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---
library_name: transformers
license: apache-2.0
base_model: microsoft/swinv2-tiny-patch4-window8-256
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: swinv2-tiny-patch4-window8-256-dmae-humeda-DAV23
results: []
---
<!-- 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-DAV23
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: 0.6901
- Accuracy: 0.8118
## 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 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: 40
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 6.4493 | 1.0 | 17 | 1.5281 | 0.2941 |
| 5.7922 | 2.0 | 34 | 1.3176 | 0.3882 |
| 4.2502 | 3.0 | 51 | 1.2015 | 0.4353 |
| 3.2402 | 4.0 | 68 | 0.8902 | 0.7176 |
| 2.5386 | 5.0 | 85 | 0.6509 | 0.7765 |
| 2.0351 | 6.0 | 102 | 0.6759 | 0.7647 |
| 1.8225 | 7.0 | 119 | 0.6607 | 0.7765 |
| 1.4778 | 8.0 | 136 | 0.7162 | 0.7529 |
| 1.4076 | 9.0 | 153 | 0.9084 | 0.7294 |
| 1.2056 | 10.0 | 170 | 0.6901 | 0.8118 |
| 0.9552 | 11.0 | 187 | 0.9153 | 0.7765 |
| 0.9859 | 12.0 | 204 | 0.8694 | 0.7529 |
| 0.8309 | 13.0 | 221 | 0.7666 | 0.8 |
| 0.7722 | 14.0 | 238 | 0.9118 | 0.7529 |
| 0.7632 | 15.0 | 255 | 0.8953 | 0.7529 |
| 0.5868 | 16.0 | 272 | 0.9678 | 0.7529 |
| 0.6577 | 17.0 | 289 | 1.0503 | 0.7765 |
| 0.5816 | 18.0 | 306 | 1.0602 | 0.7294 |
| 0.6222 | 19.0 | 323 | 1.1543 | 0.7765 |
| 0.4861 | 20.0 | 340 | 0.9739 | 0.8118 |
| 0.4422 | 21.0 | 357 | 1.0354 | 0.8 |
| 0.506 | 22.0 | 374 | 1.1097 | 0.8118 |
| 0.3833 | 23.0 | 391 | 1.2009 | 0.7765 |
| 0.4574 | 24.0 | 408 | 1.1366 | 0.7765 |
| 0.4467 | 25.0 | 425 | 1.0601 | 0.8118 |
| 0.4451 | 26.0 | 442 | 1.0935 | 0.7765 |
| 0.4384 | 27.0 | 459 | 1.1617 | 0.7647 |
| 0.4321 | 28.0 | 476 | 1.1012 | 0.7765 |
| 0.4398 | 29.0 | 493 | 1.0825 | 0.7882 |
| 0.361 | 30.0 | 510 | 1.1127 | 0.7647 |
| 0.4428 | 31.0 | 527 | 1.2024 | 0.7529 |
| 0.451 | 32.0 | 544 | 1.1550 | 0.7647 |
| 0.403 | 33.0 | 561 | 1.1646 | 0.7765 |
| 0.3059 | 34.0 | 578 | 1.2442 | 0.7765 |
| 0.3022 | 35.0 | 595 | 1.1976 | 0.7765 |
| 0.319 | 36.0 | 612 | 1.1564 | 0.7765 |
| 0.3737 | 37.0 | 629 | 1.1857 | 0.7765 |
| 0.3063 | 37.6667 | 640 | 1.1930 | 0.7765 |
### Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
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