transformers pytorch vit image-classification generated_from_trainer dataset:imagefolder base_model:facebook/deit-base-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: facebook/deit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_3x_deit_base_rms_00001_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_3x_deit_base_rms_00001_fold3

This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9351
- 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: 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2558        | 1.0   | 225   | 0.2380          | 0.9167   |
| 0.1474        | 2.0   | 450   | 0.2896          | 0.895    |
| 0.0478        | 3.0   | 675   | 0.3355          | 0.9067   |
| 0.0073        | 4.0   | 900   | 0.3566          | 0.9217   |
| 0.0246        | 5.0   | 1125  | 0.4526          | 0.9      |
| 0.0014        | 6.0   | 1350  | 0.5013          | 0.9067   |
| 0.0175        | 7.0   | 1575  | 0.6021          | 0.9017   |
| 0.0203        | 8.0   | 1800  | 0.5721          | 0.9083   |
| 0.0288        | 9.0   | 2025  | 0.5925          | 0.9083   |
| 0.0252        | 10.0  | 2250  | 0.6064          | 0.91     |
| 0.0051        | 11.0  | 2475  | 0.6439          | 0.9133   |
| 0.0001        | 12.0  | 2700  | 0.6659          | 0.9033   |
| 0.0001        | 13.0  | 2925  | 0.6695          | 0.9083   |
| 0.0001        | 14.0  | 3150  | 0.7344          | 0.9033   |
| 0.0001        | 15.0  | 3375  | 0.8923          | 0.8917   |
| 0.0001        | 16.0  | 3600  | 0.7237          | 0.905    |
| 0.0001        | 17.0  | 3825  | 0.7599          | 0.8983   |
| 0.0001        | 18.0  | 4050  | 0.7079          | 0.91     |
| 0.0           | 19.0  | 4275  | 0.6928          | 0.9117   |
| 0.012         | 20.0  | 4500  | 0.7517          | 0.9117   |
| 0.0009        | 21.0  | 4725  | 0.7047          | 0.9083   |
| 0.0065        | 22.0  | 4950  | 0.7383          | 0.9033   |
| 0.0           | 23.0  | 5175  | 0.8215          | 0.9017   |
| 0.0           | 24.0  | 5400  | 0.8602          | 0.9      |
| 0.004         | 25.0  | 5625  | 0.8484          | 0.8983   |
| 0.0           | 26.0  | 5850  | 0.7324          | 0.9017   |
| 0.0           | 27.0  | 6075  | 0.7794          | 0.9067   |
| 0.0           | 28.0  | 6300  | 0.7965          | 0.9067   |
| 0.0048        | 29.0  | 6525  | 0.7845          | 0.9017   |
| 0.0025        | 30.0  | 6750  | 0.8176          | 0.9067   |
| 0.0037        | 31.0  | 6975  | 0.8141          | 0.9067   |
| 0.0           | 32.0  | 7200  | 0.8318          | 0.9083   |
| 0.0           | 33.0  | 7425  | 0.8648          | 0.9067   |
| 0.0           | 34.0  | 7650  | 0.8665          | 0.9067   |
| 0.0           | 35.0  | 7875  | 0.8924          | 0.9067   |
| 0.0           | 36.0  | 8100  | 0.8743          | 0.9067   |
| 0.0           | 37.0  | 8325  | 0.8853          | 0.905    |
| 0.0           | 38.0  | 8550  | 0.9112          | 0.9083   |
| 0.0           | 39.0  | 8775  | 0.9104          | 0.905    |
| 0.0           | 40.0  | 9000  | 0.9178          | 0.9067   |
| 0.0           | 41.0  | 9225  | 0.9148          | 0.9067   |
| 0.0           | 42.0  | 9450  | 0.9212          | 0.905    |
| 0.0           | 43.0  | 9675  | 0.9285          | 0.905    |
| 0.0           | 44.0  | 9900  | 0.9313          | 0.905    |
| 0.0           | 45.0  | 10125 | 0.9285          | 0.9067   |
| 0.0           | 46.0  | 10350 | 0.9309          | 0.9067   |
| 0.0023        | 47.0  | 10575 | 0.9324          | 0.9067   |
| 0.0           | 48.0  | 10800 | 0.9333          | 0.9083   |
| 0.0           | 49.0  | 11025 | 0.9347          | 0.9083   |
| 0.0           | 50.0  | 11250 | 0.9351          | 0.9083   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
