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# vit-base-patch16-224-R1-40

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7212
- Accuracy: 0.7541

## 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: 5.5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 40

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3233        | 0.99  | 38   | 1.2355          | 0.5574   |
| 0.8643        | 1.99  | 76   | 0.9297          | 0.5902   |
| 0.4464        | 2.98  | 114  | 1.1190          | 0.6393   |
| 0.3092        | 4.0   | 153  | 0.9861          | 0.7049   |
| 0.1628        | 4.99  | 191  | 1.1221          | 0.6721   |
| 0.121         | 5.99  | 229  | 1.1710          | 0.6885   |
| 0.1138        | 6.98  | 267  | 1.1993          | 0.7213   |
| 0.1124        | 8.0   | 306  | 1.2636          | 0.6885   |
| 0.0748        | 8.99  | 344  | 1.3881          | 0.7049   |
| 0.0877        | 9.99  | 382  | 1.2892          | 0.7213   |
| 0.0642        | 10.98 | 420  | 1.3759          | 0.7049   |
| 0.0675        | 12.0  | 459  | 1.4283          | 0.7213   |
| 0.0694        | 12.99 | 497  | 1.3616          | 0.7213   |
| 0.0689        | 13.99 | 535  | 1.3864          | 0.7213   |
| 0.0378        | 14.98 | 573  | 1.4322          | 0.7213   |
| 0.0472        | 16.0  | 612  | 1.6004          | 0.7213   |
| 0.044         | 16.99 | 650  | 1.5810          | 0.7049   |
| 0.0386        | 17.99 | 688  | 1.6404          | 0.6885   |
| 0.0341        | 18.98 | 726  | 1.5698          | 0.7377   |
| 0.0328        | 20.0  | 765  | 1.6720          | 0.6885   |
| 0.0444        | 20.99 | 803  | 1.6269          | 0.7213   |
| 0.0342        | 21.99 | 841  | 1.6345          | 0.7377   |
| 0.0324        | 22.98 | 879  | 1.7916          | 0.7049   |
| 0.023         | 24.0  | 918  | 1.8753          | 0.6885   |
| 0.048         | 24.99 | 956  | 1.7679          | 0.7377   |
| 0.0202        | 25.99 | 994  | 1.7212          | 0.7541   |
| 0.0336        | 26.98 | 1032 | 1.7305          | 0.7377   |
| 0.0163        | 28.0  | 1071 | 1.7576          | 0.7049   |
| 0.0186        | 28.99 | 1109 | 1.7540          | 0.7377   |
| 0.0189        | 29.99 | 1147 | 1.6594          | 0.7541   |
| 0.039         | 30.98 | 1185 | 1.7423          | 0.7213   |
| 0.0194        | 32.0  | 1224 | 1.7148          | 0.7377   |
| 0.0205        | 32.99 | 1262 | 1.6965          | 0.7377   |
| 0.0186        | 33.99 | 1300 | 1.7553          | 0.7541   |
| 0.0177        | 34.98 | 1338 | 1.7476          | 0.7377   |
| 0.0132        | 36.0  | 1377 | 1.7506          | 0.7541   |
| 0.0068        | 36.99 | 1415 | 1.6917          | 0.7377   |
| 0.0121        | 37.99 | 1453 | 1.7276          | 0.7541   |
| 0.0129        | 38.98 | 1491 | 1.7218          | 0.7541   |
| 0.0067        | 39.74 | 1520 | 1.7220          | 0.7541   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0

avanzada leve moderada no dmae