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ongkn
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attraction-classifier

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

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  1. README.md +21 -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: 0.756043956043956
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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: 0.5514
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- - Accuracy: 0.7560
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  ## Model description
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@@ -57,7 +57,7 @@ The following hyperparameters were used during training:
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  - eval_batch_size: 16
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  - seed: 69
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- - lr_scheduler_type: cosine
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  - lr_scheduler_warmup_ratio: 0.05
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  - num_epochs: 10
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 0.5592 | 0.59 | 150 | 0.7210 | 0.6 |
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- | 0.5506 | 1.17 | 300 | 0.5884 | 0.6703 |
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- | 0.4778 | 1.76 | 450 | 0.5711 | 0.6967 |
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- | 0.427 | 2.34 | 600 | 0.5350 | 0.7473 |
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- | 0.4146 | 2.93 | 750 | 0.4936 | 0.7626 |
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- | 0.3544 | 3.52 | 900 | 0.6238 | 0.7253 |
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- | 0.3431 | 4.1 | 1050 | 0.5962 | 0.7055 |
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- | 0.3273 | 4.69 | 1200 | 0.5514 | 0.7560 |
 
 
 
 
 
 
 
 
 
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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: 0.7802197802197802
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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: 0.5987
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+ - Accuracy: 0.7802
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  ## Model description
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  - eval_batch_size: 16
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  - seed: 69
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.05
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  - num_epochs: 10
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 0.5587 | 0.59 | 150 | 0.7040 | 0.5846 |
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+ | 0.5033 | 1.17 | 300 | 0.5777 | 0.7011 |
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+ | 0.4748 | 1.76 | 450 | 0.5658 | 0.7033 |
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+ | 0.4138 | 2.34 | 600 | 0.5599 | 0.7363 |
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+ | 0.3974 | 2.93 | 750 | 0.4866 | 0.7495 |
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+ | 0.3399 | 3.52 | 900 | 0.6066 | 0.7121 |
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+ | 0.3428 | 4.1 | 1050 | 0.5605 | 0.7253 |
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+ | 0.3339 | 4.69 | 1200 | 0.5439 | 0.7626 |
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+ | 0.2581 | 5.27 | 1350 | 0.6002 | 0.7604 |
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+ | 0.3193 | 5.86 | 1500 | 0.5304 | 0.7626 |
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+ | 0.2566 | 6.45 | 1650 | 0.5926 | 0.7736 |
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+ | 0.1883 | 7.03 | 1800 | 0.5800 | 0.7714 |
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+ | 0.1859 | 7.62 | 1950 | 0.5804 | 0.7824 |
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+ | 0.1834 | 8.2 | 2100 | 0.7244 | 0.7363 |
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+ | 0.1862 | 8.79 | 2250 | 0.5803 | 0.7890 |
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+ | 0.1781 | 9.38 | 2400 | 0.6134 | 0.7780 |
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+ | 0.1464 | 9.96 | 2550 | 0.5987 | 0.7802 |
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  ### Framework versions
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