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mansee
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swin-tiny-patch4-window7-224-img_orientation

Image Classification
Transformers
PyTorch
swin
Generated from Trainer
Eval Results
Inference Endpoints
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  1. README.md +14 -9
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: 0.7797476986021139
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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 [mansee/swin-tiny-patch4-window7-224-img_orientation](https://huggingface.co/mansee/swin-tiny-patch4-window7-224-img_orientation) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.3966
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- - Accuracy: 0.7797
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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: 5
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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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- | 0.4741 | 1.0 | 412 | 0.4774 | 0.7346 |
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- | 0.4958 | 2.0 | 825 | 0.4403 | 0.7569 |
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- | 0.473 | 3.0 | 1237 | 0.4093 | 0.7659 |
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- | 0.4476 | 4.0 | 1650 | 0.4011 | 0.7734 |
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- | 0.4093 | 4.99 | 2060 | 0.3966 | 0.7797 |
 
 
 
 
 
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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: 0.7794067507671326
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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 [mansee/swin-tiny-patch4-window7-224-img_orientation](https://huggingface.co/mansee/swin-tiny-patch4-window7-224-img_orientation) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.4538
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+ - Accuracy: 0.7794
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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: 10
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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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+ | 0.3217 | 1.0 | 412 | 0.4922 | 0.7533 |
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+ | 0.3716 | 2.0 | 825 | 0.4986 | 0.7601 |
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+ | 0.3756 | 3.0 | 1237 | 0.4427 | 0.7762 |
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+ | 0.3926 | 4.0 | 1650 | 0.4515 | 0.7733 |
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+ | 0.3859 | 5.0 | 2062 | 0.4394 | 0.7717 |
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+ | 0.3822 | 6.0 | 2475 | 0.4410 | 0.7784 |
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+ | 0.3804 | 7.0 | 2887 | 0.4561 | 0.7767 |
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+ | 0.3231 | 8.0 | 3300 | 0.4505 | 0.7784 |
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+ | 0.3295 | 9.0 | 3712 | 0.4607 | 0.7789 |
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+ | 0.321 | 9.99 | 4120 | 0.4538 | 0.7794 |
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  ### Framework versions