transformers tensorboard safetensors vit image-classification generated_from_trainer dataset:imagefolder base_model:facebook/deit-small-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
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_0001_fold3
  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: 0.9083333333333333
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# smids_1x_deit_small_adamax_0001_fold3

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: 0.5996
- Accuracy: 0.9083

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3937        | 1.0   | 75   | 0.3211          | 0.8633   |
| 0.2826        | 2.0   | 150  | 0.3394          | 0.8733   |
| 0.1652        | 3.0   | 225  | 0.2814          | 0.8967   |
| 0.141         | 4.0   | 300  | 0.2984          | 0.9      |
| 0.0291        | 5.0   | 375  | 0.3773          | 0.9017   |
| 0.0153        | 6.0   | 450  | 0.4868          | 0.8733   |
| 0.0215        | 7.0   | 525  | 0.4578          | 0.9067   |
| 0.0108        | 8.0   | 600  | 0.5844          | 0.875    |
| 0.0251        | 9.0   | 675  | 0.5045          | 0.9033   |
| 0.0276        | 10.0  | 750  | 0.7184          | 0.875    |
| 0.0071        | 11.0  | 825  | 0.5704          | 0.89     |
| 0.0105        | 12.0  | 900  | 0.5523          | 0.9067   |
| 0.0005        | 13.0  | 975  | 0.5683          | 0.9033   |
| 0.0002        | 14.0  | 1050 | 0.5274          | 0.9033   |
| 0.0001        | 15.0  | 1125 | 0.5432          | 0.895    |
| 0.0001        | 16.0  | 1200 | 0.5742          | 0.8983   |
| 0.0001        | 17.0  | 1275 | 0.5512          | 0.9067   |
| 0.0071        | 18.0  | 1350 | 0.5548          | 0.9033   |
| 0.0043        | 19.0  | 1425 | 0.5622          | 0.9083   |
| 0.0063        | 20.0  | 1500 | 0.5939          | 0.9033   |
| 0.0           | 21.0  | 1575 | 0.5379          | 0.9133   |
| 0.0           | 22.0  | 1650 | 0.5428          | 0.91     |
| 0.0037        | 23.0  | 1725 | 0.5469          | 0.9067   |
| 0.0           | 24.0  | 1800 | 0.5517          | 0.9083   |
| 0.0           | 25.0  | 1875 | 0.5493          | 0.9083   |
| 0.0032        | 26.0  | 1950 | 0.5544          | 0.915    |
| 0.0           | 27.0  | 2025 | 0.5586          | 0.9117   |
| 0.0           | 28.0  | 2100 | 0.5623          | 0.9117   |
| 0.0           | 29.0  | 2175 | 0.5631          | 0.9117   |
| 0.0           | 30.0  | 2250 | 0.5628          | 0.91     |
| 0.0           | 31.0  | 2325 | 0.5710          | 0.9117   |
| 0.0           | 32.0  | 2400 | 0.5769          | 0.91     |
| 0.0           | 33.0  | 2475 | 0.5763          | 0.91     |
| 0.0048        | 34.0  | 2550 | 0.5811          | 0.9117   |
| 0.0073        | 35.0  | 2625 | 0.5738          | 0.9117   |
| 0.0031        | 36.0  | 2700 | 0.5751          | 0.91     |
| 0.0023        | 37.0  | 2775 | 0.5897          | 0.9133   |
| 0.0           | 38.0  | 2850 | 0.5810          | 0.91     |
| 0.0           | 39.0  | 2925 | 0.5835          | 0.91     |
| 0.0           | 40.0  | 3000 | 0.5848          | 0.9083   |
| 0.0           | 41.0  | 3075 | 0.5898          | 0.91     |
| 0.0027        | 42.0  | 3150 | 0.5994          | 0.915    |
| 0.0028        | 43.0  | 3225 | 0.5922          | 0.91     |
| 0.0           | 44.0  | 3300 | 0.5948          | 0.91     |
| 0.0027        | 45.0  | 3375 | 0.5970          | 0.91     |
| 0.0025        | 46.0  | 3450 | 0.5958          | 0.9083   |
| 0.0051        | 47.0  | 3525 | 0.6023          | 0.9133   |
| 0.0           | 48.0  | 3600 | 0.5981          | 0.9083   |
| 0.0           | 49.0  | 3675 | 0.6006          | 0.91     |
| 0.0045        | 50.0  | 3750 | 0.5996          | 0.9083   |


### Framework versions

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