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ongkn
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attraction-classifier

Image Classification
Transformers
Safetensors
vit
Generated from Trainer
Eval Results
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End of training

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  1. README.md +9 -13
  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: 0.8195652173913044
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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: 0.5046
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- - Accuracy: 0.8196
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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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- | 0.5197 | 1.15 | 150 | 0.5219 | 0.7370 |
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- | 0.4519 | 2.31 | 300 | 0.4860 | 0.7717 |
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- | 0.3846 | 3.46 | 450 | 0.4224 | 0.7870 |
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- | 0.4 | 4.62 | 600 | 0.4350 | 0.7891 |
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- | 0.3132 | 5.77 | 750 | 0.4066 | 0.8304 |
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- | 0.2948 | 6.92 | 900 | 0.4072 | 0.8239 |
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- | 0.2414 | 8.08 | 1050 | 0.4119 | 0.8326 |
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- | 0.2142 | 9.23 | 1200 | 0.4060 | 0.8217 |
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- | 0.1637 | 10.38 | 1350 | 0.5176 | 0.8065 |
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- | 0.1597 | 11.54 | 1500 | 0.5046 | 0.8196 |
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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: 0.7913043478260869
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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: 0.5241
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+ - Accuracy: 0.7913
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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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+ | 0.4858 | 1.15 | 150 | 0.5329 | 0.7304 |
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+ | 0.42 | 2.31 | 300 | 0.4627 | 0.7739 |
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+ | 0.3977 | 3.46 | 450 | 0.4414 | 0.8022 |
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+ | 0.338 | 4.62 | 600 | 0.4850 | 0.7739 |
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+ | 0.3128 | 5.77 | 750 | 0.4267 | 0.8 |
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+ | 0.2873 | 6.92 | 900 | 0.5241 | 0.7913 |
 
 
 
 
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  ### Framework versions
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