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

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  1. README.md +10 -55
  2. model.safetensors +1 -1
  3. runs/Mar02_06-42-13_601745c6f59f/events.out.tfevents.1709361746.601745c6f59f.171.0 +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: 0.9227799227799228
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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: 0.3788
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- - Accuracy: 0.9228
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  ## Model description
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@@ -61,67 +61,22 @@ 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: 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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- | 2.2506 | 0.99 | 18 | 1.6190 | 0.5135 |
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- | 1.4467 | 1.97 | 36 | 1.1899 | 0.7066 |
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- | 1.0888 | 2.96 | 54 | 0.8470 | 0.7413 |
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- | 0.8321 | 4.0 | 73 | 0.6433 | 0.7838 |
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- | 0.722 | 4.99 | 91 | 0.5037 | 0.8378 |
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- | 0.6852 | 5.97 | 109 | 0.5354 | 0.8456 |
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- | 0.5344 | 6.96 | 127 | 0.4872 | 0.8456 |
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- | 0.5354 | 8.0 | 146 | 0.3524 | 0.8958 |
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- | 0.4576 | 8.99 | 164 | 0.3925 | 0.8687 |
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- | 0.426 | 9.97 | 182 | 0.3159 | 0.9112 |
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- | 0.4059 | 10.96 | 200 | 0.4182 | 0.8610 |
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- | 0.3733 | 12.0 | 219 | 0.3867 | 0.8803 |
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- | 0.4006 | 12.99 | 237 | 0.3451 | 0.9035 |
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- | 0.3237 | 13.97 | 255 | 0.3464 | 0.9112 |
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- | 0.338 | 14.96 | 273 | 0.3715 | 0.8958 |
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- | 0.3055 | 16.0 | 292 | 0.3270 | 0.9073 |
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- | 0.3029 | 16.99 | 310 | 0.3527 | 0.9112 |
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- | 0.3032 | 17.97 | 328 | 0.3871 | 0.8842 |
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- | 0.2734 | 18.96 | 346 | 0.3783 | 0.9035 |
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- | 0.2641 | 20.0 | 365 | 0.3802 | 0.9073 |
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- | 0.2433 | 20.99 | 383 | 0.3474 | 0.8958 |
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- | 0.2338 | 21.97 | 401 | 0.3848 | 0.9035 |
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- | 0.2078 | 22.96 | 419 | 0.3941 | 0.8958 |
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- | 0.2299 | 24.0 | 438 | 0.3334 | 0.9189 |
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- | 0.2119 | 24.99 | 456 | 0.3391 | 0.9228 |
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- | 0.2125 | 25.97 | 474 | 0.3499 | 0.9228 |
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- | 0.2249 | 26.96 | 492 | 0.3287 | 0.9151 |
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- | 0.2011 | 28.0 | 511 | 0.3669 | 0.9035 |
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- | 0.1524 | 28.99 | 529 | 0.3521 | 0.9035 |
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- | 0.1624 | 29.97 | 547 | 0.3955 | 0.8958 |
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- | 0.1536 | 30.96 | 565 | 0.3757 | 0.9073 |
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- | 0.1526 | 32.0 | 584 | 0.3765 | 0.8996 |
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- | 0.1692 | 32.99 | 602 | 0.3417 | 0.9112 |
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- | 0.1561 | 33.97 | 620 | 0.3430 | 0.9228 |
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- | 0.1314 | 34.96 | 638 | 0.3381 | 0.9266 |
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- | 0.1613 | 36.0 | 657 | 0.3425 | 0.9151 |
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- | 0.1268 | 36.99 | 675 | 0.3434 | 0.9228 |
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- | 0.1263 | 37.97 | 693 | 0.3665 | 0.9035 |
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- | 0.096 | 38.96 | 711 | 0.3864 | 0.9189 |
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- | 0.1375 | 40.0 | 730 | 0.3802 | 0.9266 |
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- | 0.1279 | 40.99 | 748 | 0.3584 | 0.9266 |
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- | 0.1153 | 41.97 | 766 | 0.3745 | 0.9189 |
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- | 0.1343 | 42.96 | 784 | 0.3877 | 0.9189 |
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- | 0.1272 | 44.0 | 803 | 0.3729 | 0.9228 |
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- | 0.1238 | 44.99 | 821 | 0.3904 | 0.9228 |
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- | 0.1184 | 45.97 | 839 | 0.3819 | 0.9151 |
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- | 0.0927 | 46.96 | 857 | 0.3756 | 0.9228 |
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- | 0.1221 | 48.0 | 876 | 0.3798 | 0.9228 |
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- | 0.1116 | 48.99 | 894 | 0.3790 | 0.9228 |
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- | 0.0939 | 49.32 | 900 | 0.3788 | 0.9228 |
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  ### Framework versions
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  - Transformers 4.38.1
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  - Pytorch 2.1.0+cu121
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- - Datasets 2.17.1
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  - Tokenizers 0.15.2
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.8108108108108109
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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
 
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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: 0.6244
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+ - Accuracy: 0.8108
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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: 5
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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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+ | 1.8838 | 0.99 | 18 | 1.2972 | 0.6371 |
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+ | 1.0537 | 1.97 | 36 | 0.8605 | 0.7336 |
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+ | 0.7996 | 2.96 | 54 | 0.7460 | 0.7645 |
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+ | 0.7009 | 4.0 | 73 | 0.6442 | 0.7992 |
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+ | 0.6115 | 4.93 | 90 | 0.6244 | 0.8108 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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  - Transformers 4.38.1
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  - Pytorch 2.1.0+cu121
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+ - Datasets 2.18.0
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  - Tokenizers 0.15.2
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