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ongkn
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attraction-classifier

Image Classification
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vit
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  1. README.md +30 -30
  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: 0.8081761006289309
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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: 0.4407
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- - Accuracy: 0.8082
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  ## Model description
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@@ -67,33 +67,33 @@ 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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- | 0.6797 | 0.34 | 15 | 0.6821 | 0.6258 |
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- | 0.5629 | 0.67 | 30 | 0.5933 | 0.7421 |
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- | 0.5541 | 1.01 | 45 | 0.5586 | 0.7421 |
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- | 0.5129 | 1.34 | 60 | 0.5105 | 0.7736 |
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- | 0.5997 | 1.68 | 75 | 0.4996 | 0.7704 |
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- | 0.5485 | 2.01 | 90 | 0.5391 | 0.6981 |
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- | 0.4797 | 2.35 | 105 | 0.4873 | 0.7516 |
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- | 0.5706 | 2.68 | 120 | 0.5036 | 0.7516 |
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- | 0.5656 | 3.02 | 135 | 0.4679 | 0.7925 |
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- | 0.4468 | 3.35 | 150 | 0.4953 | 0.7610 |
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- | 0.3734 | 3.69 | 165 | 0.4321 | 0.8082 |
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- | 0.372 | 4.02 | 180 | 0.4467 | 0.7925 |
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- | 0.4824 | 4.36 | 195 | 0.4313 | 0.8145 |
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- | 0.3081 | 4.69 | 210 | 0.4879 | 0.7893 |
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- | 0.5369 | 5.03 | 225 | 0.5796 | 0.7107 |
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- | 0.3339 | 5.36 | 240 | 0.4006 | 0.8333 |
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- | 0.4371 | 5.7 | 255 | 0.4193 | 0.8365 |
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- | 0.2505 | 6.03 | 270 | 0.4585 | 0.7987 |
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- | 0.3417 | 6.37 | 285 | 0.4085 | 0.8333 |
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- | 0.2224 | 6.7 | 300 | 0.5208 | 0.7327 |
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- | 0.2901 | 7.04 | 315 | 0.4157 | 0.8302 |
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- | 0.395 | 7.37 | 330 | 0.4305 | 0.8270 |
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- | 0.1365 | 7.71 | 345 | 0.4743 | 0.8050 |
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- | 0.2152 | 8.04 | 360 | 0.4125 | 0.8302 |
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- | 0.1959 | 8.38 | 375 | 0.4246 | 0.8270 |
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- | 0.2279 | 8.72 | 390 | 0.5326 | 0.7704 |
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- | 0.1565 | 9.05 | 405 | 0.4407 | 0.8082 |
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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: 0.7955974842767296
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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: 0.4691
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+ - Accuracy: 0.7956
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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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+ | 0.6703 | 0.34 | 15 | 0.6354 | 0.7327 |
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+ | 0.5449 | 0.67 | 30 | 0.5836 | 0.7421 |
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+ | 0.5407 | 1.01 | 45 | 0.5594 | 0.7421 |
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+ | 0.5255 | 1.34 | 60 | 0.5294 | 0.7547 |
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+ | 0.5586 | 1.68 | 75 | 0.5171 | 0.7642 |
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+ | 0.5438 | 2.01 | 90 | 0.5212 | 0.7704 |
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+ | 0.4807 | 2.35 | 105 | 0.5181 | 0.7390 |
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+ | 0.6202 | 2.68 | 120 | 0.4972 | 0.7704 |
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+ | 0.5021 | 3.02 | 135 | 0.4566 | 0.7987 |
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+ | 0.4313 | 3.35 | 150 | 0.4852 | 0.7925 |
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+ | 0.3532 | 3.69 | 165 | 0.4378 | 0.8113 |
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+ | 0.3577 | 4.02 | 180 | 0.4515 | 0.8019 |
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+ | 0.4736 | 4.36 | 195 | 0.4498 | 0.7893 |
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+ | 0.3516 | 4.69 | 210 | 0.4408 | 0.8239 |
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+ | 0.4437 | 5.03 | 225 | 0.4611 | 0.7799 |
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+ | 0.3543 | 5.36 | 240 | 0.4294 | 0.8208 |
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+ | 0.4029 | 5.7 | 255 | 0.4155 | 0.8428 |
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+ | 0.3808 | 6.03 | 270 | 0.4116 | 0.8302 |
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+ | 0.3211 | 6.37 | 285 | 0.4009 | 0.8302 |
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+ | 0.2949 | 6.7 | 300 | 0.4321 | 0.8176 |
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+ | 0.2663 | 7.04 | 315 | 0.4229 | 0.8396 |
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+ | 0.3049 | 7.37 | 330 | 0.4110 | 0.8365 |
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+ | 0.1303 | 7.71 | 345 | 0.4288 | 0.8333 |
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+ | 0.2079 | 8.04 | 360 | 0.4218 | 0.8208 |
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+ | 0.208 | 8.38 | 375 | 0.3908 | 0.8365 |
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+ | 0.2067 | 8.72 | 390 | 0.5191 | 0.7862 |
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+ | 0.1635 | 9.05 | 405 | 0.4691 | 0.7956 |
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  ### Framework versions
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