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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 +13 -12
  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: 0.7838983050847458
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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: 0.5662
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- - Accuracy: 0.7839
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  ## Model description
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@@ -65,15 +65,16 @@ 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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- | 0.5293 | 1.13 | 150 | 0.5251 | 0.75 |
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- | 0.438 | 2.26 | 300 | 0.4974 | 0.7331 |
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- | 0.3452 | 3.38 | 450 | 0.4794 | 0.7881 |
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- | 0.3431 | 4.51 | 600 | 0.4787 | 0.7945 |
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- | 0.264 | 5.64 | 750 | 0.4994 | 0.7775 |
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- | 0.2361 | 6.77 | 900 | 0.5140 | 0.8114 |
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- | 0.2028 | 7.89 | 1050 | 0.6601 | 0.7627 |
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- | 0.2174 | 9.02 | 1200 | 0.5546 | 0.7818 |
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- | 0.1922 | 10.15 | 1350 | 0.5662 | 0.7839 |
 
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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: 0.7902542372881356
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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: 0.5746
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+ - Accuracy: 0.7903
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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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+ | 0.4968 | 1.13 | 150 | 0.5187 | 0.7627 |
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+ | 0.4266 | 2.26 | 300 | 0.4863 | 0.7627 |
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+ | 0.3521 | 3.38 | 450 | 0.5066 | 0.7627 |
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+ | 0.3407 | 4.51 | 600 | 0.4736 | 0.7860 |
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+ | 0.2895 | 5.64 | 750 | 0.5043 | 0.7712 |
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+ | 0.2595 | 6.77 | 900 | 0.6222 | 0.7669 |
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+ | 0.2132 | 7.89 | 1050 | 0.4935 | 0.8008 |
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+ | 0.2156 | 9.02 | 1200 | 0.5229 | 0.7924 |
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+ | 0.192 | 10.15 | 1350 | 0.5168 | 0.7881 |
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+ | 0.1329 | 11.28 | 1500 | 0.5746 | 0.7903 |
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  ### Framework versions
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