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amaye15
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SwinV2-Base-Document-Classifier

Image Classification
Transformers
Safetensors
swinv2
Generated from Trainer
Inference Endpoints
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End of training

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  1. README.md +15 -26
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -20,11 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window16-256](https://huggingface.co/microsoft/swinv2-base-patch4-window16-256) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0573
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- - Accuracy: 0.9810
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- - F1: 0.9810
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- - Precision: 0.9813
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- - Recall: 0.9810
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  ## Model description
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@@ -51,32 +51,21 @@ The following hyperparameters were used during training:
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  - total_train_batch_size: 128
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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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- - training_steps: 400
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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- | 0.0814 | 0.12 | 50 | 0.0876 | 0.9740 | 0.9741 | 0.9749 | 0.9740 |
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- | 0.078 | 0.13 | 51 | 0.0658 | 0.9797 | 0.9797 | 0.9799 | 0.9797 |
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- | 0.143 | 0.13 | 52 | 0.0609 | 0.9831 | 0.9831 | 0.9832 | 0.9831 |
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- | 0.0863 | 0.13 | 53 | 0.0595 | 0.9832 | 0.9832 | 0.9834 | 0.9832 |
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- | 0.0561 | 0.14 | 54 | 0.0650 | 0.9825 | 0.9825 | 0.9826 | 0.9825 |
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- | 0.1118 | 0.14 | 55 | 0.0697 | 0.9804 | 0.9804 | 0.9806 | 0.9804 |
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- | 0.1258 | 0.14 | 56 | 0.1053 | 0.9678 | 0.9678 | 0.9697 | 0.9678 |
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- | 0.132 | 0.14 | 57 | 0.0672 | 0.9781 | 0.9781 | 0.9785 | 0.9781 |
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- | 0.1564 | 0.14 | 58 | 0.0582 | 0.9827 | 0.9827 | 0.9829 | 0.9827 |
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- | 0.1085 | 0.15 | 59 | 0.0557 | 0.9849 | 0.9848 | 0.9849 | 0.9848 |
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- | 0.0506 | 0.15 | 60 | 0.0548 | 0.9845 | 0.9845 | 0.9845 | 0.9845 |
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- | 0.0787 | 0.15 | 61 | 0.1049 | 0.9660 | 0.9661 | 0.9681 | 0.9660 |
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- | 0.1211 | 0.15 | 62 | 0.0792 | 0.9741 | 0.9741 | 0.9746 | 0.9741 |
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- | 0.0907 | 0.16 | 63 | 0.0608 | 0.9826 | 0.9825 | 0.9826 | 0.9826 |
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- | 0.0419 | 0.16 | 64 | 0.0577 | 0.9832 | 0.9832 | 0.9833 | 0.9832 |
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- | 0.1659 | 0.16 | 65 | 0.0548 | 0.9843 | 0.9843 | 0.9844 | 0.9843 |
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- | 0.0983 | 0.17 | 66 | 0.0657 | 0.9805 | 0.9805 | 0.9814 | 0.9804 |
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- | 0.0535 | 0.17 | 67 | 0.0493 | 0.9851 | 0.9851 | 0.9851 | 0.9851 |
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- | 0.0936 | 0.17 | 68 | 0.0588 | 0.9827 | 0.9827 | 0.9830 | 0.9827 |
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- | 0.0933 | 0.17 | 69 | 0.0573 | 0.9810 | 0.9810 | 0.9813 | 0.9810 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window16-256](https://huggingface.co/microsoft/swinv2-base-patch4-window16-256) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0191
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+ - Accuracy: 0.9946
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+ - F1: 0.9946
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+ - Precision: 0.9946
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+ - Recall: 0.9946
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  ## Model description
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  - total_train_batch_size: 128
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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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+ - training_steps: 800
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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+ | 0.0794 | 0.2 | 160 | 0.0330 | 0.9899 | 0.9899 | 0.9899 | 0.9899 |
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+ | 0.0619 | 0.3 | 240 | 0.0278 | 0.9908 | 0.9908 | 0.9908 | 0.9909 |
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+ | 0.0499 | 0.4 | 320 | 0.0272 | 0.9914 | 0.9914 | 0.9914 | 0.9914 |
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+ | 0.0482 | 0.5 | 400 | 0.0275 | 0.9917 | 0.9917 | 0.9917 | 0.9917 |
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+ | 0.0416 | 1.1 | 480 | 0.0218 | 0.9931 | 0.9931 | 0.9931 | 0.9931 |
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+ | 0.0353 | 1.2 | 560 | 0.0208 | 0.9942 | 0.9942 | 0.9942 | 0.9942 |
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+ | 0.0306 | 1.3 | 640 | 0.0183 | 0.9949 | 0.9949 | 0.9949 | 0.9949 |
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+ | 0.0296 | 1.4 | 720 | 0.0198 | 0.9944 | 0.9944 | 0.9944 | 0.9944 |
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+ | 0.0305 | 1.5 | 800 | 0.0191 | 0.9946 | 0.9946 | 0.9946 | 0.9946 |
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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