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# swin-tiny-patch4-window7-224-finetuned-eurosat_animals

This model was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0772
- Accuracy: 0.9817

## 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: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1783        | 0.98  | 37   | 2.0846          | 0.1985   |
| 1.6593        | 1.99  | 75   | 1.2526          | 0.7228   |
| 0.47          | 2.99  | 113  | 0.2017          | 0.9494   |
| 0.2459        | 4.0   | 151  | 0.1004          | 0.9728   |
| 0.1891        | 4.98  | 188  | 0.0877          | 0.9785   |
| 0.1638        | 5.99  | 226  | 0.0666          | 0.9831   |
| 0.154         | 6.99  | 264  | 0.0693          | 0.9803   |
| 0.1542        | 8.0   | 302  | 0.0646          | 0.9822   |
| 0.1305        | 8.98  | 339  | 0.0663          | 0.9822   |
| 0.1337        | 9.99  | 377  | 0.0593          | 0.9841   |
| 0.129         | 10.99 | 415  | 0.0593          | 0.9836   |
| 0.1179        | 12.0  | 453  | 0.0598          | 0.9836   |
| 0.1025        | 12.98 | 490  | 0.0636          | 0.9813   |
| 0.0993        | 13.99 | 528  | 0.0637          | 0.9817   |
| 0.1043        | 14.99 | 566  | 0.0578          | 0.9827   |
| 0.0996        | 16.0  | 604  | 0.0644          | 0.9831   |
| 0.0866        | 16.98 | 641  | 0.0813          | 0.9785   |
| 0.0879        | 17.99 | 679  | 0.0734          | 0.9813   |
| 0.0812        | 18.99 | 717  | 0.0639          | 0.9855   |
| 0.0864        | 20.0  | 755  | 0.0619          | 0.9860   |
| 0.086         | 20.98 | 792  | 0.0693          | 0.9794   |
| 0.0781        | 21.99 | 830  | 0.0638          | 0.9831   |
| 0.0826        | 22.99 | 868  | 0.0681          | 0.9822   |
| 0.074         | 24.0  | 906  | 0.0687          | 0.9817   |
| 0.0828        | 24.98 | 943  | 0.0738          | 0.9836   |
| 0.0727        | 25.99 | 981  | 0.0655          | 0.9827   |
| 0.0692        | 26.99 | 1019 | 0.0713          | 0.9836   |
| 0.0696        | 28.0  | 1057 | 0.0729          | 0.9817   |
| 0.0792        | 28.98 | 1094 | 0.0707          | 0.9836   |
| 0.0657        | 29.99 | 1132 | 0.0647          | 0.9827   |
| 0.0747        | 30.99 | 1170 | 0.0769          | 0.9808   |
| 0.0861        | 32.0  | 1208 | 0.0665          | 0.9841   |
| 0.0693        | 32.98 | 1245 | 0.0617          | 0.9850   |
| 0.0682        | 33.99 | 1283 | 0.0636          | 0.9855   |
| 0.0615        | 34.99 | 1321 | 0.0685          | 0.9841   |
| 0.0581        | 36.0  | 1359 | 0.0702          | 0.9822   |
| 0.0713        | 36.98 | 1396 | 0.0675          | 0.9855   |
| 0.0593        | 37.99 | 1434 | 0.0697          | 0.9827   |
| 0.0543        | 38.99 | 1472 | 0.0701          | 0.9831   |
| 0.0628        | 40.0  | 1510 | 0.0720          | 0.9799   |
| 0.0606        | 40.98 | 1547 | 0.0794          | 0.9808   |
| 0.0619        | 41.99 | 1585 | 0.0720          | 0.9827   |
| 0.0612        | 42.99 | 1623 | 0.0768          | 0.9813   |
| 0.0435        | 44.0  | 1661 | 0.0748          | 0.9831   |
| 0.0614        | 44.98 | 1698 | 0.0738          | 0.9841   |
| 0.0544        | 45.99 | 1736 | 0.0750          | 0.9822   |
| 0.0569        | 46.99 | 1774 | 0.0802          | 0.9803   |
| 0.0527        | 48.0  | 1812 | 0.0772          | 0.9831   |
