---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_deit_tiny_rms_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: 0.9066666666666666
---

<!-- 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_5x_deit_tiny_rms_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: 0.9895
- Accuracy: 0.9067

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3055        | 1.0   | 375   | 0.4440          | 0.825    |
| 0.2222        | 2.0   | 750   | 0.3224          | 0.8867   |
| 0.2186        | 3.0   | 1125  | 0.3702          | 0.8883   |
| 0.1592        | 4.0   | 1500  | 0.4759          | 0.85     |
| 0.0922        | 5.0   | 1875  | 0.4560          | 0.8767   |
| 0.0977        | 6.0   | 2250  | 0.5531          | 0.875    |
| 0.0567        | 7.0   | 2625  | 0.5054          | 0.8883   |
| 0.0612        | 8.0   | 3000  | 0.5016          | 0.9067   |
| 0.0471        | 9.0   | 3375  | 0.6558          | 0.895    |
| 0.0783        | 10.0  | 3750  | 0.7144          | 0.89     |
| 0.0337        | 11.0  | 4125  | 0.7483          | 0.8833   |
| 0.0522        | 12.0  | 4500  | 0.6408          | 0.8967   |
| 0.0122        | 13.0  | 4875  | 0.5578          | 0.8917   |
| 0.0456        | 14.0  | 5250  | 0.6886          | 0.9      |
| 0.0505        | 15.0  | 5625  | 0.6222          | 0.9067   |
| 0.0186        | 16.0  | 6000  | 0.7341          | 0.8867   |
| 0.0232        | 17.0  | 6375  | 0.6650          | 0.9083   |
| 0.0384        | 18.0  | 6750  | 0.6731          | 0.9133   |
| 0.0134        | 19.0  | 7125  | 0.7917          | 0.8883   |
| 0.0197        | 20.0  | 7500  | 0.7544          | 0.9033   |
| 0.006         | 21.0  | 7875  | 0.7694          | 0.8983   |
| 0.084         | 22.0  | 8250  | 0.7873          | 0.8917   |
| 0.0405        | 23.0  | 8625  | 0.7521          | 0.8967   |
| 0.0002        | 24.0  | 9000  | 0.9409          | 0.8883   |
| 0.0009        | 25.0  | 9375  | 0.8364          | 0.8967   |
| 0.0273        | 26.0  | 9750  | 0.7668          | 0.8933   |
| 0.001         | 27.0  | 10125 | 0.7995          | 0.88     |
| 0.0262        | 28.0  | 10500 | 0.8060          | 0.8883   |
| 0.0003        | 29.0  | 10875 | 0.7588          | 0.9083   |
| 0.0189        | 30.0  | 11250 | 0.9019          | 0.8867   |
| 0.0003        | 31.0  | 11625 | 1.0397          | 0.8867   |
| 0.0           | 32.0  | 12000 | 0.9253          | 0.895    |
| 0.0002        | 33.0  | 12375 | 0.8619          | 0.905    |
| 0.0003        | 34.0  | 12750 | 0.9328          | 0.9      |
| 0.0           | 35.0  | 13125 | 0.9364          | 0.905    |
| 0.0002        | 36.0  | 13500 | 0.9470          | 0.8967   |
| 0.0001        | 37.0  | 13875 | 0.9360          | 0.9033   |
| 0.0033        | 38.0  | 14250 | 1.0063          | 0.9033   |
| 0.0           | 39.0  | 14625 | 0.9618          | 0.9017   |
| 0.0           | 40.0  | 15000 | 0.9713          | 0.9083   |
| 0.0           | 41.0  | 15375 | 0.9440          | 0.9083   |
| 0.0           | 42.0  | 15750 | 0.9330          | 0.91     |
| 0.0           | 43.0  | 16125 | 0.9519          | 0.9083   |
| 0.0           | 44.0  | 16500 | 0.9407          | 0.905    |
| 0.0           | 45.0  | 16875 | 0.9804          | 0.9033   |
| 0.0           | 46.0  | 17250 | 0.9891          | 0.9033   |
| 0.0031        | 47.0  | 17625 | 0.9794          | 0.9033   |
| 0.0           | 48.0  | 18000 | 0.9842          | 0.9033   |
| 0.0           | 49.0  | 18375 | 0.9888          | 0.9067   |
| 0.0021        | 50.0  | 18750 | 0.9895          | 0.9067   |


### Framework versions

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