---
library_name: transformers
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- recall
model-index:
- name: deit_fold_4
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: None
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9515570934256056
    - name: Recall
      type: recall
      value: 0.9524889695792607
---

<!-- 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. -->

# deit_fold_4

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: 1.1613
- Accuracy: 0.9516
- F1 Score: 0.9557
- Recall: 0.9525

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 150
- num_epochs: 100
- label_smoothing_factor: 0.15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:------:|
| 3.0112        | 1.0   | 19   | 2.9175          | 0.1349   | 0.1173   | 0.1476 |
| 2.8882        | 2.0   | 38   | 2.7478          | 0.2837   | 0.2479   | 0.2602 |
| 2.6765        | 3.0   | 57   | 2.5537          | 0.4360   | 0.3402   | 0.3670 |
| 2.3897        | 4.0   | 76   | 2.2672          | 0.5882   | 0.5441   | 0.5359 |
| 1.9781        | 5.0   | 95   | 1.8705          | 0.7924   | 0.7997   | 0.7861 |
| 1.6123        | 6.0   | 114  | 1.5627          | 0.8512   | 0.8659   | 0.8675 |
| 1.4216        | 7.0   | 133  | 1.3723          | 0.8962   | 0.9067   | 0.9017 |
| 1.2460        | 8.0   | 152  | 1.2605          | 0.9308   | 0.9380   | 0.9351 |
| 1.1724        | 9.0   | 171  | 1.2218          | 0.9308   | 0.9370   | 0.9337 |
| 1.1521        | 10.0  | 190  | 1.1915          | 0.9446   | 0.9492   | 0.9488 |
| 1.1116        | 11.0  | 209  | 1.1921          | 0.9377   | 0.9440   | 0.9451 |
| 1.0878        | 12.0  | 228  | 1.1973          | 0.9343   | 0.9406   | 0.9428 |
| 1.0601        | 13.0  | 247  | 1.1842          | 0.9377   | 0.9435   | 0.9451 |
| 1.0711        | 14.0  | 266  | 1.1563          | 0.9446   | 0.9497   | 0.9475 |
| 1.0461        | 15.0  | 285  | 1.1533          | 0.9446   | 0.9497   | 0.9475 |
| 1.0390        | 16.0  | 304  | 1.1613          | 0.9516   | 0.9557   | 0.9525 |
| 1.0080        | 17.0  | 323  | 1.1645          | 0.9412   | 0.9463   | 0.9465 |
| 1.0109        | 18.0  | 342  | 1.1656          | 0.9377   | 0.9434   | 0.9438 |
| 1.0092        | 19.0  | 361  | 1.1701          | 0.9446   | 0.9494   | 0.9501 |
| 0.9972        | 20.0  | 380  | 1.1859          | 0.9377   | 0.9432   | 0.9429 |
| 1.0005        | 21.0  | 399  | 1.1708          | 0.9446   | 0.9501   | 0.9522 |
| 0.9900        | 22.0  | 418  | 1.1849          | 0.9412   | 0.9470   | 0.9476 |
| 0.9758        | 23.0  | 437  | 1.1893          | 0.9446   | 0.9496   | 0.9511 |
| 0.9886        | 24.0  | 456  | 1.1739          | 0.9446   | 0.9496   | 0.9511 |
| 0.9757        | 25.0  | 475  | 1.1648          | 0.9412   | 0.9460   | 0.9440 |


### Framework versions

- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 5.0.0
- Tokenizers 0.22.2