---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_10x_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: 0.85
---

<!-- 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_10x_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: 0.3767
- Accuracy: 0.85

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.081         | 1.0   | 750   | 1.0837          | 0.4183   |
| 0.9664        | 2.0   | 1500  | 0.9960          | 0.4867   |
| 0.9109        | 3.0   | 2250  | 0.9172          | 0.5567   |
| 0.8062        | 4.0   | 3000  | 0.8454          | 0.6017   |
| 0.7721        | 5.0   | 3750  | 0.7755          | 0.6483   |
| 0.6595        | 6.0   | 4500  | 0.7101          | 0.6883   |
| 0.5526        | 7.0   | 5250  | 0.6552          | 0.715    |
| 0.6103        | 8.0   | 6000  | 0.6092          | 0.7533   |
| 0.6584        | 9.0   | 6750  | 0.5725          | 0.7633   |
| 0.5611        | 10.0  | 7500  | 0.5452          | 0.7983   |
| 0.5536        | 11.0  | 8250  | 0.5225          | 0.8067   |
| 0.522         | 12.0  | 9000  | 0.5053          | 0.8117   |
| 0.5314        | 13.0  | 9750  | 0.4913          | 0.8167   |
| 0.4936        | 14.0  | 10500 | 0.4799          | 0.8267   |
| 0.5195        | 15.0  | 11250 | 0.4692          | 0.8283   |
| 0.4075        | 16.0  | 12000 | 0.4606          | 0.8283   |
| 0.4566        | 17.0  | 12750 | 0.4529          | 0.8317   |
| 0.4172        | 18.0  | 13500 | 0.4445          | 0.8367   |
| 0.4556        | 19.0  | 14250 | 0.4390          | 0.83     |
| 0.4667        | 20.0  | 15000 | 0.4334          | 0.8333   |
| 0.3932        | 21.0  | 15750 | 0.4273          | 0.8333   |
| 0.4625        | 22.0  | 16500 | 0.4229          | 0.8367   |
| 0.418         | 23.0  | 17250 | 0.4180          | 0.8383   |
| 0.3957        | 24.0  | 18000 | 0.4143          | 0.8367   |
| 0.4114        | 25.0  | 18750 | 0.4106          | 0.8367   |
| 0.4039        | 26.0  | 19500 | 0.4070          | 0.8367   |
| 0.3652        | 27.0  | 20250 | 0.4039          | 0.84     |
| 0.3862        | 28.0  | 21000 | 0.4011          | 0.84     |
| 0.4364        | 29.0  | 21750 | 0.3984          | 0.84     |
| 0.3781        | 30.0  | 22500 | 0.3966          | 0.8417   |
| 0.3636        | 31.0  | 23250 | 0.3941          | 0.8383   |
| 0.3588        | 32.0  | 24000 | 0.3920          | 0.84     |
| 0.4007        | 33.0  | 24750 | 0.3903          | 0.8417   |
| 0.3328        | 34.0  | 25500 | 0.3888          | 0.84     |
| 0.3699        | 35.0  | 26250 | 0.3865          | 0.8417   |
| 0.3686        | 36.0  | 27000 | 0.3852          | 0.8433   |
| 0.315         | 37.0  | 27750 | 0.3840          | 0.8467   |
| 0.3799        | 38.0  | 28500 | 0.3828          | 0.8467   |
| 0.3659        | 39.0  | 29250 | 0.3817          | 0.8467   |
| 0.3715        | 40.0  | 30000 | 0.3806          | 0.8467   |
| 0.3582        | 41.0  | 30750 | 0.3798          | 0.8467   |
| 0.4093        | 42.0  | 31500 | 0.3792          | 0.8483   |
| 0.3651        | 43.0  | 32250 | 0.3786          | 0.8483   |
| 0.3713        | 44.0  | 33000 | 0.3782          | 0.85     |
| 0.3675        | 45.0  | 33750 | 0.3776          | 0.85     |
| 0.3336        | 46.0  | 34500 | 0.3773          | 0.85     |
| 0.4464        | 47.0  | 35250 | 0.3769          | 0.85     |
| 0.3703        | 48.0  | 36000 | 0.3768          | 0.85     |
| 0.366         | 49.0  | 36750 | 0.3767          | 0.85     |
| 0.311         | 50.0  | 37500 | 0.3767          | 0.85     |


### Framework versions

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