 1.1656        | 25.0  | 5625  | 1.1246          | 0.3760   |
| 1.1491        | 26.0  | 5850  | 1.1224          | 0.3777   |
| 1.1877        | 27.0  | 6075  | 1.1202          | 0.3760   |
| 1.1245        | 28.0  | 6300  | 1.1183          | 0.3810   |
| 1.1465        | 29.0  | 6525  | 1.1164          | 0.3877   |
| 1.0989        | 30.0  | 6750  | 1.1147          | 0.3910   |
| 1.1019        | 31.0  | 6975  | 1.1132          | 0.3927   |
| 1.1115        | 32.0  | 7200  | 1.1117          | 0.3927   |
| 1.1193        | 33.0  | 7425  | 1.1103          | 0.3943   |
| 1.1111        | 34.0  | 7650  | 1.1091          | 0.3960   |
| 1.1163        | 35.0  | 7875  | 1.1080          | 0.3977   |
| 1.1433        | 36.0  | 8100  | 1.1069          | 0.3993   |
| 1.0817        | 37.0  | 8325  | 1.1060          | 0.3993   |
| 1.1389        | 38.0  | 8550  | 1.1052          | 0.3993   |
| 1.1196        | 39.0  | 8775  | 1.1044          | 0.4027   |
| 1.1051        | 40.0  | 9000  | 1.1037          | 0.4043   |
| 1.1003        | 41.0  | 9225  | 1.1031          | 0.4027   |
| 1.1259        | 42.0  | 9450  | 1.1026          | 0.4027   |
| 1.1127        | 43.0  | 9675  | 1.1022          | 0.4027   |
| 1.1252        | 44.0  | 9900  | 1.1018          | 0.4010   |
| 1.0665        | 45.0  | 10125 | 1.1016          | 0.4027   |
| 1.1219        | 46.0  | 10350 | 1.1014          | 0.4027   |
| 1.1281        | 47.0  | 10575 | 1.1012          | 0.4027   |
| 1.0847        | 48.0  | 10800 | 1.1011          | 0.4027   |
| 1.1349        | 49.0  | 11025 | 1.1011          | 0.4027   |
| 1.1316        | 50.0  | 11250 | 1.1011          | 0.4027   |


### Framework versions

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