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# cards-blt-swin-tiny-patch4-window7-224-finetuned-v2

This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2162
- Accuracy: 0.5022

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4297        | 1.0   | 56   | 1.1976          | 0.4933   |
| 1.4078        | 1.99  | 112  | 1.1964          | 0.5011   |
| 1.417         | 2.99  | 168  | 1.2025          | 0.4961   |
| 1.4163        | 4.0   | 225  | 1.2295          | 0.4883   |
| 1.4318        | 5.0   | 281  | 1.2330          | 0.495    |
| 1.4383        | 5.99  | 337  | 1.2162          | 0.5022   |
| 1.4212        | 6.99  | 393  | 1.2634          | 0.4717   |
| 1.4346        | 8.0   | 450  | 1.3083          | 0.4689   |
| 1.419         | 9.0   | 506  | 1.2719          | 0.4806   |
| 1.4252        | 9.99  | 562  | 1.3048          | 0.4911   |
| 1.4522        | 10.99 | 618  | 1.2708          | 0.4794   |
| 1.3748        | 12.0  | 675  | 1.3720          | 0.4383   |
| 1.3966        | 13.0  | 731  | 1.3095          | 0.4594   |
| 1.4507        | 13.99 | 787  | 1.2430          | 0.485    |
| 1.4033        | 14.99 | 843  | 1.2728          | 0.4794   |
| 1.3972        | 16.0  | 900  | 1.2611          | 0.4883   |
| 1.4136        | 17.0  | 956  | 1.3166          | 0.45     |
| 1.3992        | 17.99 | 1012 | 1.3103          | 0.4856   |
| 1.3614        | 18.99 | 1068 | 1.3302          | 0.4422   |
| 1.3747        | 20.0  | 1125 | 1.2919          | 0.4856   |
| 1.3868        | 21.0  | 1181 | 1.3166          | 0.4728   |
| 1.3399        | 21.99 | 1237 | 1.3200          | 0.4672   |
| 1.3943        | 22.99 | 1293 | 1.2920          | 0.4811   |
| 1.3635        | 24.0  | 1350 | 1.3109          | 0.4833   |
| 1.3724        | 25.0  | 1406 | 1.3100          | 0.4644   |
| 1.3141        | 25.99 | 1462 | 1.3263          | 0.4978   |
| 1.3576        | 26.99 | 1518 | 1.3307          | 0.4772   |
| 1.3022        | 28.0  | 1575 | 1.3409          | 0.4978   |
| 1.2982        | 29.0  | 1631 | 1.3962          | 0.4583   |
| 1.2657        | 29.99 | 1687 | 1.3329          | 0.4817   |
| 1.3152        | 30.99 | 1743 | 1.2973          | 0.49     |
| 1.2924        | 32.0  | 1800 | 1.3159          | 0.4833   |
| 1.214         | 33.0  | 1856 | 1.3955          | 0.4833   |
| 1.2717        | 33.99 | 1912 | 1.4583          | 0.46     |
| 1.2692        | 34.99 | 1968 | 1.3504          | 0.4939   |
| 1.2127        | 36.0  | 2025 | 1.3784          | 0.4833   |
| 1.1956        | 37.0  | 2081 | 1.4184          | 0.4817   |
| 1.2408        | 37.99 | 2137 | 1.3849          | 0.4944   |
| 1.1699        | 38.99 | 2193 | 1.4298          | 0.4844   |
| 1.1727        | 40.0  | 2250 | 1.4331          | 0.4772   |
| 1.1485        | 41.0  | 2306 | 1.4597          | 0.4672   |
| 1.1668        | 41.99 | 2362 | 1.4429          | 0.4783   |
| 1.1881        | 42.99 | 2418 | 1.4555          | 0.4839   |
| 1.1204        | 44.0  | 2475 | 1.4648          | 0.4783   |
| 1.1523        | 45.0  | 2531 | 1.4744          | 0.4733   |
| 1.1206        | 45.99 | 2587 | 1.4792          | 0.4906   |
| 1.1135        | 46.99 | 2643 | 1.5009          | 0.4678   |
| 1.1227        | 48.0  | 2700 | 1.5480          | 0.4733   |
| 1.1017        | 49.0  | 2756 | 1.5907          | 0.4644   |
