
<!-- 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. -->

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

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: 0.8360
- Accuracy: 0.7664

## 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log        | 0.9333  | 7    | 3.8894          | 0.0841   |
| 3.897         | 2.0     | 15   | 3.8185          | 0.0841   |
| 3.8553        | 2.9333  | 22   | 3.7402          | 0.0748   |
| 3.7568        | 4.0     | 30   | 3.6372          | 0.0748   |
| 3.7568        | 4.9333  | 37   | 3.5482          | 0.0841   |
| 3.5912        | 6.0     | 45   | 3.4069          | 0.1121   |
| 3.4342        | 6.9333  | 52   | 3.2939          | 0.1308   |
| 3.2601        | 8.0     | 60   | 3.1786          | 0.2150   |
| 3.2601        | 8.9333  | 67   | 3.0323          | 0.2336   |
| 3.0498        | 10.0    | 75   | 2.8695          | 0.2617   |
| 2.849         | 10.9333 | 82   | 2.8505          | 0.2523   |
| 2.6452        | 12.0    | 90   | 2.6319          | 0.2804   |
| 2.6452        | 12.9333 | 97   | 2.4654          | 0.3271   |
| 2.4123        | 14.0    | 105  | 2.3995          | 0.3364   |
| 2.2561        | 14.9333 | 112  | 2.2584          | 0.4019   |
| 2.0447        | 16.0    | 120  | 2.2000          | 0.4299   |
| 2.0447        | 16.9333 | 127  | 2.0806          | 0.4393   |
| 1.8569        | 18.0    | 135  | 2.0593          | 0.4393   |
| 1.7447        | 18.9333 | 142  | 1.8832          | 0.4673   |
| 1.5821        | 20.0    | 150  | 1.8218          | 0.5047   |
| 1.5821        | 20.9333 | 157  | 1.7334          | 0.5421   |
| 1.3999        | 22.0    | 165  | 1.6213          | 0.5514   |
| 1.2901        | 22.9333 | 172  | 1.5932          | 0.5234   |
| 1.1569        | 24.0    | 180  | 1.5256          | 0.5701   |
| 1.1569        | 24.9333 | 187  | 1.4281          | 0.5888   |
| 1.0903        | 26.0    | 195  | 1.3997          | 0.5794   |
| 0.9674        | 26.9333 | 202  | 1.4017          | 0.5888   |
| 0.98          | 28.0    | 210  | 1.2916          | 0.5981   |
| 0.98          | 28.9333 | 217  | 1.3018          | 0.5981   |
| 0.8772        | 30.0    | 225  | 1.2552          | 0.6355   |
| 0.7842        | 30.9333 | 232  | 1.2372          | 0.6075   |
| 0.7438        | 32.0    | 240  | 1.1908          | 0.6168   |
| 0.7438        | 32.9333 | 247  | 1.1567          | 0.6636   |
| 0.725         | 34.0    | 255  | 1.1542          | 0.6262   |
| 0.6709        | 34.9333 | 262  | 1.1377          | 0.6262   |
| 0.6898        | 36.0    | 270  | 1.0524          | 0.6636   |
| 0.6898        | 36.9333 | 277  | 1.0272          | 0.6729   |
| 0.6125        | 38.0    | 285  | 1.0399          | 0.6355   |
| 0.6153        | 38.9333 | 292  | 1.0308          | 0.6822   |
| 0.5898        | 40.0    | 300  | 1.0151          | 0.7009   |
| 0.5898        | 40.9333 | 307  | 1.0483          | 0.6542   |
| 0.5881        | 42.0    | 315  | 0.9926          | 0.7009   |
| 0.54          | 42.9333 | 322  | 1.0300          | 0.6916   |
| 0.4515        | 44.0    | 330  | 0.9262          | 0.7383   |
| 0.4515        | 44.9333 | 337  | 0.9486          | 0.7290   |
| 0.5057        | 46.0    | 345  | 0.9219          | 0.7103   |
| 0.4905        | 46.9333 | 352  | 1.0184          | 0.6822   |
| 0.4669        | 48.0    | 360  | 0.9337          | 0.7290   |
| 0.4669        | 48.9333 | 367  | 0.9431          | 0.7103   |
| 0.4437        | 50.0    | 375  | 0.9312          | 0.7009   |
| 0.4754        | 50.9333 | 382  | 0.9245          | 0.7196   |
| 0.4119        | 52.0    | 390  | 0.8826          | 0.7383   |
| 0.4119        | 52.9333 | 397  | 0.9262          | 0.7196   |
| 0.4087        | 54.0    | 405  | 0.8882          | 0.7477   |
| 0.3987        | 54.9333 | 412  | 0.9282          | 0.7290   |
| 0.4253        | 56.0    | 420  | 0.9004          | 0.7477   |
| 0.4253        | 56.9333 | 427  | 0.8783          | 0.7477   |
| 0.4134        | 58.0    | 435  | 0.8360          | 0.7664   |
| 0.4024        | 58.9333 | 442  | 0.9016          | 0.7196   |
| 0.3688        | 60.0    | 450  | 0.9251          | 0.6822   |
| 0.3688        | 60.9333 | 457  | 0.9086          | 0.7103   |
| 0.3833        | 62.0    | 465  | 0.8494          | 0.7383   |
| 0.3614        | 62.9333 | 472  | 0.8299          | 0.7290   |
| 0.3792        | 64.0    | 480  | 0.9015          | 0.7383   |
| 0.3792        | 64.9333 | 487  | 0.8802          | 0.7196   |
| 0.3632        | 66.0    | 495  | 0.8881          | 0.7009   |
| 0.3405        | 66.9333 | 502  | 0.8578          | 0.7383   |
| 0.3673        | 68.0    | 510  | 0.8540          | 0.7570   |
| 0.3673        | 68.9333 | 517  | 0.8345          | 0.7383   |
| 0.3379        | 70.0    | 525  | 0.7919          | 0.7383   |
| 0.3389        | 70.9333 | 532  | 0.8384          | 0.7290   |
| 0.3363        | 72.0    | 540  | 0.8306          | 0.7383   |
| 0.3363        | 72.9333 | 547  | 0.8875          | 0.7477   |
| 0.3494        | 74.0    | 555  | 0.9151          | 0.7009   |
| 0.2989        | 74.9333 | 562  | 0.8606          | 0.7103   |
| 0.3157        | 76.0    | 570  | 0.8640          | 0.7383   |
| 0.3157        | 76.9333 | 577  | 0.8532          | 0.7290   |
| 0.3013        | 78.0    | 585  | 0.8479          | 0.7103   |
| 0.2968        | 78.9333 | 592  | 0.8839          | 0.7383   |
| 0.3013        | 80.0    | 600  | 0.8837          | 0.7196   |
| 0.3013        | 80.9333 | 607  | 0.8694          | 0.7103   |
| 0.3247        | 82.0    | 615  | 0.8721          | 0.7290   |
| 0.2515        | 82.9333 | 622  | 0.8605          | 0.7290   |
| 0.3175        | 84.0    | 630  | 0.8505          | 0.7290   |
| 0.3175        | 84.9333 | 637  | 0.8488          | 0.7290   |
| 0.3015        | 86.0    | 645  | 0.8554          | 0.7383   |
| 0.2989        | 86.9333 | 652  | 0.8707          | 0.7290   |
| 0.3155        | 88.0    | 660  | 0.8712          | 0.7290   |
| 0.3155        | 88.9333 | 667  | 0.8659          | 0.7290   |
| 0.2871        | 90.0    | 675  | 0.8573          | 0.7290   |
| 0.2872        | 90.9333 | 682  | 0.8530          | 0.7290   |
| 0.2587        | 92.0    | 690  | 0.8516          | 0.7383   |
| 0.2587        | 92.9333 | 697  | 0.8502          | 0.7383   |
| 0.3133        | 93.3333 | 700  | 0.8501          | 0.7383   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1

