---
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_fold3
  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: 0.405
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# smids_3x_deit_tiny_sgd_00001_fold3

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: 1.1190
- Accuracy: 0.405

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.3567        | 1.0   | 225   | 1.3548          | 0.3467   |
| 1.2325        | 2.0   | 450   | 1.3329          | 0.345    |
| 1.3433        | 3.0   | 675   | 1.3131          | 0.3467   |
| 1.2405        | 4.0   | 900   | 1.2949          | 0.3467   |
| 1.3135        | 5.0   | 1125  | 1.2785          | 0.3533   |
| 1.2715        | 6.0   | 1350  | 1.2636          | 0.36     |
| 1.2599        | 7.0   | 1575  | 1.2502          | 0.3583   |
| 1.2399        | 8.0   | 1800  | 1.2380          | 0.365    |
| 1.2555        | 9.0   | 2025  | 1.2270          | 0.3667   |
| 1.2073        | 10.0  | 2250  | 1.2172          | 0.38     |
| 1.2054        | 11.0  | 2475  | 1.2082          | 0.3767   |
| 1.2574        | 12.0  | 2700  | 1.2001          | 0.3817   |
| 1.1461        | 13.0  | 2925  | 1.1929          | 0.385    |
| 1.1651        | 14.0  | 3150  | 1.1863          | 0.3883   |
| 1.2048        | 15.0  | 3375  | 1.1803          | 0.39     |
| 1.1932        | 16.0  | 3600  | 1.1750          | 0.395    |
| 1.1333        | 17.0  | 3825  | 1.1701          | 0.3967   |
| 1.1551        | 18.0  | 4050  | 1.1656          | 0.3917   |
| 1.1804        | 19.0  | 4275  | 1.1615          | 0.3933   |
| 1.1749        | 20.0  | 4500  | 1.1578          | 0.39     |
| 1.1596        | 21.0  | 4725  | 1.1543          | 0.39     |
| 1.1144        | 22.0  | 4950  | 1.1511          | 0.39     |
| 1.1024        | 23.0  | 5175  | 1.1481          | 0.395    |
| 1.1547        | 24.0  | 5400  | 1.1454          | 0.3967   |
| 1.1295        | 25.0  | 5625  | 1.1429          | 0.395    |
| 1.1235        | 26.0  | 5850  | 1.1405          | 0.3983   |
| 1.0919        | 27.0  | 6075  | 1.1383          | 0.3983   |
| 1.0892        | 28.0  | 6300  | 1.1364          | 0.4033   |
| 1.1395        | 29.0  | 6525  | 1.1345          | 0.4017   |
| 1.1334        | 30.0  | 6750  | 1.1328          | 0.4      |
| 1.0988        | 31.0  | 6975  | 1.1312          | 0.405    |
| 1.1324        | 32.0  | 7200  | 1.1297          | 0.4067   |
| 1.1023        | 33.0  | 7425  | 1.1284          | 0.41     |
| 1.1357        | 34.0  | 7650  | 1.1271          | 0.4083   |
| 1.1037        | 35.0  | 7875  | 1.1259          | 0.405    |
| 1.1207        | 36.0  | 8100  | 1.1249          | 0.4033   |
| 1.1299        | 37.0  | 8325  | 1.1239          | 0.4033   |
| 1.1551        | 38.0  | 8550  | 1.1231          | 0.405    |
| 1.1268        | 39.0  | 8775  | 1.1223          | 0.4067   |
| 1.1415        | 40.0  | 9000  | 1.1216          | 0.405    |
| 1.1269        | 41.0  | 9225  | 1.1210          | 0.4067   |
| 1.1595        | 42.0  | 9450  | 1.1205          | 0.4067   |
| 1.1387        | 43.0  | 9675  | 1.1201          | 0.4067   |
| 1.0676        | 44.0  | 9900  | 1.1198          | 0.4067   |
| 1.1227        | 45.0  | 10125 | 1.1195          | 0.4067   |
| 1.0985        | 46.0  | 10350 | 1.1193          | 0.4067   |
| 1.0755        | 47.0  | 10575 | 1.1191          | 0.405    |
| 1.0965        | 48.0  | 10800 | 1.1190          | 0.405    |
| 1.1228        | 49.0  | 11025 | 1.1190          | 0.405    |
| 1.0829        | 50.0  | 11250 | 1.1190          | 0.405    |


### Framework versions

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