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

Image Classification
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  1. README.md +14 -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: 0.625
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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: 0.8869
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- - Accuracy: 0.625
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  ## Model description
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@@ -61,15 +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: 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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- | 0.6404 | 0.9825 | 14 | 0.7282 | 0.62 |
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- | 0.4758 | 1.9649 | 28 | 0.9752 | 0.57 |
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- | 0.3149 | 2.9474 | 42 | 0.8869 | 0.625 |
 
 
 
 
 
 
 
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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: 0.805
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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: 0.6650
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+ - Accuracy: 0.805
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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: 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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+ | 0.2566 | 0.9825 | 14 | 0.7227 | 0.74 |
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+ | 0.2729 | 1.9649 | 28 | 0.3916 | 0.815 |
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+ | 0.1553 | 2.9474 | 42 | 0.8409 | 0.75 |
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+ | 0.1371 | 4.0 | 57 | 0.3706 | 0.885 |
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+ | 0.1588 | 4.9825 | 71 | 0.8758 | 0.765 |
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+ | 0.1093 | 5.9649 | 85 | 0.6063 | 0.82 |
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+ | 0.102 | 6.9474 | 99 | 0.5308 | 0.84 |
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+ | 0.0713 | 8.0 | 114 | 0.3536 | 0.88 |
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+ | 0.1045 | 8.9825 | 128 | 0.3858 | 0.875 |
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+ | 0.0661 | 9.8246 | 140 | 0.6650 | 0.805 |
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  ### Framework versions
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