corrugation corrugation flaking_multiple squat corrugation spalling crack crack flaking spalling crack flaking spalling_multiple crack flaking spalling_multiple squat crack flaking_multiple crack flaking_multiple spalling squat crack flaking_multiple spalling_multiple crack flaking_multiple spalling_multiple squat crack flaking_multiple spalling_multiple squat_multiple crack flaking_multiple squat crack spalling crack spalling squat crack spalling_multiple crack spalling_multiple squat crack spalling_multiple squat_multiple crack squat crack_multiple crack_multiple flaking spalling squat crack_multiple flaking spalling squat_multiple crack_multiple flaking spalling_multiple crack_multiple flaking spalling_multiple squat crack_multiple flaking spalling_multiple squat_multiple crack_multiple flaking_multiple crack_multiple flaking_multiple spalling crack_multiple flaking_multiple spalling squat crack_multiple flaking_multiple spalling squat_multiple crack_multiple flaking_multiple spalling_multiple crack_multiple flaking_multiple spalling_multiple squat crack_multiple flaking_multiple spalling_multiple squat_multiple crack_multiple flaking_multiple squat crack_multiple spalling crack_multiple spalling squat crack_multiple spalling squat_multiple crack_multiple spalling_multiple crack_multiple spalling_multiple squat crack_multiple spalling_multiple squat_multiple crack_multiple squat crack_multiple squat_multiple empty flaking flaking putus spalling_multiple flaking spalling flaking spalling squat flaking spalling_multiple flaking spalling_multiple squat flaking squat flaking squat_multiple flaking_multiple flaking_multiple spalling flaking_multiple spalling squat flaking_multiple spalling squat_multiple flaking_multiple spalling_multiple flaking_multiple spalling_multiple squat flaking_multiple spalling_multiple squat_multiple flaking_multiple squat flaking_multiple squat_multiple putus putus spalling_multiple spalling spalling squat spalling squat_multiple spalling_multiple spalling_multiple squat spalling_multiple squat_multiple squat squat_multiple