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update model card README.md

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@@ -13,8 +13,8 @@ model-index:
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  name: Image Classification
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  type: image-classification
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  dataset:
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- name: blurryimages
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- type: blurryimages
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  config: default
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  split: train
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  args: default
@@ -29,9 +29,9 @@ should probably proofread and complete it, then remove this comment. -->
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  # vit-base-blur
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- This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on a blurry images dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0007
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  - Accuracy: 1.0
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  ## Model description
@@ -51,22 +51,29 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 0.0002
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  - train_batch_size: 16
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  - eval_batch_size: 8
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  - seed: 42
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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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- - 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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- | 0.0027 | 2.27 | 100 | 0.0565 | 0.9870 |
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- | 0.0013 | 4.55 | 200 | 0.0012 | 1.0 |
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- | 0.0008 | 6.82 | 300 | 0.0008 | 1.0 |
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- | 0.0007 | 9.09 | 400 | 0.0007 | 1.0 |
 
 
 
 
 
 
 
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  ### Framework versions
 
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  name: Image Classification
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  type: image-classification
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  dataset:
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+ name: imagefolder
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+ type: imagefolder
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  config: default
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  split: train
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  args: default
 
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  # vit-base-blur
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+ This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0008
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  - Accuracy: 1.0
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  ## Model description
 
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 0.0001
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  - train_batch_size: 16
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  - eval_batch_size: 8
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  - seed: 42
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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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+ - num_epochs: 12
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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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+ | 0.0082 | 1.02 | 100 | 0.0107 | 1.0 |
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+ | 0.0079 | 2.04 | 200 | 0.0052 | 1.0 |
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+ | 0.0029 | 3.06 | 300 | 0.0028 | 1.0 |
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+ | 0.002 | 4.08 | 400 | 0.0020 | 1.0 |
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+ | 0.0016 | 5.1 | 500 | 0.0015 | 1.0 |
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+ | 0.0013 | 6.12 | 600 | 0.0013 | 1.0 |
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+ | 0.0011 | 7.14 | 700 | 0.0011 | 1.0 |
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+ | 0.001 | 8.16 | 800 | 0.0010 | 1.0 |
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+ | 0.0009 | 9.18 | 900 | 0.0009 | 1.0 |
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+ | 0.0008 | 10.2 | 1000 | 0.0008 | 1.0 |
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+ | 0.0008 | 11.22 | 1100 | 0.0008 | 1.0 |
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  ### Framework versions