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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-DAV48
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-DAV48
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: 0.7283
- Accuracy: 0.75
## 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: 2e-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: reduce_lr_on_plateau
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log | 0.9412 | 8 | 1.4387 | 0.4432 |
| 1.5789 | 1.9412 | 16 | 1.3131 | 0.5568 |
| 1.3907 | 2.9412 | 24 | 1.1805 | 0.5909 |
| 1.3907 | 3.9412 | 32 | 1.0386 | 0.6136 |
| 1.1967 | 4.9412 | 40 | 1.0065 | 0.6136 |
| 1.0098 | 5.9412 | 48 | 0.8786 | 0.6477 |
| 1.0098 | 6.9412 | 56 | 0.8264 | 0.6932 |
| 0.863 | 7.9412 | 64 | 0.8026 | 0.7273 |
| 0.7309 | 8.9412 | 72 | 0.7853 | 0.7159 |
| 0.7309 | 9.9412 | 80 | 0.7649 | 0.7273 |
| 0.6597 | 10.9412 | 88 | 0.7671 | 0.7386 |
| 0.56 | 11.9412 | 96 | 0.7551 | 0.7159 |
| 0.56 | 12.9412 | 104 | 0.7428 | 0.7273 |
| 0.5207 | 13.9412 | 112 | 0.7396 | 0.7273 |
| 0.5108 | 14.9412 | 120 | 0.7368 | 0.7273 |
| 0.5108 | 15.9412 | 128 | 0.7366 | 0.7386 |
| 0.5062 | 16.9412 | 136 | 0.7364 | 0.7273 |
| 0.5069 | 17.9412 | 144 | 0.7329 | 0.7386 |
| 0.5069 | 18.9412 | 152 | 0.7285 | 0.7273 |
| 0.4952 | 19.9412 | 160 | 0.7371 | 0.7386 |
| 0.4979 | 20.9412 | 168 | 0.7436 | 0.7386 |
| 0.4979 | 21.9412 | 176 | 0.7338 | 0.7386 |
| 0.4745 | 22.9412 | 184 | 0.7291 | 0.75 |
| 0.4735 | 23.9412 | 192 | 0.7305 | 0.75 |
| 0.4735 | 24.9412 | 200 | 0.7301 | 0.75 |
| 0.4862 | 25.9412 | 208 | 0.7283 | 0.75 |
| 0.4955 | 26.9412 | 216 | 0.7273 | 0.75 |
| 0.4955 | 27.9412 | 224 | 0.7275 | 0.75 |
| 0.4602 | 28.9412 | 232 | 0.7280 | 0.75 |
| 0.4714 | 29.9412 | 240 | 0.7291 | 0.75 |
| 0.4714 | 30.9412 | 248 | 0.7298 | 0.75 |
| 0.4727 | 31.9412 | 256 | 0.7301 | 0.75 |
| 0.4689 | 32.9412 | 264 | 0.7293 | 0.75 |
| 0.4689 | 33.9412 | 272 | 0.7287 | 0.75 |
| 0.4725 | 34.9412 | 280 | 0.7287 | 0.75 |
| 0.4747 | 35.9412 | 288 | 0.7284 | 0.75 |
| 0.4747 | 36.9412 | 296 | 0.7284 | 0.75 |
| 0.5012 | 37.9412 | 304 | 0.7284 | 0.75 |
| 0.462 | 38.9412 | 312 | 0.7286 | 0.75 |
| 0.462 | 39.9412 | 320 | 0.7283 | 0.75 |
### Framework versions
- Transformers 4.48.2
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
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