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

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metadata
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: swin-tiny-patch4-window7-224-finetuned-eurosat
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.7663551401869159

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

This model is a fine-tuned version of 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