
<!-- 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. -->

# swinv2-tiny-patch4-window8-256-dmae-humeda-DAV74

This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3840
- Accuracy: 0.9314

## 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: 4e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1566        | 1.0   | 15   | 1.1332          | 0.2286   |
| 1.0369        | 2.0   | 30   | 0.9972          | 0.6171   |
| 0.9264        | 3.0   | 45   | 0.6970          | 0.7714   |
| 0.629         | 4.0   | 60   | 0.4858          | 0.8286   |
| 0.5428        | 5.0   | 75   | 0.4445          | 0.8171   |
| 0.4508        | 6.0   | 90   | 0.3967          | 0.8457   |
| 0.4188        | 7.0   | 105  | 0.3711          | 0.8286   |
| 0.3688        | 8.0   | 120  | 0.3448          | 0.8629   |
| 0.3503        | 9.0   | 135  | 0.3299          | 0.8743   |
| 0.2885        | 10.0  | 150  | 0.2780          | 0.8971   |
| 0.2582        | 11.0  | 165  | 0.3197          | 0.8686   |
| 0.3041        | 12.0  | 180  | 0.4322          | 0.8514   |
| 0.2957        | 13.0  | 195  | 0.4167          | 0.84     |
| 0.2821        | 14.0  | 210  | 0.4550          | 0.84     |
| 0.2713        | 15.0  | 225  | 0.4461          | 0.8343   |
| 0.2402        | 16.0  | 240  | 0.3447          | 0.8971   |
| 0.1729        | 17.0  | 255  | 0.3471          | 0.8914   |
| 0.1773        | 18.0  | 270  | 0.3235          | 0.8914   |
| 0.155         | 19.0  | 285  | 0.3583          | 0.8857   |
| 0.1867        | 20.0  | 300  | 0.3917          | 0.88     |
| 0.159         | 21.0  | 315  | 0.3539          | 0.8743   |
| 0.1498        | 22.0  | 330  | 0.3438          | 0.8629   |
| 0.1585        | 23.0  | 345  | 0.3092          | 0.8914   |
| 0.1655        | 24.0  | 360  | 0.2937          | 0.9029   |
| 0.1191        | 25.0  | 375  | 0.3470          | 0.8971   |
| 0.1756        | 26.0  | 390  | 0.3520          | 0.8857   |
| 0.1467        | 27.0  | 405  | 0.3552          | 0.88     |
| 0.1788        | 28.0  | 420  | 0.3994          | 0.8914   |
| 0.108         | 29.0  | 435  | 0.3685          | 0.9143   |
| 0.1359        | 30.0  | 450  | 0.3831          | 0.9086   |
| 0.0982        | 31.0  | 465  | 0.4162          | 0.8971   |
| 0.1154        | 32.0  | 480  | 0.3634          | 0.9086   |
| 0.0959        | 33.0  | 495  | 0.3828          | 0.8914   |
| 0.1136        | 34.0  | 510  | 0.4778          | 0.88     |
| 0.1184        | 35.0  | 525  | 0.3773          | 0.8857   |
| 0.0858        | 36.0  | 540  | 0.3628          | 0.8971   |
| 0.0958        | 37.0  | 555  | 0.4379          | 0.8857   |
| 0.0891        | 38.0  | 570  | 0.3993          | 0.8857   |
| 0.1132        | 39.0  | 585  | 0.3931          | 0.8857   |
| 0.093         | 40.0  | 600  | 0.3840          | 0.9314   |
| 0.094         | 41.0  | 615  | 0.3870          | 0.9029   |
| 0.0938        | 42.0  | 630  | 0.4061          | 0.8914   |
| 0.1024        | 43.0  | 645  | 0.5408          | 0.8571   |
| 0.0835        | 44.0  | 660  | 0.3230          | 0.9086   |
| 0.0795        | 45.0  | 675  | 0.3599          | 0.9086   |
| 0.053         | 46.0  | 690  | 0.4675          | 0.8686   |
| 0.0673        | 47.0  | 705  | 0.4926          | 0.8857   |
| 0.0888        | 48.0  | 720  | 0.4276          | 0.8971   |
