---
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_adamax_0001_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: 0.8885191347753744
---

<!-- 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_adamax_0001_fold2

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: 1.0252
- Accuracy: 0.8885

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2345        | 1.0   | 750   | 0.2862          | 0.8852   |
| 0.1226        | 2.0   | 1500  | 0.3492          | 0.8819   |
| 0.0786        | 3.0   | 2250  | 0.4125          | 0.8785   |
| 0.0821        | 4.0   | 3000  | 0.6690          | 0.8569   |
| 0.0058        | 5.0   | 3750  | 0.6467          | 0.8869   |
| 0.0246        | 6.0   | 4500  | 0.7107          | 0.8819   |
| 0.0249        | 7.0   | 5250  | 0.7670          | 0.8852   |
| 0.0023        | 8.0   | 6000  | 0.8535          | 0.8952   |
| 0.0094        | 9.0   | 6750  | 0.9434          | 0.8819   |
| 0.0202        | 10.0  | 7500  | 1.0037          | 0.8835   |
| 0.0073        | 11.0  | 8250  | 1.0395          | 0.8802   |
| 0.0           | 12.0  | 9000  | 0.9154          | 0.8935   |
| 0.0001        | 13.0  | 9750  | 1.0585          | 0.8785   |
| 0.0048        | 14.0  | 10500 | 0.9392          | 0.8952   |
| 0.0002        | 15.0  | 11250 | 0.9865          | 0.8885   |
| 0.0           | 16.0  | 12000 | 1.1010          | 0.8885   |
| 0.0           | 17.0  | 12750 | 1.0500          | 0.8968   |
| 0.0           | 18.0  | 13500 | 1.0366          | 0.8968   |
| 0.0           | 19.0  | 14250 | 0.9974          | 0.8735   |
| 0.0           | 20.0  | 15000 | 1.0266          | 0.8935   |
| 0.0           | 21.0  | 15750 | 0.9740          | 0.9018   |
| 0.0135        | 22.0  | 16500 | 1.0163          | 0.8935   |
| 0.0062        | 23.0  | 17250 | 1.0796          | 0.8835   |
| 0.0           | 24.0  | 18000 | 1.0547          | 0.8852   |
| 0.0           | 25.0  | 18750 | 1.0544          | 0.8918   |
| 0.0           | 26.0  | 19500 | 0.9809          | 0.8952   |
| 0.0089        | 27.0  | 20250 | 1.0367          | 0.8918   |
| 0.0           | 28.0  | 21000 | 1.0326          | 0.8835   |
| 0.0           | 29.0  | 21750 | 1.0069          | 0.8935   |
| 0.0           | 30.0  | 22500 | 1.0290          | 0.8968   |
| 0.0           | 31.0  | 23250 | 1.0034          | 0.8935   |
| 0.0           | 32.0  | 24000 | 0.9398          | 0.8985   |
| 0.0           | 33.0  | 24750 | 1.0178          | 0.8935   |
| 0.0           | 34.0  | 25500 | 1.0300          | 0.8902   |
| 0.0           | 35.0  | 26250 | 1.0140          | 0.8918   |
| 0.0           | 36.0  | 27000 | 1.0115          | 0.8902   |
| 0.0074        | 37.0  | 27750 | 1.0130          | 0.8918   |
| 0.0           | 38.0  | 28500 | 1.0096          | 0.8902   |
| 0.0           | 39.0  | 29250 | 1.0259          | 0.8935   |
| 0.0           | 40.0  | 30000 | 1.0330          | 0.8918   |
| 0.0           | 41.0  | 30750 | 1.0283          | 0.8902   |
| 0.0           | 42.0  | 31500 | 1.0254          | 0.8902   |
| 0.0           | 43.0  | 32250 | 1.0245          | 0.8869   |
| 0.0           | 44.0  | 33000 | 1.0228          | 0.8885   |
| 0.0024        | 45.0  | 33750 | 1.0249          | 0.8885   |
| 0.0           | 46.0  | 34500 | 1.0240          | 0.8885   |
| 0.0           | 47.0  | 35250 | 1.0247          | 0.8885   |
| 0.0           | 48.0  | 36000 | 1.0252          | 0.8885   |
| 0.0           | 49.0  | 36750 | 1.0256          | 0.8885   |
| 0.0           | 50.0  | 37500 | 1.0252          | 0.8885   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2