---
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_sgd_0001_fold4
  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: 0.8
---

<!-- 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_sgd_0001_fold4

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: 0.4936
- Accuracy: 0.8

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1564        | 1.0   | 375   | 1.1576          | 0.3767   |
| 1.0853        | 2.0   | 750   | 1.0913          | 0.4      |
| 0.9971        | 3.0   | 1125  | 1.0409          | 0.44     |
| 0.9972        | 4.0   | 1500  | 0.9979          | 0.4683   |
| 0.9211        | 5.0   | 1875  | 0.9595          | 0.4967   |
| 0.885         | 6.0   | 2250  | 0.9224          | 0.535    |
| 0.8576        | 7.0   | 2625  | 0.8878          | 0.5583   |
| 0.8551        | 8.0   | 3000  | 0.8542          | 0.5783   |
| 0.8253        | 9.0   | 3375  | 0.8221          | 0.6017   |
| 0.8198        | 10.0  | 3750  | 0.7908          | 0.6283   |
| 0.6752        | 11.0  | 4125  | 0.7615          | 0.645    |
| 0.6508        | 12.0  | 4500  | 0.7343          | 0.6767   |
| 0.6556        | 13.0  | 4875  | 0.7097          | 0.69     |
| 0.7132        | 14.0  | 5250  | 0.6866          | 0.7083   |
| 0.6057        | 15.0  | 5625  | 0.6661          | 0.7183   |
| 0.5722        | 16.0  | 6000  | 0.6478          | 0.7283   |
| 0.5982        | 17.0  | 6375  | 0.6328          | 0.7333   |
| 0.5686        | 18.0  | 6750  | 0.6177          | 0.735    |
| 0.5939        | 19.0  | 7125  | 0.6046          | 0.7417   |
| 0.5225        | 20.0  | 7500  | 0.5938          | 0.7483   |
| 0.5314        | 21.0  | 7875  | 0.5829          | 0.7567   |
| 0.5367        | 22.0  | 8250  | 0.5746          | 0.765    |
| 0.506         | 23.0  | 8625  | 0.5665          | 0.77     |
| 0.5218        | 24.0  | 9000  | 0.5589          | 0.7717   |
| 0.5608        | 25.0  | 9375  | 0.5520          | 0.7767   |
| 0.5255        | 26.0  | 9750  | 0.5459          | 0.78     |
| 0.5248        | 27.0  | 10125 | 0.5406          | 0.78     |
| 0.496         | 28.0  | 10500 | 0.5353          | 0.78     |
| 0.4514        | 29.0  | 10875 | 0.5308          | 0.785    |
| 0.4878        | 30.0  | 11250 | 0.5266          | 0.785    |
| 0.4791        | 31.0  | 11625 | 0.5226          | 0.785    |
| 0.4601        | 32.0  | 12000 | 0.5192          | 0.785    |
| 0.527         | 33.0  | 12375 | 0.5161          | 0.7867   |
| 0.4682        | 34.0  | 12750 | 0.5130          | 0.785    |
| 0.4268        | 35.0  | 13125 | 0.5104          | 0.7917   |
| 0.4602        | 36.0  | 13500 | 0.5080          | 0.795    |
| 0.4456        | 37.0  | 13875 | 0.5057          | 0.7983   |
| 0.4657        | 38.0  | 14250 | 0.5038          | 0.7983   |
| 0.5191        | 39.0  | 14625 | 0.5021          | 0.7983   |
| 0.5029        | 40.0  | 15000 | 0.5005          | 0.8      |
| 0.4811        | 41.0  | 15375 | 0.4991          | 0.8      |
| 0.4466        | 42.0  | 15750 | 0.4979          | 0.8      |
| 0.4615        | 43.0  | 16125 | 0.4969          | 0.8017   |
| 0.4147        | 44.0  | 16500 | 0.4960          | 0.8      |
| 0.4484        | 45.0  | 16875 | 0.4953          | 0.8      |
| 0.4471        | 46.0  | 17250 | 0.4947          | 0.8      |
| 0.4839        | 47.0  | 17625 | 0.4942          | 0.8      |
| 0.4773        | 48.0  | 18000 | 0.4939          | 0.8      |
| 0.4334        | 49.0  | 18375 | 0.4937          | 0.8      |
| 0.4329        | 50.0  | 18750 | 0.4936          | 0.8      |


### Framework versions

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