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# vit-base-patch16-224-in21k-finetuned

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1228
- Accuracy: 1.0

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.5651        | 0.9816  | 40   | 0.7021          | 0.5      |
| 0.3002        | 1.9877  | 81   | 0.7162          | 0.625    |
| 0.251         | 2.9939  | 122  | 0.8250          | 0.625    |
| 0.1628        | 4.0     | 163  | 0.8735          | 0.625    |
| 0.1763        | 4.9816  | 203  | 0.7803          | 0.625    |
| 0.1694        | 5.9877  | 244  | 0.3916          | 0.6875   |
| 0.1572        | 6.9939  | 285  | 0.6275          | 0.8125   |
| 0.1343        | 8.0     | 326  | 1.3112          | 0.625    |
| 0.1629        | 8.9816  | 366  | 0.5798          | 0.625    |
| 0.1675        | 9.9877  | 407  | 0.4662          | 0.8125   |
| 0.1254        | 10.9939 | 448  | 0.4484          | 0.8125   |
| 0.136         | 12.0    | 489  | 0.3055          | 0.8125   |
| 0.1303        | 12.9816 | 529  | 0.2235          | 0.875    |
| 0.177         | 13.9877 | 570  | 0.4362          | 0.8125   |
| 0.125         | 14.9939 | 611  | 0.5964          | 0.625    |
| 0.1059        | 16.0    | 652  | 0.5711          | 0.6875   |
| 0.1012        | 16.9816 | 692  | 0.1228          | 1.0      |
| 0.0945        | 17.9877 | 733  | 0.1478          | 1.0      |
| 0.1169        | 18.9939 | 774  | 0.2164          | 0.9375   |
| 0.0968        | 19.6319 | 800  | 0.2333          | 0.875    |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3

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