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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 +17 -17
  2. model.safetensors +1 -1
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: 0.8147321428571429
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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: 0.5005
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- - Accuracy: 0.8147
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  ## Model description
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@@ -65,20 +65,20 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 0.6105 | 0.6 | 150 | 0.5459 | 0.6897 |
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- | 0.5778 | 1.19 | 300 | 0.5391 | 0.7344 |
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- | 0.4556 | 1.79 | 450 | 0.4952 | 0.7634 |
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- | 0.4429 | 2.38 | 600 | 0.5212 | 0.75 |
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- | 0.4056 | 2.98 | 750 | 0.5397 | 0.7656 |
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- | 0.4306 | 3.57 | 900 | 0.4718 | 0.7723 |
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- | 0.3223 | 4.17 | 1050 | 0.5190 | 0.7723 |
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- | 0.3323 | 4.76 | 1200 | 0.4670 | 0.7902 |
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- | 0.3393 | 5.36 | 1350 | 0.5131 | 0.7946 |
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- | 0.2648 | 5.95 | 1500 | 0.4877 | 0.8013 |
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- | 0.2568 | 6.55 | 1650 | 0.4933 | 0.8170 |
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- | 0.1744 | 7.14 | 1800 | 0.5128 | 0.8170 |
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- | 0.2558 | 7.74 | 1950 | 0.6192 | 0.7746 |
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- | 0.1618 | 8.33 | 2100 | 0.5005 | 0.8147 |
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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: 0.779510022271715
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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: 0.5413
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+ - Accuracy: 0.7795
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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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+ | 0.5906 | 0.59 | 150 | 0.5682 | 0.6904 |
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+ | 0.489 | 1.19 | 300 | 0.5132 | 0.7528 |
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+ | 0.5119 | 1.78 | 450 | 0.5435 | 0.7238 |
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+ | 0.4065 | 2.37 | 600 | 0.5917 | 0.7261 |
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+ | 0.4008 | 2.96 | 750 | 0.4804 | 0.7661 |
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+ | 0.3851 | 3.56 | 900 | 0.4760 | 0.7751 |
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+ | 0.287 | 4.15 | 1050 | 0.5359 | 0.7795 |
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+ | 0.317 | 4.74 | 1200 | 0.5060 | 0.7595 |
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+ | 0.2459 | 5.34 | 1350 | 0.4643 | 0.7862 |
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+ | 0.285 | 5.93 | 1500 | 0.4925 | 0.8062 |
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+ | 0.2894 | 6.52 | 1650 | 0.4474 | 0.8085 |
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+ | 0.2538 | 7.11 | 1800 | 0.5309 | 0.8018 |
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+ | 0.2387 | 7.71 | 1950 | 0.5362 | 0.7973 |
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+ | 0.1507 | 8.3 | 2100 | 0.5413 | 0.7795 |
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  ### Framework versions
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