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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 +59 -39
  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: 0.8033333333333333
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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
@@ -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: 0.4788
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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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@@ -72,42 +72,62 @@ 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: 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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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.7433333333333333
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  - name: Precision
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  type: precision
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+ value: 0.7306273291925466
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  - name: Recall
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  type: recall
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+ value: 0.7433333333333333
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  ---
35
 
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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.
42
  It achieves the following results on the evaluation set:
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+ - Loss: 0.5534
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+ - Accuracy: 0.7433
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+ - Precision: 0.7306
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+ - Recall: 0.7433
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+ - F1 Score: 0.7344
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  ## Model description
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
73
  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 50
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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 | 0.7306 | 0.4 | 0.6521 | 0.4 | 0.3821 |
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+ | No log | 2.0 | 8 | 0.5815 | 0.7333 | 0.8050 | 0.7333 | 0.6286 |
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+ | No log | 3.0 | 12 | 0.5700 | 0.725 | 0.5256 | 0.725 | 0.6094 |
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+ | No log | 4.0 | 16 | 0.5635 | 0.725 | 0.5256 | 0.725 | 0.6094 |
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+ | No log | 5.0 | 20 | 0.5509 | 0.7292 | 0.8028 | 0.7292 | 0.6191 |
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+ | No log | 6.0 | 24 | 0.5356 | 0.7417 | 0.7438 | 0.7417 | 0.6589 |
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+ | No log | 7.0 | 28 | 0.5353 | 0.75 | 0.7360 | 0.75 | 0.6895 |
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+ | No log | 8.0 | 32 | 0.5299 | 0.7375 | 0.7090 | 0.7375 | 0.6668 |
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+ | No log | 9.0 | 36 | 0.5335 | 0.7667 | 0.7509 | 0.7667 | 0.7310 |
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+ | No log | 10.0 | 40 | 0.5344 | 0.7417 | 0.7315 | 0.7417 | 0.6644 |
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+ | No log | 11.0 | 44 | 0.5297 | 0.7458 | 0.7279 | 0.7458 | 0.6821 |
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+ | No log | 12.0 | 48 | 0.5202 | 0.75 | 0.7360 | 0.75 | 0.6895 |
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+ | 0.5942 | 13.0 | 52 | 0.5325 | 0.7542 | 0.7411 | 0.7542 | 0.7452 |
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+ | 0.5942 | 14.0 | 56 | 0.5139 | 0.7583 | 0.7505 | 0.7583 | 0.7039 |
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+ | 0.5942 | 15.0 | 60 | 0.5528 | 0.7417 | 0.7347 | 0.7417 | 0.7377 |
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+ | 0.5942 | 16.0 | 64 | 0.5070 | 0.7625 | 0.7437 | 0.7625 | 0.7277 |
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+ | 0.5942 | 17.0 | 68 | 0.5193 | 0.775 | 0.7594 | 0.775 | 0.7592 |
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+ | 0.5942 | 18.0 | 72 | 0.5090 | 0.7583 | 0.7448 | 0.7583 | 0.7487 |
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+ | 0.5942 | 19.0 | 76 | 0.5189 | 0.7792 | 0.7847 | 0.7792 | 0.7816 |
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+ | 0.5942 | 20.0 | 80 | 0.5214 | 0.775 | 0.7795 | 0.775 | 0.7770 |
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+ | 0.5942 | 21.0 | 84 | 0.5188 | 0.775 | 0.7710 | 0.775 | 0.7728 |
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+ | 0.5942 | 22.0 | 88 | 0.5029 | 0.7667 | 0.7526 | 0.7667 | 0.7557 |
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+ | 0.5942 | 23.0 | 92 | 0.5061 | 0.7833 | 0.7734 | 0.7833 | 0.7761 |
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+ | 0.5942 | 24.0 | 96 | 0.5350 | 0.7667 | 0.7713 | 0.7667 | 0.7687 |
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+ | 0.4829 | 25.0 | 100 | 0.5149 | 0.7542 | 0.7330 | 0.7542 | 0.7337 |
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+ | 0.4829 | 26.0 | 104 | 0.5283 | 0.7583 | 0.7737 | 0.7583 | 0.7641 |
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+ | 0.4829 | 27.0 | 108 | 0.5109 | 0.7792 | 0.7647 | 0.7792 | 0.7646 |
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+ | 0.4829 | 28.0 | 112 | 0.5258 | 0.775 | 0.7729 | 0.775 | 0.7739 |
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+ | 0.4829 | 29.0 | 116 | 0.5207 | 0.7625 | 0.745 | 0.7625 | 0.7468 |
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+ | 0.4829 | 30.0 | 120 | 0.5306 | 0.75 | 0.7357 | 0.75 | 0.7400 |
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+ | 0.4829 | 31.0 | 124 | 0.5455 | 0.75 | 0.7375 | 0.75 | 0.7417 |
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+ | 0.4829 | 32.0 | 128 | 0.5653 | 0.7458 | 0.7380 | 0.7458 | 0.7412 |
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+ | 0.4829 | 33.0 | 132 | 0.5565 | 0.7417 | 0.7212 | 0.7417 | 0.7256 |
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+ | 0.4829 | 34.0 | 136 | 0.5468 | 0.7708 | 0.7658 | 0.7708 | 0.7679 |
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+ | 0.4829 | 35.0 | 140 | 0.5268 | 0.7833 | 0.7723 | 0.7833 | 0.7747 |
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+ | 0.4829 | 36.0 | 144 | 0.5260 | 0.775 | 0.7710 | 0.775 | 0.7728 |
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+ | 0.4829 | 37.0 | 148 | 0.5281 | 0.775 | 0.7659 | 0.775 | 0.7689 |
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+ | 0.3846 | 38.0 | 152 | 0.5385 | 0.7708 | 0.7742 | 0.7708 | 0.7724 |
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+ | 0.3846 | 39.0 | 156 | 0.5253 | 0.7708 | 0.7623 | 0.7708 | 0.7653 |
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+ | 0.3846 | 40.0 | 160 | 0.5319 | 0.7708 | 0.7719 | 0.7708 | 0.7714 |
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+ | 0.3846 | 41.0 | 164 | 0.5311 | 0.775 | 0.7631 | 0.775 | 0.7660 |
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+ | 0.3846 | 42.0 | 168 | 0.5325 | 0.7792 | 0.7683 | 0.7792 | 0.7711 |
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+ | 0.3846 | 43.0 | 172 | 0.5254 | 0.7667 | 0.7606 | 0.7667 | 0.7631 |
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+ | 0.3846 | 44.0 | 176 | 0.5232 | 0.7708 | 0.7623 | 0.7708 | 0.7653 |
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+ | 0.3846 | 45.0 | 180 | 0.5291 | 0.7708 | 0.7640 | 0.7708 | 0.7667 |
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+ | 0.3846 | 46.0 | 184 | 0.5356 | 0.7708 | 0.7607 | 0.7708 | 0.7639 |
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+ | 0.3846 | 47.0 | 188 | 0.5400 | 0.7708 | 0.7607 | 0.7708 | 0.7639 |
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+ | 0.3846 | 48.0 | 192 | 0.5409 | 0.7667 | 0.7540 | 0.7667 | 0.7573 |
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+ | 0.3846 | 49.0 | 196 | 0.5403 | 0.7667 | 0.7540 | 0.7667 | 0.7573 |
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+ | 0.3353 | 50.0 | 200 | 0.5397 | 0.7708 | 0.7592 | 0.7708 | 0.7624 |
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  ### Framework versions
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