---
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_deit_small_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: 0.48833333333333334
---

<!-- 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_5x_deit_small_sgd_00001_fold3

This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co./facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0078
- Accuracy: 0.4883

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0625        | 1.0   | 375   | 1.0854          | 0.38     |
| 1.055         | 2.0   | 750   | 1.0820          | 0.3817   |
| 1.0441        | 3.0   | 1125  | 1.0787          | 0.3817   |
| 1.0543        | 4.0   | 1500  | 1.0755          | 0.3833   |
| 1.0717        | 5.0   | 1875  | 1.0724          | 0.3833   |
| 1.0405        | 6.0   | 2250  | 1.0694          | 0.3833   |
| 1.0573        | 7.0   | 2625  | 1.0664          | 0.3867   |
| 1.052         | 8.0   | 3000  | 1.0635          | 0.3933   |
| 1.0402        | 9.0   | 3375  | 1.0606          | 0.395    |
| 1.026         | 10.0  | 3750  | 1.0579          | 0.3967   |
| 1.0363        | 11.0  | 4125  | 1.0552          | 0.4017   |
| 1.044         | 12.0  | 4500  | 1.0526          | 0.4033   |
| 1.0227        | 13.0  | 4875  | 1.0501          | 0.4117   |
| 1.0237        | 14.0  | 5250  | 1.0477          | 0.4133   |
| 1.0137        | 15.0  | 5625  | 1.0453          | 0.4183   |
| 1.005         | 16.0  | 6000  | 1.0431          | 0.4167   |
| 1.0298        | 17.0  | 6375  | 1.0409          | 0.4167   |
| 1.0209        | 18.0  | 6750  | 1.0387          | 0.4183   |
| 1.0296        | 19.0  | 7125  | 1.0366          | 0.425    |
| 1.0081        | 20.0  | 7500  | 1.0346          | 0.4283   |
| 0.9849        | 21.0  | 7875  | 1.0327          | 0.4317   |
| 1.0033        | 22.0  | 8250  | 1.0308          | 0.44     |
| 1.0003        | 23.0  | 8625  | 1.0290          | 0.4417   |
| 1.0236        | 24.0  | 9000  | 1.0274          | 0.445    |
| 0.9768        | 25.0  | 9375  | 1.0257          | 0.4533   |
| 0.9963        | 26.0  | 9750  | 1.0242          | 0.4567   |
| 0.9973        | 27.0  | 10125 | 1.0227          | 0.46     |
| 1.025         | 28.0  | 10500 | 1.0213          | 0.4617   |
| 0.9786        | 29.0  | 10875 | 1.0199          | 0.465    |
| 1.0006        | 30.0  | 11250 | 1.0187          | 0.4667   |
| 1.0183        | 31.0  | 11625 | 1.0175          | 0.47     |
| 0.9871        | 32.0  | 12000 | 1.0164          | 0.4733   |
| 0.9751        | 33.0  | 12375 | 1.0154          | 0.4733   |
| 0.9558        | 34.0  | 12750 | 1.0144          | 0.475    |
| 0.9521        | 35.0  | 13125 | 1.0135          | 0.475    |
| 0.975         | 36.0  | 13500 | 1.0127          | 0.475    |
| 0.9912        | 37.0  | 13875 | 1.0119          | 0.4783   |
| 0.9818        | 38.0  | 14250 | 1.0112          | 0.48     |
| 0.9973        | 39.0  | 14625 | 1.0106          | 0.4817   |
| 0.9737        | 40.0  | 15000 | 1.0101          | 0.4833   |
| 0.9571        | 41.0  | 15375 | 1.0096          | 0.4833   |
| 0.9497        | 42.0  | 15750 | 1.0092          | 0.4833   |
| 0.9898        | 43.0  | 16125 | 1.0088          | 0.485    |
| 0.9733        | 44.0  | 16500 | 1.0085          | 0.485    |
| 0.9695        | 45.0  | 16875 | 1.0083          | 0.4833   |
| 0.9603        | 46.0  | 17250 | 1.0081          | 0.4867   |
| 0.9924        | 47.0  | 17625 | 1.0079          | 0.4867   |
| 0.9781        | 48.0  | 18000 | 1.0079          | 0.4867   |
| 1.0064        | 49.0  | 18375 | 1.0078          | 0.4883   |
| 0.9488        | 50.0  | 18750 | 1.0078          | 0.4883   |


### Framework versions

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