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_10x_deit_base_sgd_00001_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: 0.5833333333333334
---

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# smids_10x_deit_base_sgd_00001_fold4

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: 1.0053
- Accuracy: 0.5833

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1184        | 1.0   | 750   | 1.1087          | 0.3417   |
| 1.0954        | 2.0   | 1500  | 1.1049          | 0.3567   |
| 1.0943        | 3.0   | 2250  | 1.1012          | 0.3733   |
| 1.0965        | 4.0   | 3000  | 1.0976          | 0.3883   |
| 1.097         | 5.0   | 3750  | 1.0941          | 0.3983   |
| 1.0859        | 6.0   | 4500  | 1.0907          | 0.41     |
| 1.0772        | 7.0   | 5250  | 1.0874          | 0.4133   |
| 1.0913        | 8.0   | 6000  | 1.0841          | 0.43     |
| 1.0722        | 9.0   | 6750  | 1.0809          | 0.4383   |
| 1.0755        | 10.0  | 7500  | 1.0778          | 0.445    |
| 1.0725        | 11.0  | 8250  | 1.0746          | 0.4467   |
| 1.0546        | 12.0  | 9000  | 1.0716          | 0.4517   |
| 1.0625        | 13.0  | 9750  | 1.0686          | 0.46     |
| 1.0581        | 14.0  | 10500 | 1.0656          | 0.4683   |
| 1.047         | 15.0  | 11250 | 1.0626          | 0.4733   |
| 1.0434        | 16.0  | 12000 | 1.0597          | 0.48     |
| 1.0251        | 17.0  | 12750 | 1.0568          | 0.4917   |
| 1.0453        | 18.0  | 13500 | 1.0540          | 0.4933   |
| 1.0513        | 19.0  | 14250 | 1.0512          | 0.5      |
| 1.0388        | 20.0  | 15000 | 1.0484          | 0.51     |
| 1.0333        | 21.0  | 15750 | 1.0457          | 0.52     |
| 1.0252        | 22.0  | 16500 | 1.0431          | 0.5267   |
| 1.0295        | 23.0  | 17250 | 1.0405          | 0.5283   |
| 1.0271        | 24.0  | 18000 | 1.0379          | 0.5317   |
| 1.0317        | 25.0  | 18750 | 1.0355          | 0.5333   |
| 1.0277        | 26.0  | 19500 | 1.0331          | 0.5367   |
| 1.0061        | 27.0  | 20250 | 1.0308          | 0.5467   |
| 1.0064        | 28.0  | 21000 | 1.0286          | 0.555    |
| 1.0287        | 29.0  | 21750 | 1.0265          | 0.56     |
| 1.0101        | 30.0  | 22500 | 1.0245          | 0.5633   |
| 1.0026        | 31.0  | 23250 | 1.0225          | 0.5667   |
| 1.0037        | 32.0  | 24000 | 1.0207          | 0.5667   |
| 1.0192        | 33.0  | 24750 | 1.0189          | 0.57     |
| 1.0129        | 34.0  | 25500 | 1.0173          | 0.57     |
| 1.007         | 35.0  | 26250 | 1.0157          | 0.57     |
| 0.9958        | 36.0  | 27000 | 1.0143          | 0.5717   |
| 1.0225        | 37.0  | 27750 | 1.0130          | 0.5733   |
| 1.0064        | 38.0  | 28500 | 1.0118          | 0.575    |
| 0.99          | 39.0  | 29250 | 1.0107          | 0.5733   |
| 0.9987        | 40.0  | 30000 | 1.0097          | 0.5767   |
| 1.0146        | 41.0  | 30750 | 1.0088          | 0.5817   |
| 0.9734        | 42.0  | 31500 | 1.0080          | 0.5817   |
| 1.0086        | 43.0  | 32250 | 1.0073          | 0.5817   |
| 0.9898        | 44.0  | 33000 | 1.0068          | 0.5833   |
| 0.9877        | 45.0  | 33750 | 1.0063          | 0.5833   |
| 0.9974        | 46.0  | 34500 | 1.0059          | 0.5833   |
| 0.9888        | 47.0  | 35250 | 1.0057          | 0.5833   |
| 0.9987        | 48.0  | 36000 | 1.0055          | 0.5833   |
| 1.01          | 49.0  | 36750 | 1.0054          | 0.5833   |
| 0.9819        | 50.0  | 37500 | 1.0053          | 0.5833   |


### Framework versions

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