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ongkn
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attraction-classifier

Image Classification
Transformers
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Model card Files
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End of training

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  1. README.md +18 -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: 0.7977777777777778
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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: 0.5575
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- - Accuracy: 0.7978
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  ## Model description
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@@ -65,20 +65,21 @@ 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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- | 0.6334 | 0.59 | 150 | 0.5455 | 0.7311 |
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- | 0.499 | 1.19 | 300 | 0.5235 | 0.7356 |
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- | 0.5162 | 1.78 | 450 | 0.5364 | 0.7222 |
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- | 0.4464 | 2.37 | 600 | 0.5016 | 0.7711 |
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- | 0.4574 | 2.96 | 750 | 0.4697 | 0.7733 |
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- | 0.3451 | 3.56 | 900 | 0.4832 | 0.7911 |
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- | 0.3305 | 4.15 | 1050 | 0.4713 | 0.7933 |
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- | 0.345 | 4.74 | 1200 | 0.4679 | 0.8089 |
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- | 0.2773 | 5.34 | 1350 | 0.4857 | 0.8 |
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- | 0.2596 | 5.93 | 1500 | 0.4896 | 0.7911 |
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- | 0.2374 | 6.52 | 1650 | 0.5070 | 0.8133 |
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- | 0.2448 | 7.11 | 1800 | 0.5372 | 0.7867 |
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- | 0.2128 | 7.71 | 1950 | 0.5421 | 0.7844 |
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- | 0.1489 | 8.3 | 2100 | 0.5575 | 0.7978 |
 
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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: 0.8252212389380531
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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: 0.5421
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+ - Accuracy: 0.8252
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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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+ | 0.5852 | 0.59 | 150 | 0.5592 | 0.7035 |
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+ | 0.6115 | 1.18 | 300 | 0.6364 | 0.6704 |
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+ | 0.475 | 1.77 | 450 | 0.5351 | 0.7257 |
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+ | 0.4268 | 2.36 | 600 | 0.5552 | 0.7190 |
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+ | 0.4349 | 2.95 | 750 | 0.4939 | 0.7677 |
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+ | 0.364 | 3.54 | 900 | 0.4969 | 0.7611 |
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+ | 0.343 | 4.13 | 1050 | 0.5717 | 0.7721 |
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+ | 0.3516 | 4.72 | 1200 | 0.4815 | 0.7898 |
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+ | 0.3222 | 5.31 | 1350 | 0.4609 | 0.8142 |
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+ | 0.2444 | 5.91 | 1500 | 0.5285 | 0.7854 |
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+ | 0.2152 | 6.5 | 1650 | 0.4901 | 0.8097 |
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+ | 0.2318 | 7.09 | 1800 | 0.4804 | 0.8252 |
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+ | 0.1875 | 7.68 | 1950 | 0.5690 | 0.8119 |
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+ | 0.195 | 8.27 | 2100 | 0.5276 | 0.8031 |
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+ | 0.1409 | 8.86 | 2250 | 0.5421 | 0.8252 |
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  ### Framework versions
model.safetensors CHANGED
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