---
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_1x_deit_small_adamax_00001_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: 0.8666666666666667
---

<!-- 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_small_adamax_00001_fold5

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: 0.6794
- Accuracy: 0.8667

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6229        | 1.0   | 75   | 0.5343          | 0.7933   |
| 0.4114        | 2.0   | 150  | 0.3999          | 0.8333   |
| 0.3246        | 3.0   | 225  | 0.3573          | 0.835    |
| 0.2962        | 4.0   | 300  | 0.3318          | 0.8633   |
| 0.2498        | 5.0   | 375  | 0.3315          | 0.86     |
| 0.1818        | 6.0   | 450  | 0.3035          | 0.87     |
| 0.1932        | 7.0   | 525  | 0.3061          | 0.875    |
| 0.1474        | 8.0   | 600  | 0.3041          | 0.87     |
| 0.0826        | 9.0   | 675  | 0.3169          | 0.87     |
| 0.081         | 10.0  | 750  | 0.3163          | 0.8667   |
| 0.0813        | 11.0  | 825  | 0.3296          | 0.87     |
| 0.0261        | 12.0  | 900  | 0.3349          | 0.87     |
| 0.0464        | 13.0  | 975  | 0.3659          | 0.8667   |
| 0.0215        | 14.0  | 1050 | 0.4099          | 0.87     |
| 0.027         | 15.0  | 1125 | 0.4201          | 0.87     |
| 0.0112        | 16.0  | 1200 | 0.4420          | 0.8717   |
| 0.0162        | 17.0  | 1275 | 0.4669          | 0.8733   |
| 0.016         | 18.0  | 1350 | 0.5089          | 0.87     |
| 0.0103        | 19.0  | 1425 | 0.4963          | 0.8717   |
| 0.0014        | 20.0  | 1500 | 0.5125          | 0.8733   |
| 0.0096        | 21.0  | 1575 | 0.5220          | 0.8733   |
| 0.0069        | 22.0  | 1650 | 0.5718          | 0.8617   |
| 0.0159        | 23.0  | 1725 | 0.5556          | 0.8717   |
| 0.0008        | 24.0  | 1800 | 0.5732          | 0.8667   |
| 0.0352        | 25.0  | 1875 | 0.5727          | 0.8683   |
| 0.0005        | 26.0  | 1950 | 0.5893          | 0.8683   |
| 0.0095        | 27.0  | 2025 | 0.6176          | 0.8667   |
| 0.0202        | 28.0  | 2100 | 0.5996          | 0.865    |
| 0.0236        | 29.0  | 2175 | 0.6069          | 0.8633   |
| 0.0103        | 30.0  | 2250 | 0.6179          | 0.865    |
| 0.0003        | 31.0  | 2325 | 0.6857          | 0.8633   |
| 0.0003        | 32.0  | 2400 | 0.6471          | 0.8667   |
| 0.0077        | 33.0  | 2475 | 0.6466          | 0.8667   |
| 0.0003        | 34.0  | 2550 | 0.6723          | 0.8633   |
| 0.0076        | 35.0  | 2625 | 0.6448          | 0.865    |
| 0.0002        | 36.0  | 2700 | 0.6372          | 0.87     |
| 0.0037        | 37.0  | 2775 | 0.6601          | 0.87     |
| 0.0002        | 38.0  | 2850 | 0.6572          | 0.8683   |
| 0.0002        | 39.0  | 2925 | 0.6720          | 0.8683   |
| 0.0002        | 40.0  | 3000 | 0.6642          | 0.8683   |
| 0.0072        | 41.0  | 3075 | 0.6568          | 0.8683   |
| 0.0002        | 42.0  | 3150 | 0.6668          | 0.8633   |
| 0.0069        | 43.0  | 3225 | 0.6577          | 0.8683   |
| 0.0002        | 44.0  | 3300 | 0.6738          | 0.8667   |
| 0.0042        | 45.0  | 3375 | 0.6742          | 0.865    |
| 0.0002        | 46.0  | 3450 | 0.6815          | 0.8667   |
| 0.0111        | 47.0  | 3525 | 0.6812          | 0.8667   |
| 0.0112        | 48.0  | 3600 | 0.6837          | 0.8683   |
| 0.0002        | 49.0  | 3675 | 0.6809          | 0.8667   |
| 0.0038        | 50.0  | 3750 | 0.6794          | 0.8667   |


### Framework versions

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