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swinv2-tiny-patch4-window8-256-finetuned-200k
This model is a fine-tuned version of microsoft/swinv2-tiny-patch4-window8-256 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.3715
- Accuracy: 0.8360
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: 256
- eval_batch_size: 256
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 1024
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.7044 | 0.93 | 10 | 0.6932 | 0.5410 |
| 0.6768 | 1.95 | 21 | 0.6407 | 0.6614 |
| 0.6359 | 2.98 | 32 | 0.5647 | 0.7208 |
| 0.5989 | 4.0 | 43 | 0.5674 | 0.7086 |
| 0.5831 | 4.93 | 53 | 0.5108 | 0.7679 |
| 0.549 | 5.95 | 64 | 0.4882 | 0.7836 |
| 0.5341 | 6.98 | 75 | 0.4831 | 0.7714 |
| 0.5172 | 8.0 | 86 | 0.4422 | 0.8115 |
| 0.4961 | 8.93 | 96 | 0.4422 | 0.7941 |
| 0.4796 | 9.95 | 107 | 0.4066 | 0.8098 |
| 0.4776 | 10.98 | 118 | 0.3906 | 0.8185 |
| 0.4668 | 12.0 | 129 | 0.4135 | 0.8150 |
| 0.4588 | 12.93 | 139 | 0.3884 | 0.8202 |
| 0.448 | 13.95 | 150 | 0.3764 | 0.8220 |
| 0.4508 | 14.98 | 161 | 0.3802 | 0.8220 |
| 0.43 | 16.0 | 172 | 0.3829 | 0.8150 |
| 0.4347 | 16.93 | 182 | 0.3857 | 0.8133 |
| 0.4232 | 17.95 | 193 | 0.3819 | 0.8150 |
| 0.4289 | 18.98 | 204 | 0.4055 | 0.8080 |
| 0.4271 | 20.0 | 215 | 0.3577 | 0.8377 |
| 0.4301 | 20.93 | 225 | 0.3598 | 0.8272 |
| 0.4257 | 21.95 | 236 | 0.3780 | 0.8237 |
| 0.4191 | 22.98 | 247 | 0.3545 | 0.8307 |
| 0.4164 | 24.0 | 258 | 0.4208 | 0.8115 |
| 0.4297 | 24.93 | 268 | 0.3817 | 0.8290 |
| 0.4168 | 25.95 | 279 | 0.3876 | 0.8220 |
| 0.4118 | 26.98 | 290 | 0.3670 | 0.8307 |
| 0.4042 | 28.0 | 301 | 0.3620 | 0.8290 |
| 0.4018 | 28.93 | 311 | 0.3670 | 0.8290 |
| 0.4074 | 29.95 | 322 | 0.3822 | 0.8290 |
| 0.4044 | 30.98 | 333 | 0.3561 | 0.8325 |
| 0.3998 | 32.0 | 344 | 0.3642 | 0.8377 |
| 0.3994 | 32.93 | 354 | 0.3721 | 0.8290 |
| 0.3982 | 33.95 | 365 | 0.3592 | 0.8394 |
| 0.4002 | 34.98 | 376 | 0.3740 | 0.8290 |
| 0.4014 | 36.0 | 387 | 0.3705 | 0.8325 |
| 0.3953 | 36.93 | 397 | 0.3865 | 0.8237 |
| 0.3934 | 37.95 | 408 | 0.3689 | 0.8342 |
| 0.3964 | 38.98 | 419 | 0.3570 | 0.8255 |
| 0.4027 | 40.0 | 430 | 0.3738 | 0.8325 |
| 0.392 | 40.93 | 440 | 0.3566 | 0.8342 |
| 0.3875 | 41.95 | 451 | 0.3652 | 0.8377 |
| 0.3866 | 42.98 | 462 | 0.3657 | 0.8342 |
| 0.396 | 44.0 | 473 | 0.3662 | 0.8342 |
| 0.3841 | 44.93 | 483 | 0.3764 | 0.8360 |
| 0.387 | 45.95 | 494 | 0.3687 | 0.8325 |
| 0.3844 | 46.51 | 500 | 0.3715 | 0.8360 |
Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
