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ongkn
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attraction-classifier

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

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  1. README.md +13 -15
  2. model.safetensors +1 -1
README.md CHANGED
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  ---
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- license: apache-2.0
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  base_model: google/vit-base-patch16-224-in21k
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  tags:
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  - generated_from_trainer
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.7630331753554502
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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 +31,8 @@ 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: 0.5610
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- - Accuracy: 0.7630
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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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- | 0.5741 | 0.63 | 150 | 0.5854 | 0.6872 |
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- | 0.5918 | 1.26 | 300 | 0.5624 | 0.7038 |
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- | 0.5056 | 1.89 | 450 | 0.5353 | 0.7109 |
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- | 0.5178 | 2.52 | 600 | 0.5753 | 0.7014 |
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- | 0.4277 | 3.15 | 750 | 0.5325 | 0.7417 |
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- | 0.3949 | 3.78 | 900 | 0.4820 | 0.7678 |
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- | 0.3536 | 4.41 | 1050 | 0.4987 | 0.7725 |
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- | 0.3103 | 5.04 | 1200 | 0.4543 | 0.7938 |
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- | 0.2928 | 5.67 | 1350 | 0.5102 | 0.7701 |
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- | 0.2351 | 6.3 | 1500 | 0.4802 | 0.7820 |
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- | 0.2635 | 6.93 | 1650 | 0.5610 | 0.7630 |
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  ### Framework versions
 
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  ---
 
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  base_model: google/vit-base-patch16-224-in21k
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  tags:
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  - generated_from_trainer
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.7546728971962616
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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: 0.5790
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+ - Accuracy: 0.7547
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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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+ | 0.5533 | 0.62 | 150 | 0.5783 | 0.6916 |
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+ | 0.5608 | 1.24 | 300 | 0.5504 | 0.7243 |
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+ | 0.5382 | 1.87 | 450 | 0.5403 | 0.75 |
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+ | 0.4353 | 2.49 | 600 | 0.5244 | 0.7383 |
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+ | 0.3963 | 3.11 | 750 | 0.6338 | 0.7220 |
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+ | 0.3963 | 3.73 | 900 | 0.5162 | 0.7383 |
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+ | 0.3183 | 4.36 | 1050 | 0.5115 | 0.7710 |
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+ | 0.3715 | 4.98 | 1200 | 0.5172 | 0.7640 |
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+ | 0.2492 | 5.6 | 1350 | 0.5787 | 0.7477 |
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+ | 0.2739 | 6.22 | 1500 | 0.5790 | 0.7547 |
 
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  ### Framework versions
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