
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# vit-base-patch16-224-U8-40b

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: 0.5666
- Accuracy: 0.8824

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3457        | 1.0   | 20   | 1.3070          | 0.4706   |
| 1.1498        | 2.0   | 40   | 1.0956          | 0.5686   |
| 0.8293        | 3.0   | 60   | 0.8270          | 0.6471   |
| 0.5448        | 4.0   | 80   | 0.6145          | 0.8235   |
| 0.3525        | 5.0   | 100  | 0.6439          | 0.7451   |
| 0.2436        | 6.0   | 120  | 0.5427          | 0.8235   |
| 0.195         | 7.0   | 140  | 0.6276          | 0.7843   |
| 0.1629        | 8.0   | 160  | 0.7868          | 0.7255   |
| 0.1697        | 9.0   | 180  | 0.8245          | 0.7255   |
| 0.1324        | 10.0  | 200  | 0.6599          | 0.8235   |
| 0.1714        | 11.0  | 220  | 0.7453          | 0.7647   |
| 0.0908        | 12.0  | 240  | 0.5666          | 0.8824   |
| 0.0812        | 13.0  | 260  | 0.9997          | 0.7451   |
| 0.0672        | 14.0  | 280  | 0.8049          | 0.8039   |
| 0.0843        | 15.0  | 300  | 0.6723          | 0.8431   |
| 0.0946        | 16.0  | 320  | 0.8892          | 0.7451   |
| 0.0684        | 17.0  | 340  | 1.1429          | 0.7451   |
| 0.0711        | 18.0  | 360  | 1.1384          | 0.7451   |
| 0.0677        | 19.0  | 380  | 1.0296          | 0.7843   |
| 0.0562        | 20.0  | 400  | 0.9803          | 0.7647   |
| 0.0688        | 21.0  | 420  | 0.9401          | 0.7843   |
| 0.0576        | 22.0  | 440  | 1.0823          | 0.7843   |
| 0.0892        | 23.0  | 460  | 1.0819          | 0.7255   |
| 0.063         | 24.0  | 480  | 1.0756          | 0.7647   |
| 0.055         | 25.0  | 500  | 0.9693          | 0.7647   |
| 0.0407        | 26.0  | 520  | 1.0132          | 0.7451   |
| 0.0562        | 27.0  | 540  | 1.0267          | 0.7843   |
| 0.0365        | 28.0  | 560  | 1.0530          | 0.7451   |
| 0.0363        | 29.0  | 580  | 0.9277          | 0.7843   |
| 0.0392        | 30.0  | 600  | 0.9798          | 0.8039   |
| 0.0374        | 31.0  | 620  | 1.0239          | 0.8039   |
| 0.0386        | 32.0  | 640  | 1.0221          | 0.8039   |
| 0.0345        | 33.0  | 660  | 1.0239          | 0.7843   |
| 0.035         | 34.0  | 680  | 1.0163          | 0.8039   |
| 0.0367        | 35.0  | 700  | 1.0902          | 0.8039   |
| 0.0219        | 36.0  | 720  | 1.1079          | 0.7843   |
| 0.0263        | 37.0  | 740  | 1.0727          | 0.8039   |
| 0.0261        | 38.0  | 760  | 1.0471          | 0.8039   |
| 0.0193        | 39.0  | 780  | 1.0347          | 0.8039   |
| 0.0301        | 40.0  | 800  | 1.0319          | 0.8039   |


### Framework versions

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

avanzada leve moderada no dmae