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morganchen1007
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swin-tiny-patch4-window7-224-finetuned-eurosat

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update model card README.md

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@@ -21,7 +21,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.948729184925504
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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
@@ -31,8 +31,8 @@ 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: 0.1460
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- - Accuracy: 0.9487
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  ## Model description
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@@ -60,15 +60,24 @@ 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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- | 0.2751 | 1.0 | 160 | 0.1808 | 0.9233 |
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- | 0.2127 | 2.0 | 320 | 0.1461 | 0.9443 |
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- | 0.1935 | 3.0 | 480 | 0.1460 | 0.9487 |
 
 
 
 
 
 
 
 
 
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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: 0.9341978866474544
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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: 0.1507
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+ - Accuracy: 0.9342
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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: 12
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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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+ | 0.2891 | 1.0 | 146 | 0.2322 | 0.9068 |
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+ | 0.2609 | 2.0 | 292 | 0.1710 | 0.9227 |
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+ | 0.2417 | 3.0 | 438 | 0.1830 | 0.9251 |
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+ | 0.2406 | 4.0 | 584 | 0.1809 | 0.9198 |
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+ | 0.2113 | 5.0 | 730 | 0.1631 | 0.9289 |
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+ | 0.1812 | 6.0 | 876 | 0.1561 | 0.9308 |
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+ | 0.2082 | 7.0 | 1022 | 0.1507 | 0.9342 |
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+ | 0.1922 | 8.0 | 1168 | 0.1611 | 0.9294 |
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+ | 0.1715 | 9.0 | 1314 | 0.1536 | 0.9308 |
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+ | 0.1675 | 10.0 | 1460 | 0.1609 | 0.9289 |
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+ | 0.194 | 11.0 | 1606 | 0.1499 | 0.9337 |
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+ | 0.1706 | 12.0 | 1752 | 0.1514 | 0.9323 |
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  ### Framework versions