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

Image Classification
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swin
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  1. README.md +21 -21
  2. model.safetensors +1 -1
  3. runs/Oct26_08-08-51_c3ee2852f8cc/events.out.tfevents.1729930151.c3ee2852f8cc.307.0 +2 -2
README.md CHANGED
@@ -26,16 +26,16 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.9714285714285714
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  - name: Precision
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  type: precision
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- value: 0.9696825396825397
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  - name: Recall
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  type: recall
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- value: 0.9714285714285714
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  - name: F1
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  type: f1
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- value: 0.9695078031212484
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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
@@ -45,11 +45,11 @@ 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: 0.0814
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- - Accuracy: 0.9714
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- - Precision: 0.9697
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- - Recall: 0.9714
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- - F1: 0.9695
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  ## Model description
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@@ -81,18 +81,18 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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- |:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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- | 0.2262 | 0.9888 | 22 | 0.2061 | 0.9365 | 0.8770 | 0.9365 | 0.9058 |
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- | 0.1666 | 1.9775 | 44 | 0.1274 | 0.9333 | 0.8769 | 0.9333 | 0.9042 |
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- | 0.1168 | 2.9663 | 66 | 0.1054 | 0.9524 | 0.9461 | 0.9524 | 0.9438 |
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- | 0.0984 | 4.0 | 89 | 0.0824 | 0.9619 | 0.9591 | 0.9619 | 0.9599 |
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- | 0.1028 | 4.9888 | 111 | 0.0814 | 0.9714 | 0.9697 | 0.9714 | 0.9695 |
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- | 0.1082 | 5.9775 | 133 | 0.0835 | 0.9492 | 0.9518 | 0.9492 | 0.9329 |
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- | 0.0962 | 6.9663 | 155 | 0.0872 | 0.9587 | 0.9578 | 0.9587 | 0.9582 |
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- | 0.0799 | 8.0 | 178 | 0.0803 | 0.9587 | 0.9543 | 0.9587 | 0.9546 |
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- | 0.0954 | 8.9888 | 200 | 0.0685 | 0.9619 | 0.9584 | 0.9619 | 0.9587 |
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- | 0.0771 | 9.8876 | 220 | 0.0711 | 0.9619 | 0.9584 | 0.9619 | 0.9587 |
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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: 0.8327974276527331
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  - name: Precision
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  type: precision
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+ value: 0.860997154156041
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  - name: Recall
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  type: recall
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+ value: 0.8327974276527331
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  - name: F1
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  type: f1
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+ value: 0.8137864007121186
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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: 0.3277
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+ - Accuracy: 0.8328
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+ - Precision: 0.8610
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+ - Recall: 0.8328
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+ - F1: 0.8138
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  ## Model description
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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+ | 0.5603 | 1.0 | 22 | 0.5610 | 0.6817 | 0.7833 | 0.6817 | 0.5559 |
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+ | 0.3934 | 2.0 | 44 | 0.3976 | 0.7910 | 0.7879 | 0.7910 | 0.7768 |
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+ | 0.2992 | 3.0 | 66 | 0.3036 | 0.8071 | 0.8044 | 0.8071 | 0.7965 |
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+ | 0.2746 | 4.0 | 88 | 0.3538 | 0.7878 | 0.7812 | 0.7878 | 0.7799 |
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+ | 0.2573 | 5.0 | 110 | 0.2242 | 0.8521 | 0.8561 | 0.8521 | 0.8535 |
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+ | 0.2724 | 6.0 | 132 | 0.3801 | 0.7749 | 0.8145 | 0.7749 | 0.7347 |
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+ | 0.2344 | 7.0 | 154 | 0.3327 | 0.8232 | 0.8544 | 0.8232 | 0.8011 |
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+ | 0.2225 | 8.0 | 176 | 0.3736 | 0.8392 | 0.8655 | 0.8392 | 0.8221 |
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+ | 0.225 | 9.0 | 198 | 0.3479 | 0.8328 | 0.8610 | 0.8328 | 0.8138 |
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+ | 0.2308 | 10.0 | 220 | 0.3277 | 0.8328 | 0.8610 | 0.8328 | 0.8138 |
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  ### Framework versions
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