| 0.0535        | 48.98 | 1849 | 0.0724          | 0.9831   |
| 0.063         | 49.99 | 1887 | 0.0736          | 0.9831   |
| 0.0534        | 50.99 | 1925 | 0.0767          | 0.9822   |
| 0.0515        | 52.0  | 1963 | 0.0736          | 0.9817   |
| 0.0522        | 52.98 | 2000 | 0.0739          | 0.9827   |
| 0.0474        | 53.99 | 2038 | 0.0687          | 0.9831   |
| 0.0515        | 54.99 | 2076 | 0.0675          | 0.9846   |
| 0.0558        | 56.0  | 2114 | 0.0676          | 0.9822   |
| 0.0461        | 56.98 | 2151 | 0.0714          | 0.9813   |
| 0.0532        | 57.99 | 2189 | 0.0753          | 0.9803   |
| 0.0539        | 58.99 | 2227 | 0.0826          | 0.9803   |
| 0.0428        | 60.0  | 2265 | 0.0785          | 0.9827   |
| 0.0361        | 60.98 | 2302 | 0.0821          | 0.9813   |
| 0.0515        | 61.99 | 2340 | 0.0813          | 0.9817   |
| 0.047         | 62.99 | 2378 | 0.0817          | 0.9813   |
| 0.046         | 64.0  | 2416 | 0.0765          | 0.9827   |
| 0.039         | 64.98 | 2453 | 0.0794          | 0.9827   |
| 0.0399        | 65.99 | 2491 | 0.0805          | 0.9822   |
| 0.0478        | 66.99 | 2529 | 0.0758          | 0.9817   |
| 0.0426        | 68.0  | 2567 | 0.0726          | 0.9831   |
| 0.0383        | 68.98 | 2604 | 0.0785          | 0.9827   |
| 0.0407        | 69.99 | 2642 | 0.0795          | 0.9813   |
| 0.0413        | 70.99 | 2680 | 0.0774          | 0.9822   |
| 0.0428        | 72.0  | 2718 | 0.0765          | 0.9827   |
| 0.0375        | 72.98 | 2755 | 0.0730          | 0.9836   |
| 0.0428        | 73.99 | 2793 | 0.0715          | 0.9841   |
| 0.0443        | 74.99 | 2831 | 0.0744          | 0.9841   |
| 0.0383        | 76.0  | 2869 | 0.0748          | 0.9822   |
| 0.0338        | 76.98 | 2906 | 0.0818          | 0.9808   |
| 0.0445        | 77.99 | 2944 | 0.0759          | 0.9817   |
| 0.0374        | 78.99 | 2982 | 0.0757          | 0.9827   |
| 0.0404        | 80.0  | 3020 | 0.0793          | 0.9813   |
| 0.0336        | 80.98 | 3057 | 0.0750          | 0.9827   |
| 0.0364        | 81.99 | 3095 | 0.0816          | 0.9813   |
| 0.0403        | 82.99 | 3133 | 0.0795          | 0.9822   |
| 0.0287        | 84.0  | 3171 | 0.0818          | 0.9803   |
| 0.0425        | 84.98 | 3208 | 0.0819          | 0.9803   |
| 0.0446        | 85.99 | 3246 | 0.0775          | 0.9813   |
| 0.0341        | 86.99 | 3284 | 0.0772          | 0.9803   |
| 0.0414        | 88.0  | 3322 | 0.0757          | 0.9813   |
| 0.0401        | 88.98 | 3359 | 0.0751          | 0.9822   |
| 0.0442        | 89.99 | 3397 | 0.0775          | 0.9827   |
| 0.046         | 90.99 | 3435 | 0.0794          | 0.9808   |
| 0.0378        | 92.0  | 3473 | 0.0773          | 0.9822   |
| 0.0343        | 92.98 | 3510 | 0.0759          | 0.9817   |
| 0.0348        | 93.99 | 3548 | 0.0766          | 0.9822   |
| 0.0409        | 94.99 | 3586 | 0.0773          | 0.9817   |
| 0.0389        | 96.0  | 3624 | 0.0770          | 0.9822   |
| 0.0362        | 96.98 | 3661 | 0.0773          | 0.9817   |
| 0.0317        | 97.99 | 3699 | 0.0772          | 0.9817   |
| 0.0244        | 98.01 | 3700 | 0.0772          | 0.9817   |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.1.2+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2

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