---
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_3x_deit_small_adamax_00001_fold2
  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: 0.8735440931780366
---

<!-- 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_small_adamax_00001_fold2

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: 0.9125
- Accuracy: 0.8735

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3392        | 1.0   | 225   | 0.3664          | 0.8502   |
| 0.2605        | 2.0   | 450   | 0.3323          | 0.8719   |
| 0.212         | 3.0   | 675   | 0.3215          | 0.8686   |
| 0.2229        | 4.0   | 900   | 0.3309          | 0.8652   |
| 0.106         | 5.0   | 1125  | 0.3345          | 0.8802   |
| 0.0845        | 6.0   | 1350  | 0.3616          | 0.8719   |
| 0.0626        | 7.0   | 1575  | 0.3907          | 0.8686   |
| 0.0326        | 8.0   | 1800  | 0.4483          | 0.8669   |
| 0.0372        | 9.0   | 2025  | 0.4833          | 0.8652   |
| 0.0087        | 10.0  | 2250  | 0.5521          | 0.8735   |
| 0.0217        | 11.0  | 2475  | 0.5679          | 0.8752   |
| 0.0111        | 12.0  | 2700  | 0.6269          | 0.8702   |
| 0.011         | 13.0  | 2925  | 0.6480          | 0.8702   |
| 0.0061        | 14.0  | 3150  | 0.6728          | 0.8686   |
| 0.0004        | 15.0  | 3375  | 0.7336          | 0.8669   |
| 0.0093        | 16.0  | 3600  | 0.7662          | 0.8702   |
| 0.0044        | 17.0  | 3825  | 0.7704          | 0.8752   |
| 0.0001        | 18.0  | 4050  | 0.7907          | 0.8735   |
| 0.0005        | 19.0  | 4275  | 0.7929          | 0.8669   |
| 0.0001        | 20.0  | 4500  | 0.8179          | 0.8669   |
| 0.0001        | 21.0  | 4725  | 0.8135          | 0.8785   |
| 0.0001        | 22.0  | 4950  | 0.8581          | 0.8702   |
| 0.0037        | 23.0  | 5175  | 0.8366          | 0.8719   |
| 0.0001        | 24.0  | 5400  | 0.8672          | 0.8686   |
| 0.0168        | 25.0  | 5625  | 0.8621          | 0.8686   |
| 0.0001        | 26.0  | 5850  | 0.8633          | 0.8702   |
| 0.0           | 27.0  | 6075  | 0.8679          | 0.8669   |
| 0.0001        | 28.0  | 6300  | 0.8863          | 0.8735   |
| 0.0001        | 29.0  | 6525  | 0.8794          | 0.8686   |
| 0.0145        | 30.0  | 6750  | 0.8923          | 0.8686   |
| 0.0           | 31.0  | 6975  | 0.8806          | 0.8719   |
| 0.0           | 32.0  | 7200  | 0.8844          | 0.8686   |
| 0.0001        | 33.0  | 7425  | 0.8917          | 0.8669   |
| 0.0           | 34.0  | 7650  | 0.8891          | 0.8719   |
| 0.0           | 35.0  | 7875  | 0.8984          | 0.8735   |
| 0.0077        | 36.0  | 8100  | 0.8879          | 0.8752   |
| 0.0           | 37.0  | 8325  | 0.9058          | 0.8702   |
| 0.0           | 38.0  | 8550  | 0.9002          | 0.8686   |
| 0.0096        | 39.0  | 8775  | 0.9018          | 0.8752   |
| 0.0           | 40.0  | 9000  | 0.9051          | 0.8752   |
| 0.0           | 41.0  | 9225  | 0.9023          | 0.8702   |
| 0.0           | 42.0  | 9450  | 0.9103          | 0.8752   |
| 0.0           | 43.0  | 9675  | 0.9151          | 0.8735   |
| 0.0           | 44.0  | 9900  | 0.9097          | 0.8735   |
| 0.0           | 45.0  | 10125 | 0.9063          | 0.8702   |
| 0.0           | 46.0  | 10350 | 0.9129          | 0.8735   |
| 0.0           | 47.0  | 10575 | 0.9170          | 0.8735   |
| 0.0           | 48.0  | 10800 | 0.9138          | 0.8735   |
| 0.0048        | 49.0  | 11025 | 0.9128          | 0.8735   |
| 0.0048        | 50.0  | 11250 | 0.9125          | 0.8735   |


### Framework versions

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