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Yogesh1p
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swin-tiny-patch4-window7-224-finetuned-cp1

Image Classification
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swin
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  1. README.md +32 -23
  2. model.safetensors +1 -1
  3. runs/Nov21_03-56-28_b5f9a6b3f51f/events.out.tfevents.1700538989.b5f9a6b3f51f.3745.4 +2 -2
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: 0.8666666666666667
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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 [Yogesh1p/swin-tiny-patch4-window7-224-finetuned-cp1](https://huggingface.co/Yogesh1p/swin-tiny-patch4-window7-224-finetuned-cp1) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.3993
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- - Accuracy: 0.8667
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  ## Model description
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@@ -61,31 +61,40 @@ 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: 20
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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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- | No log | 0.92 | 3 | 0.6966 | 0.6667 |
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- | No log | 1.85 | 6 | 0.6159 | 0.7778 |
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- | No log | 2.77 | 9 | 0.6518 | 0.7111 |
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- | 0.6821 | 4.0 | 13 | 0.5392 | 0.7556 |
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- | 0.6821 | 4.92 | 16 | 0.5302 | 0.7333 |
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- | 0.6821 | 5.85 | 19 | 0.4517 | 0.7778 |
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- | 0.4852 | 6.77 | 22 | 0.4534 | 0.8222 |
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- | 0.4852 | 8.0 | 26 | 0.3993 | 0.8667 |
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- | 0.4852 | 8.92 | 29 | 0.3955 | 0.8667 |
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- | 0.4126 | 9.85 | 32 | 0.4563 | 0.7778 |
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- | 0.4126 | 10.77 | 35 | 0.4315 | 0.8 |
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- | 0.4126 | 12.0 | 39 | 0.6096 | 0.7556 |
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- | 0.3375 | 12.92 | 42 | 0.5938 | 0.7556 |
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- | 0.3375 | 13.85 | 45 | 0.5608 | 0.7333 |
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- | 0.3375 | 14.77 | 48 | 0.4996 | 0.7556 |
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- | 0.3164 | 16.0 | 52 | 0.4619 | 0.7778 |
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- | 0.3164 | 16.92 | 55 | 0.4123 | 0.8222 |
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- | 0.3164 | 17.85 | 58 | 0.3873 | 0.8222 |
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- | 0.2482 | 18.46 | 60 | 0.3830 | 0.8222 |
 
 
 
 
 
 
 
 
 
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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: 0.8444444444444444
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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 [Yogesh1p/swin-tiny-patch4-window7-224-finetuned-cp1](https://huggingface.co/Yogesh1p/swin-tiny-patch4-window7-224-finetuned-cp1) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.3494
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+ - Accuracy: 0.8444
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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 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | No log | 0.92 | 3 | 0.4305 | 0.8222 |
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+ | No log | 1.85 | 6 | 0.4510 | 0.8 |
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+ | No log | 2.77 | 9 | 0.4329 | 0.7778 |
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+ | 0.3489 | 4.0 | 13 | 0.4965 | 0.7778 |
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+ | 0.3489 | 4.92 | 16 | 0.4247 | 0.8 |
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+ | 0.3489 | 5.85 | 19 | 0.3559 | 0.8444 |
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+ | 0.2872 | 6.77 | 22 | 0.3628 | 0.8667 |
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+ | 0.2872 | 8.0 | 26 | 0.3511 | 0.8667 |
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+ | 0.2872 | 8.92 | 29 | 0.3538 | 0.8667 |
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+ | 0.3071 | 9.85 | 32 | 0.3644 | 0.8222 |
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+ | 0.3071 | 10.77 | 35 | 0.3860 | 0.8222 |
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+ | 0.3071 | 12.0 | 39 | 0.5917 | 0.8 |
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+ | 0.2765 | 12.92 | 42 | 0.5149 | 0.7778 |
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+ | 0.2765 | 13.85 | 45 | 0.5605 | 0.7556 |
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+ | 0.2765 | 14.77 | 48 | 0.4737 | 0.8 |
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+ | 0.2637 | 16.0 | 52 | 0.4109 | 0.8444 |
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+ | 0.2637 | 16.92 | 55 | 0.3584 | 0.8222 |
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+ | 0.2637 | 17.85 | 58 | 0.3771 | 0.8222 |
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+ | 0.2082 | 18.77 | 61 | 0.4406 | 0.8222 |
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+ | 0.2082 | 20.0 | 65 | 0.3592 | 0.8222 |
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+ | 0.2082 | 20.92 | 68 | 0.3302 | 0.8667 |
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+ | 0.1944 | 21.85 | 71 | 0.3206 | 0.8444 |
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+ | 0.1944 | 22.77 | 74 | 0.3215 | 0.8667 |
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+ | 0.1944 | 24.0 | 78 | 0.3209 | 0.8667 |
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+ | 0.2144 | 24.92 | 81 | 0.3327 | 0.8444 |
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+ | 0.2144 | 25.85 | 84 | 0.3506 | 0.8444 |
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+ | 0.2144 | 26.77 | 87 | 0.3545 | 0.8444 |
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+ | 0.1754 | 27.69 | 90 | 0.3494 | 0.8444 |
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  ### Framework versions
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