| 0.0539        | 49.0  | 735  | 0.5204          | 0.8686   |
| 0.0662        | 50.0  | 750  | 0.4685          | 0.9143   |
| 0.0738        | 51.0  | 765  | 0.3824          | 0.9143   |
| 0.0459        | 52.0  | 780  | 0.4200          | 0.9143   |
| 0.0651        | 53.0  | 795  | 0.4323          | 0.9086   |
| 0.0744        | 54.0  | 810  | 0.4256          | 0.9086   |
| 0.0499        | 55.0  | 825  | 0.4435          | 0.9143   |
| 0.076         | 56.0  | 840  | 0.4884          | 0.9086   |
| 0.0689        | 57.0  | 855  | 0.4900          | 0.9029   |
| 0.0899        | 58.0  | 870  | 0.5848          | 0.8971   |
| 0.054         | 59.0  | 885  | 0.5400          | 0.9143   |
| 0.0524        | 60.0  | 900  | 0.5701          | 0.8914   |
| 0.0532        | 61.0  | 915  | 0.5269          | 0.9029   |
| 0.0596        | 62.0  | 930  | 0.5223          | 0.88     |
| 0.0814        | 63.0  | 945  | 0.5199          | 0.8914   |
| 0.0899        | 64.0  | 960  | 0.4566          | 0.9143   |
| 0.0649        | 65.0  | 975  | 0.4577          | 0.92     |
| 0.0508        | 66.0  | 990  | 0.4655          | 0.92     |
| 0.0529        | 67.0  | 1005 | 0.4868          | 0.9029   |
| 0.0707        | 68.0  | 1020 | 0.4883          | 0.9029   |
| 0.0603        | 69.0  | 1035 | 0.5059          | 0.9029   |
| 0.0776        | 70.0  | 1050 | 0.5878          | 0.8857   |
| 0.0747        | 71.0  | 1065 | 0.4694          | 0.92     |
| 0.0681        | 72.0  | 1080 | 0.5228          | 0.8857   |
| 0.0357        | 73.0  | 1095 | 0.5152          | 0.8857   |
| 0.049         | 74.0  | 1110 | 0.5197          | 0.9086   |
| 0.0335        | 75.0  | 1125 | 0.5028          | 0.9143   |
| 0.0523        | 76.0  | 1140 | 0.6165          | 0.8857   |
| 0.0258        | 77.0  | 1155 | 0.5491          | 0.8971   |
| 0.0398        | 78.0  | 1170 | 0.5218          | 0.8971   |
| 0.0408        | 79.0  | 1185 | 0.6784          | 0.8743   |
| 0.0606        | 80.0  | 1200 | 0.4997          | 0.9086   |
| 0.062         | 81.0  | 1215 | 0.5853          | 0.8857   |
| 0.0485        | 82.0  | 1230 | 0.5230          | 0.9086   |
| 0.0337        | 83.0  | 1245 | 0.5579          | 0.8971   |
| 0.0474        | 84.0  | 1260 | 0.5233          | 0.8971   |
| 0.0542        | 85.0  | 1275 | 0.5082          | 0.9143   |
| 0.0512        | 86.0  | 1290 | 0.5149          | 0.9029   |
| 0.0497        | 87.0  | 1305 | 0.5065          | 0.8971   |
| 0.0317        | 88.0  | 1320 | 0.4929          | 0.9143   |
| 0.0507        | 89.0  | 1335 | 0.5252          | 0.9143   |
| 0.0229        | 90.0  | 1350 | 0.5545          | 0.8914   |
| 0.0248        | 91.0  | 1365 | 0.5472          | 0.8914   |
| 0.0499        | 92.0  | 1380 | 0.5360          | 0.8971   |
| 0.0241        | 93.0  | 1395 | 0.5291          | 0.8971   |
| 0.0458        | 94.0  | 1410 | 0.5110          | 0.9029   |
| 0.0304        | 95.0  | 1425 | 0.5070          | 0.9029   |
| 0.1043        | 96.0  | 1440 | 0.5084          | 0.9086   |
| 0.0454        | 97.0  | 1455 | 0.5128          | 0.9086   |
| 0.0474        | 98.0  | 1470 | 0.5270          | 0.8971   |
| 0.0325        | 99.0  | 1485 | 0.5307          | 0.8971   |
| 0.0273        | 100.0 | 1500 | 0.5334          | 0.8971   |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 2.19.0
- Tokenizers 0.21.1

avanzada avanzada humeda no dmae