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ongkn
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attraction-classifier

Image Classification
Transformers
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
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  1. README.md +16 -8
  2. model.safetensors +1 -1
README.md CHANGED
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.7746781115879828
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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 [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: 0.4988
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- - Accuracy: 0.7747
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 0.4908 | 1.15 | 150 | 0.5481 | 0.7103 |
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- | 0.4162 | 2.29 | 300 | 0.4733 | 0.7940 |
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- | 0.4201 | 3.44 | 450 | 0.4648 | 0.7876 |
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- | 0.3438 | 4.58 | 600 | 0.4803 | 0.7854 |
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- | 0.2595 | 5.73 | 750 | 0.4988 | 0.7747 |
 
 
 
 
 
 
 
 
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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: 0.8133047210300429
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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 [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: 0.5427
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+ - Accuracy: 0.8133
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 0.49 | 1.15 | 150 | 0.5494 | 0.7232 |
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+ | 0.4285 | 2.29 | 300 | 0.4832 | 0.7768 |
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+ | 0.461 | 3.44 | 450 | 0.4907 | 0.7618 |
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+ | 0.3535 | 4.58 | 600 | 0.4597 | 0.7811 |
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+ | 0.2758 | 5.73 | 750 | 0.5102 | 0.7790 |
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+ | 0.2705 | 6.87 | 900 | 0.4669 | 0.8004 |
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+ | 0.2614 | 8.02 | 1050 | 0.4598 | 0.8004 |
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+ | 0.2213 | 9.16 | 1200 | 0.4797 | 0.8112 |
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+ | 0.1682 | 10.31 | 1350 | 0.5601 | 0.7876 |
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+ | 0.144 | 11.45 | 1500 | 0.4544 | 0.8155 |
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+ | 0.1269 | 12.6 | 1650 | 0.4904 | 0.8262 |
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+ | 0.1638 | 13.74 | 1800 | 0.5052 | 0.8197 |
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+ | 0.0869 | 14.89 | 1950 | 0.5427 | 0.8133 |
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  ### Framework versions
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