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# 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: 0.3209
- Accuracy: 0.8902

## 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        | 1.0   | 8    | 2.7448          | 0.0314   |
| 2.7716        | 2.0   | 16   | 2.5834          | 0.1765   |
| 2.5974        | 3.0   | 24   | 2.3608          | 0.3020   |
| 2.3426        | 4.0   | 32   | 2.1157          | 0.3333   |
| 1.9747        | 5.0   | 40   | 1.7539          | 0.4627   |
| 1.9747        | 6.0   | 48   | 1.3641          | 0.6078   |
| 1.5182        | 7.0   | 56   | 1.0755          | 0.6471   |
| 1.198         | 8.0   | 64   | 0.8743          | 0.7216   |
| 1.0206        | 9.0   | 72   | 0.7666          | 0.7294   |
| 0.8731        | 10.0  | 80   | 0.7035          | 0.7490   |
| 0.8731        | 11.0  | 88   | 0.6122          | 0.7608   |
| 0.7938        | 12.0  | 96   | 0.6508          | 0.7490   |
| 0.7286        | 13.0  | 104  | 0.5081          | 0.7961   |
| 0.659         | 14.0  | 112  | 0.5536          | 0.7961   |
| 0.6232        | 15.0  | 120  | 0.5079          | 0.8      |
| 0.6232        | 16.0  | 128  | 0.4483          | 0.8314   |
| 0.6028        | 17.0  | 136  | 0.4096          | 0.8157   |
| 0.5333        | 18.0  | 144  | 0.3710          | 0.8510   |
| 0.5053        | 19.0  | 152  | 0.4810          | 0.8039   |
| 0.4717        | 20.0  | 160  | 0.4121          | 0.8235   |
| 0.4717        | 21.0  | 168  | 0.4021          | 0.8392   |
| 0.4728        | 22.0  | 176  | 0.3780          | 0.8588   |
| 0.4347        | 23.0  | 184  | 0.3374          | 0.8745   |
| 0.4545        | 24.0  | 192  | 0.4056          | 0.8431   |
| 0.3954        | 25.0  | 200  | 0.4088          | 0.8745   |
| 0.3954        | 26.0  | 208  | 0.4169          | 0.8392   |
| 0.4145        | 27.0  | 216  | 0.3262          | 0.8706   |
| 0.3895        | 28.0  | 224  | 0.4235          | 0.8706   |
| 0.4185        | 29.0  | 232  | 0.3482          | 0.8706   |
| 0.3686        | 30.0  | 240  | 0.3088          | 0.8824   |
| 0.3686        | 31.0  | 248  | 0.3230          | 0.8902   |
| 0.3617        | 32.0  | 256  | 0.3473          | 0.8824   |
| 0.3136        | 33.0  | 264  | 0.3793          | 0.8627   |
| 0.3482        | 34.0  | 272  | 0.3477          | 0.8588   |
| 0.3519        | 35.0  | 280  | 0.3692          | 0.8667   |
| 0.3519        | 36.0  | 288  | 0.3611          | 0.8627   |
| 0.3311        | 37.0  | 296  | 0.3233          | 0.8745   |
| 0.3222        | 38.0  | 304  | 0.3416          | 0.8627   |
| 0.3013        | 39.0  | 312  | 0.3198          | 0.8824   |
| 0.2871        | 40.0  | 320  | 0.3308          | 0.8667   |
| 0.2871        | 41.0  | 328  | 0.3246          | 0.8667   |
| 0.3154        | 42.0  | 336  | 0.3943          | 0.8667   |
| 0.2735        | 43.0  | 344  | 0.3186          | 0.8784   |
| 0.2911        | 44.0  | 352  | 0.3132          | 0.8824   |
| 0.266         | 45.0  | 360  | 0.3204          | 0.8980   |
| 0.266         | 46.0  | 368  | 0.3097          | 0.8784   |
| 0.2686        | 47.0  | 376  | 0.3075          | 0.8902   |
| 0.2818        | 48.0  | 384  | 0.3192          | 0.8902   |
| 0.2492        | 49.0  | 392  | 0.3434          | 0.8745   |
| 0.276         | 50.0  | 400  | 0.3237          | 0.8824   |
| 0.276         | 51.0  | 408  | 0.3450          | 0.8745   |
| 0.245         | 52.0  | 416  | 0.3284          | 0.8706   |
| 0.2292        | 53.0  | 424  | 0.3263          | 0.8902   |
