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# vit-base-patch16-224-ve-U16-b-80

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: 0.5265
- Accuracy: 0.8696

## 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: 80

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 8    | 1.3828          | 0.4565   |
| 1.3846        | 2.0   | 16   | 1.3610          | 0.5      |
| 1.3611        | 3.0   | 24   | 1.2967          | 0.4348   |
| 1.2759        | 4.0   | 32   | 1.1830          | 0.3913   |
| 1.1164        | 5.0   | 40   | 1.0824          | 0.3696   |
| 1.1164        | 6.0   | 48   | 0.9665          | 0.5      |
| 0.98          | 7.0   | 56   | 0.9036          | 0.5652   |
| 0.8533        | 8.0   | 64   | 0.8348          | 0.7826   |
| 0.7321        | 9.0   | 72   | 0.7397          | 0.8261   |
| 0.6075        | 10.0  | 80   | 0.7155          | 0.7174   |
| 0.6075        | 11.0  | 88   | 0.6006          | 0.8261   |
| 0.4901        | 12.0  | 96   | 0.5265          | 0.8696   |
| 0.3967        | 13.0  | 104  | 0.5214          | 0.8043   |
| 0.2746        | 14.0  | 112  | 0.5433          | 0.7826   |
| 0.2366        | 15.0  | 120  | 0.6141          | 0.7826   |
| 0.2366        | 16.0  | 128  | 0.6658          | 0.7826   |
| 0.2247        | 17.0  | 136  | 0.6327          | 0.7609   |
| 0.2047        | 18.0  | 144  | 0.5339          | 0.8261   |
| 0.1592        | 19.0  | 152  | 0.6647          | 0.8043   |
| 0.1349        | 20.0  | 160  | 0.7483          | 0.7609   |
| 0.1349        | 21.0  | 168  | 0.7387          | 0.8043   |
| 0.1285        | 22.0  | 176  | 0.8261          | 0.7609   |
| 0.1104        | 23.0  | 184  | 0.7151          | 0.8043   |
| 0.1191        | 24.0  | 192  | 0.7785          | 0.7609   |
| 0.1074        | 25.0  | 200  | 0.8902          | 0.7391   |
| 0.1074        | 26.0  | 208  | 0.7757          | 0.7826   |
| 0.0947        | 27.0  | 216  | 0.7157          | 0.7826   |
| 0.0973        | 28.0  | 224  | 0.8198          | 0.7826   |
| 0.0992        | 29.0  | 232  | 0.7240          | 0.8261   |
| 0.0766        | 30.0  | 240  | 0.6993          | 0.8043   |
| 0.0766        | 31.0  | 248  | 0.5688          | 0.8261   |
| 0.0606        | 32.0  | 256  | 0.6202          | 0.8478   |
| 0.0633        | 33.0  | 264  | 0.6740          | 0.8261   |
| 0.0681        | 34.0  | 272  | 0.6782          | 0.8261   |
| 0.0591        | 35.0  | 280  | 0.8370          | 0.7826   |
| 0.0591        | 36.0  | 288  | 0.6995          | 0.8261   |
| 0.0731        | 37.0  | 296  | 0.7560          | 0.8261   |
| 0.0618        | 38.0  | 304  | 0.6730          | 0.8261   |
| 0.0543        | 39.0  | 312  | 0.7166          | 0.8261   |
| 0.0574        | 40.0  | 320  | 0.7332          | 0.8261   |
| 0.0574        | 41.0  | 328  | 0.6982          | 0.8261   |
| 0.0707        | 42.0  | 336  | 0.7183          | 0.7826   |
| 0.0646        | 43.0  | 344  | 0.7568          | 0.8043   |
| 0.0476        | 44.0  | 352  | 0.8521          | 0.8043   |
| 0.047         | 45.0  | 360  | 0.8992          | 0.8043   |
| 0.047         | 46.0  | 368  | 0.8749          | 0.7826   |
| 0.0406        | 47.0  | 376  | 0.9928          | 0.7826   |
| 0.0361        | 48.0  | 384  | 0.9659          | 0.7826   |
| 0.042         | 49.0  | 392  | 0.8839          | 0.8043   |
| 0.0421        | 50.0  | 400  | 0.8613          | 0.7391   |
| 0.0421        | 51.0  | 408  | 0.9006          | 0.7826   |
| 0.0396        | 52.0  | 416  | 0.8627          | 0.7826   |
| 0.0255        | 53.0  | 424  | 0.8717          | 0.7609   |
| 0.0359        | 54.0  | 432  | 1.0508          | 0.7609   |
| 0.0424        | 55.0  | 440  | 0.9745          | 0.7826   |
| 0.0424        | 56.0  | 448  | 0.9511          | 0.8043   |
| 0.0364        | 57.0  | 456  | 0.9239          | 0.8043   |
| 0.0444        | 58.0  | 464  | 0.9500          | 0.7826   |
| 0.0445        | 59.0  | 472  | 0.9266          | 0.8261   |
| 0.0368        | 60.0  | 480  | 0.9346          | 0.8043   |
| 0.0368        | 61.0  | 488  | 0.9513          | 0.8043   |
| 0.0278        | 62.0  | 496  | 0.9505          | 0.8043   |
| 0.0324        | 63.0  | 504  | 0.9625          | 0.8261   |
| 0.0308        | 64.0  | 512  | 0.9720          | 0.8261   |
| 0.0185        | 65.0  | 520  | 0.9515          | 0.8043   |
| 0.0185        | 66.0  | 528  | 0.9278          | 0.8043   |
| 0.0323        | 67.0  | 536  | 0.9315          | 0.8261   |
| 0.0251        | 68.0  | 544  | 0.9794          | 0.8043   |
| 0.0297        | 69.0  | 552  | 1.0378          | 0.7609   |
| 0.0257        | 70.0  | 560  | 1.0336          | 0.7609   |
| 0.0257        | 71.0  | 568  | 1.0577          | 0.7826   |
| 0.02          | 72.0  | 576  | 1.0332          | 0.8043   |
| 0.0226        | 73.0  | 584  | 1.0165          | 0.8043   |
| 0.0257        | 74.0  | 592  | 1.0194          | 0.8043   |
| 0.0232        | 75.0  | 600  | 1.0026          | 0.8043   |
| 0.0232        | 76.0  | 608  | 1.0073          | 0.8043   |
| 0.0274        | 77.0  | 616  | 1.0099          | 0.8043   |
| 0.0182        | 78.0  | 624  | 1.0170          | 0.8043   |
| 0.0375        | 79.0  | 632  | 1.0139          | 0.8043   |
| 0.029         | 80.0  | 640  | 1.0128          | 0.8043   |


### Framework versions

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

avanzada leve moderada no dmae