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_5x_deit_base_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: 0.42833333333333334
---

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# smids_5x_deit_base_sgd_00001_fold5

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: 1.0685
- Accuracy: 0.4283

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1186        | 1.0   | 375   | 1.1098          | 0.3483   |
| 1.1102        | 2.0   | 750   | 1.1079          | 0.3417   |
| 1.1136        | 3.0   | 1125  | 1.1060          | 0.345    |
| 1.1131        | 4.0   | 1500  | 1.1043          | 0.35     |
| 1.0975        | 5.0   | 1875  | 1.1026          | 0.3433   |
| 1.1051        | 6.0   | 2250  | 1.1010          | 0.345    |
| 1.0892        | 7.0   | 2625  | 1.0994          | 0.3417   |
| 1.0802        | 8.0   | 3000  | 1.0978          | 0.3533   |
| 1.0951        | 9.0   | 3375  | 1.0964          | 0.3517   |
| 1.0929        | 10.0  | 3750  | 1.0949          | 0.3517   |
| 1.0628        | 11.0  | 4125  | 1.0935          | 0.3533   |
| 1.0809        | 12.0  | 4500  | 1.0922          | 0.36     |
| 1.0566        | 13.0  | 4875  | 1.0909          | 0.375    |
| 1.0849        | 14.0  | 5250  | 1.0897          | 0.38     |
| 1.0684        | 15.0  | 5625  | 1.0884          | 0.3817   |
| 1.0868        | 16.0  | 6000  | 1.0873          | 0.3817   |
| 1.0653        | 17.0  | 6375  | 1.0861          | 0.3817   |
| 1.0768        | 18.0  | 6750  | 1.0850          | 0.385    |
| 1.0758        | 19.0  | 7125  | 1.0839          | 0.385    |
| 1.0932        | 20.0  | 7500  | 1.0829          | 0.3883   |
| 1.072         | 21.0  | 7875  | 1.0819          | 0.39     |
| 1.06          | 22.0  | 8250  | 1.0809          | 0.3917   |
| 1.0521        | 23.0  | 8625  | 1.0800          | 0.3967   |
| 1.0558        | 24.0  | 9000  | 1.0792          | 0.3983   |
| 1.0773        | 25.0  | 9375  | 1.0783          | 0.4      |
| 1.0609        | 26.0  | 9750  | 1.0775          | 0.4      |
| 1.0495        | 27.0  | 10125 | 1.0767          | 0.4017   |
| 1.0658        | 28.0  | 10500 | 1.0760          | 0.4      |
| 1.0475        | 29.0  | 10875 | 1.0753          | 0.4083   |
| 1.0538        | 30.0  | 11250 | 1.0746          | 0.415    |
| 1.0455        | 31.0  | 11625 | 1.0740          | 0.4133   |
| 1.0741        | 32.0  | 12000 | 1.0734          | 0.415    |
| 1.0518        | 33.0  | 12375 | 1.0728          | 0.4167   |
| 1.04          | 34.0  | 12750 | 1.0723          | 0.4183   |
| 1.0566        | 35.0  | 13125 | 1.0718          | 0.4167   |
| 1.0416        | 36.0  | 13500 | 1.0714          | 0.4167   |
| 1.0546        | 37.0  | 13875 | 1.0709          | 0.4217   |
| 1.0514        | 38.0  | 14250 | 1.0706          | 0.4233   |
| 1.0481        | 39.0  | 14625 | 1.0702          | 0.425    |
| 1.0581        | 40.0  | 15000 | 1.0699          | 0.4267   |
| 1.0484        | 41.0  | 15375 | 1.0696          | 0.4267   |
| 1.0544        | 42.0  | 15750 | 1.0694          | 0.4267   |
| 1.0499        | 43.0  | 16125 | 1.0691          | 0.4267   |
| 1.0424        | 44.0  | 16500 | 1.0690          | 0.4267   |
| 1.0515        | 45.0  | 16875 | 1.0688          | 0.4283   |
| 1.0389        | 46.0  | 17250 | 1.0687          | 0.4283   |
| 1.0556        | 47.0  | 17625 | 1.0686          | 0.4283   |
| 1.0595        | 48.0  | 18000 | 1.0685          | 0.4283   |
| 1.0528        | 49.0  | 18375 | 1.0685          | 0.4283   |
| 1.0533        | 50.0  | 18750 | 1.0685          | 0.4283   |


### Framework versions

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