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HorcruxNo13
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swin-tiny-patch4-window7-224

Image Classification
Transformers
PyTorch
swin
Generated from Trainer
Eval Results
Inference Endpoints
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  1. README.md +23 -23
  2. pytorch_model.bin +1 -1
README.md CHANGED
@@ -24,13 +24,13 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.7133333333333334
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  - name: Precision
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  type: precision
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- value: 0.6732516172965611
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  - name: Recall
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  type: recall
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- value: 0.7133333333333334
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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
@@ -40,11 +40,11 @@ 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: 0.5797
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- - Accuracy: 0.7133
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- - Precision: 0.6733
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- - Recall: 0.7133
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- - F1 Score: 0.6650
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  ## Model description
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@@ -78,21 +78,21 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
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- | No log | 1.0 | 4 | 0.5965 | 0.725 | 0.5256 | 0.725 | 0.6094 |
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- | No log | 2.0 | 8 | 0.6045 | 0.7125 | 0.5795 | 0.7125 | 0.6104 |
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- | No log | 3.0 | 12 | 0.5910 | 0.725 | 0.6645 | 0.725 | 0.6169 |
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- | 0.6165 | 4.0 | 16 | 0.5865 | 0.7333 | 0.7162 | 0.7333 | 0.6418 |
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- | 0.6165 | 5.0 | 20 | 0.5789 | 0.7292 | 0.6846 | 0.7292 | 0.6562 |
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- | 0.6165 | 6.0 | 24 | 0.5649 | 0.725 | 0.6702 | 0.725 | 0.6427 |
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- | 0.6165 | 7.0 | 28 | 0.5660 | 0.7375 | 0.7090 | 0.7375 | 0.6668 |
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- | 0.5966 | 8.0 | 32 | 0.5972 | 0.7375 | 0.7108 | 0.7375 | 0.7132 |
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- | 0.5966 | 9.0 | 36 | 0.5666 | 0.7417 | 0.7134 | 0.7417 | 0.6835 |
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- | 0.5966 | 10.0 | 40 | 0.5781 | 0.7417 | 0.7124 | 0.7417 | 0.7084 |
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- | 0.5966 | 11.0 | 44 | 0.6009 | 0.7083 | 0.6900 | 0.7083 | 0.6967 |
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- | 0.5921 | 12.0 | 48 | 0.5678 | 0.75 | 0.7244 | 0.75 | 0.7118 |
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- | 0.5921 | 13.0 | 52 | 0.5581 | 0.7583 | 0.7429 | 0.7583 | 0.7115 |
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- | 0.5921 | 14.0 | 56 | 0.5587 | 0.7542 | 0.7340 | 0.7542 | 0.7083 |
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- | 0.5847 | 15.0 | 60 | 0.5589 | 0.7542 | 0.7340 | 0.7542 | 0.7083 |
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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: 0.8933333333333333
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  - name: Precision
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  type: precision
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+ value: 0.8772576832151301
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  - name: Recall
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  type: recall
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+ value: 0.8933333333333333
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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: 0.2912
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+ - Accuracy: 0.8933
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+ - Precision: 0.8773
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+ - Recall: 0.8933
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+ - F1 Score: 0.8762
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
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+ | No log | 1.0 | 4 | 0.4588 | 0.8708 | 0.7584 | 0.8708 | 0.8107 |
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+ | No log | 2.0 | 8 | 0.3854 | 0.8708 | 0.7584 | 0.8708 | 0.8107 |
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+ | No log | 3.0 | 12 | 0.4070 | 0.8708 | 0.7584 | 0.8708 | 0.8107 |
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+ | 0.4953 | 4.0 | 16 | 0.3890 | 0.8708 | 0.7584 | 0.8708 | 0.8107 |
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+ | 0.4953 | 5.0 | 20 | 0.3688 | 0.8708 | 0.7584 | 0.8708 | 0.8107 |
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+ | 0.4953 | 6.0 | 24 | 0.3549 | 0.8708 | 0.7584 | 0.8708 | 0.8107 |
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+ | 0.4953 | 7.0 | 28 | 0.3138 | 0.8708 | 0.7584 | 0.8708 | 0.8107 |
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+ | 0.4217 | 8.0 | 32 | 0.3330 | 0.8708 | 0.8312 | 0.8708 | 0.8308 |
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+ | 0.4217 | 9.0 | 36 | 0.2946 | 0.9 | 0.8881 | 0.9 | 0.8845 |
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+ | 0.4217 | 10.0 | 40 | 0.2753 | 0.9042 | 0.8938 | 0.9042 | 0.8905 |
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+ | 0.4217 | 11.0 | 44 | 0.2996 | 0.9 | 0.8909 | 0.9 | 0.8935 |
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+ | 0.3747 | 12.0 | 48 | 0.2684 | 0.9 | 0.8883 | 0.9 | 0.8894 |
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+ | 0.3747 | 13.0 | 52 | 0.2670 | 0.9 | 0.8883 | 0.9 | 0.8894 |
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+ | 0.3747 | 14.0 | 56 | 0.2722 | 0.9042 | 0.8940 | 0.9042 | 0.8951 |
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+ | 0.3579 | 15.0 | 60 | 0.2718 | 0.9042 | 0.8940 | 0.9042 | 0.8951 |
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  ### Framework versions
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