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  1. README.md +24 -7
  2. model.safetensors +1 -1
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: 0.6930860033726813
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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: 0.6096
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- - Accuracy: 0.6931
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  ## Model description
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@@ -61,15 +61,32 @@ 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: 3
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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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- | 0.6277 | 0.98 | 41 | 0.6070 | 0.6425 |
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- | 0.557 | 1.99 | 83 | 0.6569 | 0.6644 |
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- | 0.5445 | 2.95 | 123 | 0.6096 | 0.6931 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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: 0.8111298482293423
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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: 0.4435
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+ - Accuracy: 0.8111
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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: 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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+ | 0.5077 | 0.98 | 41 | 0.6378 | 0.6796 |
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+ | 0.5111 | 1.99 | 83 | 0.7097 | 0.6577 |
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+ | 0.5395 | 2.99 | 125 | 0.5374 | 0.7470 |
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+ | 0.5498 | 4.0 | 167 | 0.5524 | 0.7420 |
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+ | 0.4754 | 4.98 | 208 | 0.5324 | 0.7639 |
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+ | 0.4662 | 5.99 | 250 | 0.4962 | 0.7639 |
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+ | 0.4677 | 6.99 | 292 | 0.5070 | 0.7774 |
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+ | 0.4525 | 8.0 | 334 | 0.5144 | 0.7673 |
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+ | 0.4635 | 8.98 | 375 | 0.4978 | 0.7757 |
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+ | 0.4309 | 9.99 | 417 | 0.5388 | 0.7774 |
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+ | 0.4292 | 10.99 | 459 | 0.4937 | 0.7825 |
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+ | 0.4182 | 12.0 | 501 | 0.5234 | 0.7808 |
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+ | 0.4242 | 12.98 | 542 | 0.4539 | 0.7960 |
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+ | 0.4053 | 13.99 | 584 | 0.5089 | 0.7858 |
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+ | 0.4135 | 14.99 | 626 | 0.4655 | 0.8044 |
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+ | 0.3888 | 16.0 | 668 | 0.4398 | 0.8212 |
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+ | 0.3701 | 16.98 | 709 | 0.4258 | 0.8145 |
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+ | 0.3641 | 17.99 | 751 | 0.4339 | 0.8196 |
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+ | 0.3547 | 18.99 | 793 | 0.4556 | 0.7993 |
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+ | 0.3623 | 19.64 | 820 | 0.4435 | 0.8111 |
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  ### Framework versions
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