---
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_rms_001_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: 0.765
---

<!-- 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_rms_001_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: 1.8052
- Accuracy: 0.765

## 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: 0.001
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2535        | 1.0   | 75   | 0.9734          | 0.4717   |
| 1.0172        | 2.0   | 150  | 0.8857          | 0.5217   |
| 0.9205        | 3.0   | 225  | 0.8219          | 0.5633   |
| 0.8404        | 4.0   | 300  | 0.8833          | 0.54     |
| 0.8125        | 5.0   | 375  | 0.7752          | 0.615    |
| 0.8375        | 6.0   | 450  | 0.7791          | 0.6133   |
| 0.7706        | 7.0   | 525  | 0.7651          | 0.6433   |
| 0.6843        | 8.0   | 600  | 0.7674          | 0.6083   |
| 0.717         | 9.0   | 675  | 0.7318          | 0.655    |
| 0.6266        | 10.0  | 750  | 0.7160          | 0.6867   |
| 0.674         | 11.0  | 825  | 0.6761          | 0.69     |
| 0.6618        | 12.0  | 900  | 0.7236          | 0.6433   |
| 0.6204        | 13.0  | 975  | 0.7093          | 0.6733   |
| 0.6403        | 14.0  | 1050 | 0.6526          | 0.7133   |
| 0.5728        | 15.0  | 1125 | 0.7313          | 0.6617   |
| 0.5566        | 16.0  | 1200 | 0.6152          | 0.7317   |
| 0.5735        | 17.0  | 1275 | 0.6901          | 0.7083   |
| 0.6111        | 18.0  | 1350 | 0.6429          | 0.7317   |
| 0.6075        | 19.0  | 1425 | 0.6044          | 0.7533   |
| 0.5675        | 20.0  | 1500 | 0.5922          | 0.7633   |
| 0.4747        | 21.0  | 1575 | 0.6118          | 0.7483   |
| 0.5157        | 22.0  | 1650 | 0.6322          | 0.7383   |
| 0.4995        | 23.0  | 1725 | 0.6300          | 0.745    |
| 0.4632        | 24.0  | 1800 | 0.6076          | 0.74     |
| 0.4596        | 25.0  | 1875 | 0.6047          | 0.7733   |
| 0.4702        | 26.0  | 1950 | 0.6096          | 0.7633   |
| 0.5043        | 27.0  | 2025 | 0.6045          | 0.7567   |
| 0.5051        | 28.0  | 2100 | 0.5905          | 0.75     |
| 0.4664        | 29.0  | 2175 | 0.6085          | 0.7567   |
| 0.3949        | 30.0  | 2250 | 0.6634          | 0.76     |
| 0.3708        | 31.0  | 2325 | 0.6461          | 0.7667   |
| 0.3964        | 32.0  | 2400 | 0.6482          | 0.7617   |
| 0.3827        | 33.0  | 2475 | 0.6696          | 0.76     |
| 0.3422        | 34.0  | 2550 | 0.6799          | 0.765    |
| 0.3716        | 35.0  | 2625 | 0.7307          | 0.7767   |
| 0.3007        | 36.0  | 2700 | 0.7490          | 0.7583   |
| 0.2019        | 37.0  | 2775 | 0.8838          | 0.7533   |
| 0.232         | 38.0  | 2850 | 0.8738          | 0.76     |
| 0.221         | 39.0  | 2925 | 0.8842          | 0.7733   |
| 0.1875        | 40.0  | 3000 | 1.0078          | 0.7383   |
| 0.203         | 41.0  | 3075 | 1.0476          | 0.7567   |
| 0.1699        | 42.0  | 3150 | 1.0739          | 0.7567   |
| 0.171         | 43.0  | 3225 | 1.1644          | 0.7417   |
| 0.1205        | 44.0  | 3300 | 1.2501          | 0.7533   |
| 0.0811        | 45.0  | 3375 | 1.2967          | 0.755    |
| 0.0202        | 46.0  | 3450 | 1.5619          | 0.745    |
| 0.0237        | 47.0  | 3525 | 1.5862          | 0.7617   |
| 0.0127        | 48.0  | 3600 | 1.6631          | 0.7667   |
| 0.0204        | 49.0  | 3675 | 1.7536          | 0.7667   |
| 0.0042        | 50.0  | 3750 | 1.8052          | 0.765    |


### Framework versions

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