| 0.2252        | 54.0  | 432  | 0.3216          | 0.8745   |
| 0.2483        | 55.0  | 440  | 0.3359          | 0.8863   |
| 0.2483        | 56.0  | 448  | 0.3314          | 0.8902   |
| 0.2549        | 57.0  | 456  | 0.3932          | 0.8745   |
| 0.2247        | 58.0  | 464  | 0.3189          | 0.8745   |
| 0.2344        | 59.0  | 472  | 0.3251          | 0.8745   |
| 0.2315        | 60.0  | 480  | 0.3289          | 0.8824   |
| 0.2315        | 61.0  | 488  | 0.3058          | 0.8745   |
| 0.2109        | 62.0  | 496  | 0.2999          | 0.8863   |
| 0.2325        | 63.0  | 504  | 0.3078          | 0.8980   |
| 0.2126        | 64.0  | 512  | 0.3531          | 0.8784   |
| 0.1975        | 65.0  | 520  | 0.3394          | 0.8902   |
| 0.1975        | 66.0  | 528  | 0.3113          | 0.8902   |
| 0.1998        | 67.0  | 536  | 0.3365          | 0.8941   |
| 0.2208        | 68.0  | 544  | 0.2854          | 0.9020   |
| 0.2126        | 69.0  | 552  | 0.3170          | 0.8941   |
| 0.2352        | 70.0  | 560  | 0.3155          | 0.8824   |
| 0.2352        | 71.0  | 568  | 0.3327          | 0.8824   |
| 0.1724        | 72.0  | 576  | 0.3503          | 0.8902   |
| 0.2038        | 73.0  | 584  | 0.3309          | 0.8824   |
| 0.1919        | 74.0  | 592  | 0.3299          | 0.8902   |
| 0.2199        | 75.0  | 600  | 0.3347          | 0.8863   |
| 0.2199        | 76.0  | 608  | 0.3471          | 0.8824   |
| 0.2075        | 77.0  | 616  | 0.3437          | 0.8863   |
| 0.2206        | 78.0  | 624  | 0.3161          | 0.8824   |
| 0.1655        | 79.0  | 632  | 0.3227          | 0.8784   |
| 0.1765        | 80.0  | 640  | 0.3302          | 0.8784   |
| 0.1765        | 81.0  | 648  | 0.3153          | 0.8745   |
| 0.1832        | 82.0  | 656  | 0.3010          | 0.8745   |
| 0.185         | 83.0  | 664  | 0.3266          | 0.8941   |
| 0.1627        | 84.0  | 672  | 0.3192          | 0.8941   |
| 0.176         | 85.0  | 680  | 0.3125          | 0.8863   |
| 0.176         | 86.0  | 688  | 0.3241          | 0.8745   |
| 0.1723        | 87.0  | 696  | 0.3124          | 0.8784   |
| 0.1477        | 88.0  | 704  | 0.3109          | 0.8745   |
| 0.1703        | 89.0  | 712  | 0.3196          | 0.8824   |
| 0.1919        | 90.0  | 720  | 0.3186          | 0.8980   |
| 0.1919        | 91.0  | 728  | 0.3178          | 0.8902   |
| 0.1465        | 92.0  | 736  | 0.3241          | 0.8824   |
| 0.155         | 93.0  | 744  | 0.3281          | 0.8784   |
| 0.1829        | 94.0  | 752  | 0.3263          | 0.8824   |
| 0.167         | 95.0  | 760  | 0.3282          | 0.8824   |
| 0.167         | 96.0  | 768  | 0.3290          | 0.8824   |
| 0.166         | 97.0  | 776  | 0.3253          | 0.8902   |
| 0.1756        | 98.0  | 784  | 0.3231          | 0.8863   |
| 0.157         | 99.0  | 792  | 0.3215          | 0.8902   |
| 0.1492        | 100.0 | 800  | 0.3209          | 0.8902   |


### Framework versions

- Transformers 4.33.3
- Pytorch 1.11.0+cu113
- Datasets 2.14.5
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/swin-tiny-patch4-window7-224", "model-index": [{"name": "swin-tiny-patch4-window7-224-finetuned-eurosat", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8901960784313725, "name": "Accuracy"}]}]}]}
transformers pytorch swin image-classification generated_from_trainer dataset:imagefolder base_model:microsoft/swin-tiny-patch4-window7-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us