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swinv2-base-patch4-window8-256

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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: 0.813953488372093
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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/swinv2-base-patch4-window8-256](https://huggingface.co/microsoft/swinv2-base-patch4-window8-256) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5212
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- - Accuracy: 0.8140
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  ## Model description
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@@ -52,31 +52,51 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 5e-06
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- - train_batch_size: 4
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- - eval_batch_size: 4
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  - seed: 42
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  - gradient_accumulation_steps: 4
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- - total_train_batch_size: 16
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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: 10
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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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- | No log | 0.96 | 6 | 0.6027 | 0.7907 |
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- | 0.6492 | 1.92 | 12 | 0.5212 | 0.8140 |
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- | 0.6492 | 2.88 | 18 | 0.4939 | 0.8140 |
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- | 0.5773 | 4.0 | 25 | 0.4829 | 0.8140 |
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- | 0.6313 | 4.96 | 31 | 0.4833 | 0.8140 |
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- | 0.6313 | 5.92 | 37 | 0.4873 | 0.8140 |
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- | 0.5665 | 6.88 | 43 | 0.4876 | 0.8140 |
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- | 0.5615 | 8.0 | 50 | 0.4861 | 0.8140 |
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- | 0.5615 | 8.96 | 56 | 0.4873 | 0.8140 |
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- | 0.5712 | 9.6 | 60 | 0.4871 | 0.8140 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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: 0.7241379310344828
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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/swinv2-base-patch4-window8-256](https://huggingface.co/microsoft/swinv2-base-patch4-window8-256) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5995
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+ - Accuracy: 0.7241
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 0.0001
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+ - train_batch_size: 1
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+ - eval_batch_size: 1
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  - seed: 42
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  - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 4
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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: 30
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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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+ | 0.5428 | 0.9912 | 28 | 0.6211 | 0.7241 |
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+ | 0.6494 | 1.9823 | 56 | 0.6130 | 0.7241 |
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+ | 0.5752 | 2.9735 | 84 | 0.6846 | 0.7241 |
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+ | 0.7165 | 4.0 | 113 | 0.9642 | 0.7241 |
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+ | 0.5699 | 4.9912 | 141 | 0.6072 | 0.7241 |
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+ | 0.5517 | 5.9823 | 169 | 0.6231 | 0.7241 |
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+ | 0.5268 | 6.9735 | 197 | 0.6098 | 0.7241 |
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+ | 0.672 | 8.0 | 226 | 0.5891 | 0.7241 |
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+ | 0.5448 | 8.9912 | 254 | 0.6023 | 0.7241 |
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+ | 0.555 | 9.9823 | 282 | 0.5917 | 0.7241 |
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+ | 0.5818 | 10.9735 | 310 | 0.5940 | 0.7241 |
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+ | 0.6556 | 12.0 | 339 | 0.5966 | 0.7241 |
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+ | 0.716 | 12.9912 | 367 | 0.5904 | 0.7241 |
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+ | 0.6104 | 13.9823 | 395 | 0.5938 | 0.7241 |
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+ | 0.5046 | 14.9735 | 423 | 0.5921 | 0.7241 |
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+ | 0.5871 | 16.0 | 452 | 0.6027 | 0.7241 |
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+ | 0.5222 | 16.9912 | 480 | 0.5921 | 0.7241 |
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+ | 0.5511 | 17.9823 | 508 | 0.5948 | 0.7241 |
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+ | 0.6394 | 18.9735 | 536 | 0.5969 | 0.7241 |
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+ | 0.566 | 20.0 | 565 | 0.6005 | 0.7241 |
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+ | 0.6032 | 20.9912 | 593 | 0.5968 | 0.7241 |
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+ | 0.4824 | 21.9823 | 621 | 0.5934 | 0.7241 |
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+ | 0.4975 | 22.9735 | 649 | 0.5979 | 0.7241 |
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+ | 0.4976 | 24.0 | 678 | 0.6034 | 0.7241 |
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+ | 0.5355 | 24.9912 | 706 | 0.6033 | 0.7241 |
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+ | 0.4323 | 25.9823 | 734 | 0.6015 | 0.7241 |
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+ | 0.5579 | 26.9735 | 762 | 0.6043 | 0.7241 |
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+ | 0.5639 | 28.0 | 791 | 0.6023 | 0.7241 |
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+ | 0.5595 | 28.9912 | 819 | 0.5996 | 0.7241 |
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+ | 0.4372 | 29.7345 | 840 | 0.5995 | 0.7241 |
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  ### Framework versions
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