---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_1x_deit_tiny_sgd_0001_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: 0.5916666666666667
---

<!-- 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_1x_deit_tiny_sgd_0001_fold5

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: 0.8882
- Accuracy: 0.5917

## 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: 0.0001
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2279        | 1.0   | 75   | 1.2828          | 0.36     |
| 1.1801        | 2.0   | 150  | 1.2204          | 0.3717   |
| 1.1425        | 3.0   | 225  | 1.1764          | 0.3733   |
| 1.1496        | 4.0   | 300  | 1.1467          | 0.3817   |
| 1.0862        | 5.0   | 375  | 1.1259          | 0.39     |
| 1.1317        | 6.0   | 450  | 1.1103          | 0.3983   |
| 1.0711        | 7.0   | 525  | 1.0972          | 0.4083   |
| 1.0717        | 8.0   | 600  | 1.0858          | 0.4167   |
| 1.0458        | 9.0   | 675  | 1.0754          | 0.42     |
| 1.0711        | 10.0  | 750  | 1.0656          | 0.425    |
| 1.0389        | 11.0  | 825  | 1.0563          | 0.4383   |
| 1.0272        | 12.0  | 900  | 1.0476          | 0.4467   |
| 1.0495        | 13.0  | 975  | 1.0393          | 0.4517   |
| 1.0448        | 14.0  | 1050 | 1.0308          | 0.4533   |
| 1.0339        | 15.0  | 1125 | 1.0229          | 0.4583   |
| 0.9744        | 16.0  | 1200 | 1.0150          | 0.4617   |
| 0.9857        | 17.0  | 1275 | 1.0069          | 0.47     |
| 1.0108        | 18.0  | 1350 | 0.9993          | 0.4717   |
| 0.9584        | 19.0  | 1425 | 0.9919          | 0.4717   |
| 0.9977        | 20.0  | 1500 | 0.9844          | 0.485    |
| 0.9787        | 21.0  | 1575 | 0.9775          | 0.49     |
| 0.9724        | 22.0  | 1650 | 0.9707          | 0.5067   |
| 0.9219        | 23.0  | 1725 | 0.9645          | 0.515    |
| 0.923         | 24.0  | 1800 | 0.9585          | 0.525    |
| 0.9224        | 25.0  | 1875 | 0.9527          | 0.5317   |
| 0.9312        | 26.0  | 1950 | 0.9470          | 0.5417   |
| 0.9161        | 27.0  | 2025 | 0.9417          | 0.5433   |
| 0.9574        | 28.0  | 2100 | 0.9369          | 0.5467   |
| 0.9255        | 29.0  | 2175 | 0.9322          | 0.5517   |
| 0.9146        | 30.0  | 2250 | 0.9278          | 0.555    |
| 0.9155        | 31.0  | 2325 | 0.9238          | 0.5617   |
| 0.856         | 32.0  | 2400 | 0.9200          | 0.565    |
| 0.9504        | 33.0  | 2475 | 0.9164          | 0.5717   |
| 0.9096        | 34.0  | 2550 | 0.9130          | 0.5783   |
| 0.8983        | 35.0  | 2625 | 0.9100          | 0.5817   |
| 0.8589        | 36.0  | 2700 | 0.9071          | 0.585    |
| 0.8916        | 37.0  | 2775 | 0.9044          | 0.5817   |
| 0.8984        | 38.0  | 2850 | 0.9020          | 0.585    |
| 0.8824        | 39.0  | 2925 | 0.8998          | 0.5867   |
| 0.8736        | 40.0  | 3000 | 0.8977          | 0.5867   |
| 0.8723        | 41.0  | 3075 | 0.8958          | 0.5883   |
| 0.8965        | 42.0  | 3150 | 0.8942          | 0.59     |
| 0.8854        | 43.0  | 3225 | 0.8928          | 0.59     |
| 0.8622        | 44.0  | 3300 | 0.8915          | 0.5917   |
| 0.8601        | 45.0  | 3375 | 0.8905          | 0.5917   |
| 0.8904        | 46.0  | 3450 | 0.8896          | 0.5917   |
| 0.8654        | 47.0  | 3525 | 0.8890          | 0.5917   |
| 0.8638        | 48.0  | 3600 | 0.8885          | 0.5917   |
| 0.8282        | 49.0  | 3675 | 0.8883          | 0.5917   |
| 0.8485        | 50.0  | 3750 | 0.8882          | 0.5917   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0