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

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  1. README.md +54 -24
  2. model.safetensors +1 -1
  3. runs/Feb24_14-42-30_88354ccce9ce/events.out.tfevents.1708785760.88354ccce9ce.155.0 +2 -2
README.md CHANGED
@@ -22,7 +22,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.8764478764478765
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,8 +32,8 @@ should probably proofread and complete it, then remove this comment. -->
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  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.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.4650
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- - Accuracy: 0.8764
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  ## Model description
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@@ -61,32 +61,62 @@ The following hyperparameters were used during training:
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.1
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- - num_epochs: 20
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 2.3831 | 0.99 | 18 | 1.3831 | 0.6757 |
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- | 1.1489 | 1.97 | 36 | 0.9853 | 0.7027 |
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- | 0.8413 | 2.96 | 54 | 0.7500 | 0.7876 |
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- | 0.7498 | 4.0 | 73 | 0.6910 | 0.8031 |
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- | 0.6646 | 4.99 | 91 | 0.6345 | 0.8263 |
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- | 0.5488 | 5.97 | 109 | 0.5561 | 0.8224 |
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- | 0.4922 | 6.96 | 127 | 0.5110 | 0.8494 |
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- | 0.4622 | 8.0 | 146 | 0.5236 | 0.8301 |
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- | 0.4585 | 8.99 | 164 | 0.5004 | 0.8649 |
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- | 0.4168 | 9.97 | 182 | 0.5065 | 0.8456 |
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- | 0.3577 | 10.96 | 200 | 0.4910 | 0.8494 |
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- | 0.3575 | 12.0 | 219 | 0.4826 | 0.8571 |
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- | 0.3326 | 12.99 | 237 | 0.5168 | 0.8649 |
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- | 0.3257 | 13.97 | 255 | 0.4473 | 0.8803 |
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- | 0.3034 | 14.96 | 273 | 0.4603 | 0.8803 |
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- | 0.3063 | 16.0 | 292 | 0.5032 | 0.8533 |
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- | 0.2824 | 16.99 | 310 | 0.4690 | 0.8649 |
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- | 0.2984 | 17.97 | 328 | 0.4601 | 0.8764 |
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- | 0.2818 | 18.96 | 346 | 0.4661 | 0.8764 |
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- | 0.2547 | 19.73 | 360 | 0.4650 | 0.8764 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.8532818532818532
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  ---
27
 
28
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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  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.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.6144
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+ - Accuracy: 0.8533
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  ## Model description
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 50
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 2.1952 | 0.99 | 18 | 1.5914 | 0.5985 |
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+ | 1.3705 | 1.97 | 36 | 1.2164 | 0.6873 |
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+ | 1.026 | 2.96 | 54 | 0.9974 | 0.7375 |
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+ | 0.829 | 4.0 | 73 | 0.7667 | 0.7722 |
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+ | 0.6513 | 4.99 | 91 | 0.6674 | 0.8224 |
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+ | 0.5516 | 5.97 | 109 | 0.5810 | 0.8378 |
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+ | 0.4978 | 6.96 | 127 | 0.5498 | 0.8263 |
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+ | 0.4568 | 8.0 | 146 | 0.5999 | 0.8185 |
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+ | 0.4047 | 8.99 | 164 | 0.5211 | 0.8494 |
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+ | 0.3696 | 9.97 | 182 | 0.5201 | 0.8571 |
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+ | 0.3479 | 10.96 | 200 | 0.5310 | 0.8263 |
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+ | 0.329 | 12.0 | 219 | 0.5439 | 0.8494 |
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+ | 0.3376 | 12.99 | 237 | 0.5050 | 0.8494 |
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+ | 0.2804 | 13.97 | 255 | 0.5709 | 0.8263 |
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+ | 0.2941 | 14.96 | 273 | 0.6376 | 0.8147 |
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+ | 0.3026 | 16.0 | 292 | 0.5447 | 0.8494 |
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+ | 0.2578 | 16.99 | 310 | 0.5056 | 0.8803 |
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+ | 0.219 | 17.97 | 328 | 0.5620 | 0.8610 |
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+ | 0.2403 | 18.96 | 346 | 0.5582 | 0.8456 |
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+ | 0.2258 | 20.0 | 365 | 0.5458 | 0.8494 |
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+ | 0.2265 | 20.99 | 383 | 0.5411 | 0.8533 |
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+ | 0.1893 | 21.97 | 401 | 0.5477 | 0.8494 |
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+ | 0.1896 | 22.96 | 419 | 0.5125 | 0.8494 |
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+ | 0.1976 | 24.0 | 438 | 0.5672 | 0.8340 |
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+ | 0.1725 | 24.99 | 456 | 0.5581 | 0.8456 |
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+ | 0.168 | 25.97 | 474 | 0.5965 | 0.8456 |
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+ | 0.1821 | 26.96 | 492 | 0.5567 | 0.8610 |
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+ | 0.1805 | 28.0 | 511 | 0.5998 | 0.8533 |
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+ | 0.1616 | 28.99 | 529 | 0.5451 | 0.8533 |
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+ | 0.1467 | 29.97 | 547 | 0.5574 | 0.8494 |
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+ | 0.1439 | 30.96 | 565 | 0.5707 | 0.8571 |
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+ | 0.13 | 32.0 | 584 | 0.6019 | 0.8378 |
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+ | 0.1353 | 32.99 | 602 | 0.5952 | 0.8610 |
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+ | 0.1329 | 33.97 | 620 | 0.6262 | 0.8378 |
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+ | 0.1258 | 34.96 | 638 | 0.6314 | 0.8456 |
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+ | 0.1408 | 36.0 | 657 | 0.5761 | 0.8494 |
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+ | 0.1197 | 36.99 | 675 | 0.5703 | 0.8610 |
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+ | 0.1208 | 37.97 | 693 | 0.6247 | 0.8456 |
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+ | 0.1197 | 38.96 | 711 | 0.6026 | 0.8533 |
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+ | 0.1271 | 40.0 | 730 | 0.5953 | 0.8533 |
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+ | 0.1053 | 40.99 | 748 | 0.6070 | 0.8533 |
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+ | 0.0846 | 41.97 | 766 | 0.6094 | 0.8610 |
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+ | 0.1206 | 42.96 | 784 | 0.5912 | 0.8494 |
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+ | 0.1225 | 44.0 | 803 | 0.6074 | 0.8494 |
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+ | 0.1184 | 44.99 | 821 | 0.5943 | 0.8494 |
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+ | 0.1027 | 45.97 | 839 | 0.6084 | 0.8494 |
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+ | 0.1113 | 46.96 | 857 | 0.6034 | 0.8533 |
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+ | 0.0945 | 48.0 | 876 | 0.6106 | 0.8494 |
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+ | 0.1159 | 48.99 | 894 | 0.6143 | 0.8533 |
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+ | 0.0963 | 49.32 | 900 | 0.6144 | 0.8533 |
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  ### Framework versions
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