| 1.1601        | 49.99 | 2812 | 1.5136          | 0.47     |
| 1.1239        | 50.99 | 2868 | 1.5384          | 0.4789   |
| 1.09          | 52.0  | 2925 | 1.5716          | 0.4711   |
| 1.1023        | 53.0  | 2981 | 1.5736          | 0.4728   |
| 1.1038        | 53.99 | 3037 | 1.5919          | 0.4556   |
| 1.058         | 54.99 | 3093 | 1.5534          | 0.4772   |
| 1.0405        | 56.0  | 3150 | 1.5788          | 0.4717   |
| 1.0172        | 57.0  | 3206 | 1.5855          | 0.4767   |
| 1.0036        | 57.99 | 3262 | 1.6425          | 0.455    |
| 1.0124        | 58.99 | 3318 | 1.6039          | 0.4678   |
| 1.0647        | 60.0  | 3375 | 1.5891          | 0.4572   |
| 1.0143        | 61.0  | 3431 | 1.6265          | 0.4483   |
| 1.0051        | 61.99 | 3487 | 1.6208          | 0.4633   |
| 0.9571        | 62.99 | 3543 | 1.6874          | 0.4483   |
| 0.9838        | 64.0  | 3600 | 1.6778          | 0.4517   |
| 0.9995        | 65.0  | 3656 | 1.6248          | 0.4722   |
| 1.0374        | 65.99 | 3712 | 1.6645          | 0.4667   |
| 0.9483        | 66.99 | 3768 | 1.6307          | 0.4611   |
| 0.9825        | 68.0  | 3825 | 1.6662          | 0.4661   |
| 1.0023        | 69.0  | 3881 | 1.6650          | 0.46     |
| 0.9642        | 69.99 | 3937 | 1.6953          | 0.4494   |
| 0.9687        | 70.99 | 3993 | 1.7076          | 0.4661   |
| 0.9542        | 72.0  | 4050 | 1.7012          | 0.4656   |
| 0.9378        | 73.0  | 4106 | 1.7056          | 0.4533   |
| 0.9542        | 73.99 | 4162 | 1.7331          | 0.4572   |
| 0.9035        | 74.99 | 4218 | 1.7459          | 0.4417   |
| 0.9631        | 76.0  | 4275 | 1.7236          | 0.465    |
| 0.8759        | 77.0  | 4331 | 1.7294          | 0.455    |
| 0.9218        | 77.99 | 4387 | 1.7654          | 0.4578   |
| 0.9077        | 78.99 | 4443 | 1.7234          | 0.4594   |
| 0.8924        | 80.0  | 4500 | 1.7256          | 0.4683   |
| 0.9156        | 81.0  | 4556 | 1.7320          | 0.4678   |
| 0.806         | 81.99 | 4612 | 1.7348          | 0.4661   |
| 0.8863        | 82.99 | 4668 | 1.7514          | 0.4606   |
| 0.8698        | 84.0  | 4725 | 1.7484          | 0.4661   |
| 0.8623        | 85.0  | 4781 | 1.7420          | 0.4778   |
| 0.8643        | 85.99 | 4837 | 1.7636          | 0.4617   |
| 0.8914        | 86.99 | 4893 | 1.7552          | 0.465    |
| 0.837         | 88.0  | 4950 | 1.7552          | 0.4644   |
| 0.8217        | 89.0  | 5006 | 1.7532          | 0.4639   |
| 0.8601        | 89.99 | 5062 | 1.7447          | 0.4683   |
| 0.8293        | 90.99 | 5118 | 1.7622          | 0.4611   |
| 0.8301        | 92.0  | 5175 | 1.7616          | 0.4633   |
| 0.7752        | 93.0  | 5231 | 1.7585          | 0.4722   |
| 0.8533        | 93.99 | 5287 | 1.7842          | 0.4617   |
| 0.8156        | 94.99 | 5343 | 1.7837          | 0.4622   |
| 0.8094        | 96.0  | 5400 | 1.7896          | 0.4583   |
| 0.839         | 97.0  | 5456 | 1.7835          | 0.465    |
| 0.839         | 97.99 | 5512 | 1.7883          | 0.46     |
| 0.7763        | 98.99 | 5568 | 1.7838          | 0.4594   |
| 0.8186        | 99.56 | 5600 | 1.7837          | 0.4606   |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.0.1+cu117
- Datasets 2.17.0
- Tokenizers 0.15.2

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