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_sgd_00001_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: 0.49833333333333335
---

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# smids_1x_deit_small_sgd_00001_fold5

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: 1.0422
- Accuracy: 0.4983

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0773        | 1.0   | 75   | 1.0761          | 0.4267   |
| 1.0923        | 2.0   | 150  | 1.0743          | 0.43     |
| 1.0827        | 3.0   | 225  | 1.0725          | 0.4317   |
| 1.0592        | 4.0   | 300  | 1.0709          | 0.4333   |
| 1.0688        | 5.0   | 375  | 1.0693          | 0.43     |
| 1.0722        | 6.0   | 450  | 1.0678          | 0.4317   |
| 1.0759        | 7.0   | 525  | 1.0664          | 0.4317   |
| 1.0583        | 8.0   | 600  | 1.0650          | 0.4333   |
| 1.0565        | 9.0   | 675  | 1.0637          | 0.4383   |
| 1.0589        | 10.0  | 750  | 1.0624          | 0.44     |
| 1.0608        | 11.0  | 825  | 1.0612          | 0.4483   |
| 1.0706        | 12.0  | 900  | 1.0600          | 0.4517   |
| 1.0517        | 13.0  | 975  | 1.0589          | 0.4567   |
| 1.0525        | 14.0  | 1050 | 1.0579          | 0.4567   |
| 1.0257        | 15.0  | 1125 | 1.0569          | 0.4583   |
| 1.0608        | 16.0  | 1200 | 1.0559          | 0.4617   |
| 1.0548        | 17.0  | 1275 | 1.0550          | 0.46     |
| 1.0482        | 18.0  | 1350 | 1.0541          | 0.4617   |
| 1.0606        | 19.0  | 1425 | 1.0533          | 0.4633   |
| 1.0832        | 20.0  | 1500 | 1.0524          | 0.4667   |
| 1.0387        | 21.0  | 1575 | 1.0517          | 0.4717   |
| 1.0524        | 22.0  | 1650 | 1.0510          | 0.4733   |
| 1.043         | 23.0  | 1725 | 1.0503          | 0.4733   |
| 1.0404        | 24.0  | 1800 | 1.0496          | 0.475    |
| 1.0507        | 25.0  | 1875 | 1.0490          | 0.4767   |
| 1.026         | 26.0  | 1950 | 1.0484          | 0.48     |
| 1.0409        | 27.0  | 2025 | 1.0478          | 0.48     |
| 1.0569        | 28.0  | 2100 | 1.0473          | 0.4867   |
| 1.0416        | 29.0  | 2175 | 1.0468          | 0.4867   |
| 1.0319        | 30.0  | 2250 | 1.0463          | 0.4867   |
| 1.0368        | 31.0  | 2325 | 1.0459          | 0.49     |
| 1.0498        | 32.0  | 2400 | 1.0455          | 0.4933   |
| 1.0315        | 33.0  | 2475 | 1.0451          | 0.4933   |
| 1.0281        | 34.0  | 2550 | 1.0447          | 0.49     |
| 1.0165        | 35.0  | 2625 | 1.0444          | 0.4917   |
| 1.0233        | 36.0  | 2700 | 1.0441          | 0.4933   |
| 1.0217        | 37.0  | 2775 | 1.0438          | 0.4967   |
| 1.0413        | 38.0  | 2850 | 1.0435          | 0.4967   |
| 1.0419        | 39.0  | 2925 | 1.0433          | 0.4967   |
| 1.0408        | 40.0  | 3000 | 1.0431          | 0.4967   |
| 1.0269        | 41.0  | 3075 | 1.0429          | 0.4983   |
| 1.0155        | 42.0  | 3150 | 1.0428          | 0.4983   |
| 1.0319        | 43.0  | 3225 | 1.0426          | 0.4983   |
| 1.015         | 44.0  | 3300 | 1.0425          | 0.4983   |
| 1.0304        | 45.0  | 3375 | 1.0424          | 0.4983   |
| 1.037         | 46.0  | 3450 | 1.0424          | 0.4983   |
| 1.0444        | 47.0  | 3525 | 1.0423          | 0.4983   |
| 1.0465        | 48.0  | 3600 | 1.0423          | 0.4983   |
| 1.0337        | 49.0  | 3675 | 1.0423          | 0.4983   |
| 1.0221        | 50.0  | 3750 | 1.0422          | 0.4983   |


### Framework versions

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