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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 +40 -25
  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: 0.8933333333333333
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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
@@ -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: 0.2912
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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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@@ -72,32 +72,47 @@ 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: 15
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  ### Training results
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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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- - Transformers 4.33.2
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  - Pytorch 2.0.1+cu118
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  - Datasets 2.14.5
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  - Tokenizers 0.13.3
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.8033333333333333
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  - name: Precision
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  type: precision
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+ value: 0.7970708748615725
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  - name: Recall
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  type: recall
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+ value: 0.8033333333333333
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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: 0.4788
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+ - Accuracy: 0.8033
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+ - Precision: 0.7971
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+ - Recall: 0.8033
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+ - F1 Score: 0.7802
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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: 30
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  ### Training results
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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 | 0.5946 | 0.7333 | 0.5378 | 0.7333 | 0.6205 |
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+ | No log | 2.0 | 8 | 0.6006 | 0.7333 | 0.5378 | 0.7333 | 0.6205 |
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+ | No log | 3.0 | 12 | 0.5677 | 0.7333 | 0.5378 | 0.7333 | 0.6205 |
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+ | No log | 4.0 | 16 | 0.5616 | 0.7333 | 0.5378 | 0.7333 | 0.6205 |
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+ | No log | 5.0 | 20 | 0.5556 | 0.75 | 0.7193 | 0.75 | 0.7023 |
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+ | No log | 6.0 | 24 | 0.5435 | 0.7667 | 0.7819 | 0.7667 | 0.7019 |
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+ | No log | 7.0 | 28 | 0.5318 | 0.7792 | 0.7885 | 0.7792 | 0.7281 |
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+ | 0.5745 | 8.0 | 32 | 0.5316 | 0.7542 | 0.7262 | 0.7542 | 0.7126 |
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+ | 0.5745 | 9.0 | 36 | 0.5232 | 0.7667 | 0.7533 | 0.7667 | 0.7185 |
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+ | 0.5745 | 10.0 | 40 | 0.5226 | 0.7708 | 0.7639 | 0.7708 | 0.7217 |
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+ | 0.5745 | 11.0 | 44 | 0.5217 | 0.7708 | 0.7597 | 0.7708 | 0.7253 |
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+ | 0.5745 | 12.0 | 48 | 0.5224 | 0.7625 | 0.7561 | 0.7625 | 0.7034 |
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+ | 0.5745 | 13.0 | 52 | 0.5213 | 0.7708 | 0.7510 | 0.7708 | 0.7409 |
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+ | 0.5745 | 14.0 | 56 | 0.5207 | 0.7667 | 0.7709 | 0.7667 | 0.7064 |
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+ | 0.4741 | 15.0 | 60 | 0.5247 | 0.7583 | 0.7343 | 0.7583 | 0.7334 |
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+ | 0.4741 | 16.0 | 64 | 0.5352 | 0.7708 | 0.7639 | 0.7708 | 0.7217 |
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+ | 0.4741 | 17.0 | 68 | 0.5227 | 0.7708 | 0.7507 | 0.7708 | 0.7460 |
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+ | 0.4741 | 18.0 | 72 | 0.5206 | 0.7583 | 0.7564 | 0.7583 | 0.6912 |
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+ | 0.4741 | 19.0 | 76 | 0.5088 | 0.775 | 0.7627 | 0.775 | 0.7353 |
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+ | 0.4741 | 20.0 | 80 | 0.5144 | 0.7667 | 0.7503 | 0.7667 | 0.7221 |
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+ | 0.4741 | 21.0 | 84 | 0.5227 | 0.7875 | 0.7918 | 0.7875 | 0.7453 |
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+ | 0.4741 | 22.0 | 88 | 0.5150 | 0.775 | 0.7564 | 0.775 | 0.7494 |
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+ | 0.4233 | 23.0 | 92 | 0.5240 | 0.7667 | 0.7533 | 0.7667 | 0.7185 |
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+ | 0.4233 | 24.0 | 96 | 0.5156 | 0.7792 | 0.7684 | 0.7792 | 0.7418 |
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+ | 0.4233 | 25.0 | 100 | 0.5141 | 0.7792 | 0.7631 | 0.7792 | 0.7503 |
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+ | 0.4233 | 26.0 | 104 | 0.5234 | 0.7833 | 0.7813 | 0.7833 | 0.7420 |
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+ | 0.4233 | 27.0 | 108 | 0.5175 | 0.7833 | 0.7813 | 0.7833 | 0.7420 |
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+ | 0.4233 | 28.0 | 112 | 0.5122 | 0.7958 | 0.7856 | 0.7958 | 0.7715 |
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+ | 0.4233 | 29.0 | 116 | 0.5126 | 0.7958 | 0.7856 | 0.7958 | 0.7715 |
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+ | 0.3931 | 30.0 | 120 | 0.5130 | 0.7958 | 0.7856 | 0.7958 | 0.7715 |
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  ### Framework versions
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+ - Transformers 4.33.3
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  - Pytorch 2.0.1+cu118
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  - Datasets 2.14.5
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  - Tokenizers 0.13.3
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