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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: 0.4382
- Accuracy: 0.8727

## 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: 50

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log        | 0.9032  | 7    | 2.3727          | 0.2      |
| 2.3966        | 1.9355  | 15   | 2.2910          | 0.3182   |
| 2.3131        | 2.9677  | 23   | 2.1218          | 0.4091   |
| 2.072         | 4.0     | 31   | 1.8349          | 0.4545   |
| 2.072         | 4.9032  | 38   | 1.4635          | 0.5364   |
| 1.5528        | 5.9355  | 46   | 1.1036          | 0.6636   |
| 1.0472        | 6.9677  | 54   | 0.9273          | 0.7273   |
| 0.7989        | 8.0     | 62   | 0.8008          | 0.7909   |
| 0.7989        | 8.9032  | 69   | 0.7359          | 0.7818   |
| 0.604         | 9.9355  | 77   | 0.7283          | 0.7909   |
| 0.5228        | 10.9677 | 85   | 0.5897          | 0.8364   |
| 0.4734        | 12.0    | 93   | 0.6503          | 0.8182   |
| 0.3987        | 12.9032 | 100  | 0.5785          | 0.8273   |
| 0.3987        | 13.9355 | 108  | 0.6091          | 0.8182   |
| 0.3742        | 14.9677 | 116  | 0.5278          | 0.8455   |
| 0.3588        | 16.0    | 124  | 0.5279          | 0.8545   |
| 0.3536        | 16.9032 | 131  | 0.5189          | 0.8364   |
| 0.3536        | 17.9355 | 139  | 0.5036          | 0.8545   |
| 0.331         | 18.9677 | 147  | 0.5327          | 0.8364   |
| 0.2836        | 20.0    | 155  | 0.4717          | 0.8636   |
| 0.2785        | 20.9032 | 162  | 0.4598          | 0.8545   |
| 0.2439        | 21.9355 | 170  | 0.4783          | 0.8545   |
| 0.2439        | 22.9677 | 178  | 0.4948          | 0.8545   |
| 0.2779        | 24.0    | 186  | 0.4884          | 0.8455   |
| 0.2167        | 24.9032 | 193  | 0.5084          | 0.8545   |
| 0.2164        | 25.9355 | 201  | 0.4715          | 0.8545   |
| 0.2164        | 26.9677 | 209  | 0.5503          | 0.8273   |
| 0.2342        | 28.0    | 217  | 0.4980          | 0.8273   |
| 0.216         | 28.9032 | 224  | 0.4241          | 0.8545   |
| 0.1986        | 29.9355 | 232  | 0.4466          | 0.8545   |
| 0.1919        | 30.9677 | 240  | 0.4558          | 0.8636   |
| 0.1919        | 32.0    | 248  | 0.4390          | 0.8636   |
| 0.1958        | 32.9032 | 255  | 0.4379          | 0.8545   |
| 0.1693        | 33.9355 | 263  | 0.4424          | 0.8455   |
| 0.2158        | 34.9677 | 271  | 0.4524          | 0.8364   |
| 0.2158        | 36.0    | 279  | 0.4388          | 0.8545   |
| 0.1578        | 36.9032 | 286  | 0.4327          | 0.8545   |
| 0.1866        | 37.9355 | 294  | 0.4528          | 0.8455   |
| 0.1664        | 38.9677 | 302  | 0.4533          | 0.8455   |
| 0.1757        | 40.0    | 310  | 0.4492          | 0.8545   |
| 0.1757        | 40.9032 | 317  | 0.4418          | 0.8636   |
| 0.1542        | 41.9355 | 325  | 0.4412          | 0.8636   |
| 0.144         | 42.9677 | 333  | 0.4438          | 0.8545   |
| 0.1647        | 44.0    | 341  | 0.4411          | 0.8636   |
| 0.1647        | 44.9032 | 348  | 0.4383          | 0.8636   |
| 0.1418        | 45.1613 | 350  | 0.4382          | 0.8727   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1

ak-47 awp famas galil-ar glock m4a1 m4a4 p-90 sg-553 ump usp