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End of training

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  1. README.md +8 -11
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@@ -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: 0.575
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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: 1.1641
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- - Accuracy: 0.575
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  ## Model description
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  - seed: 42
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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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- - num_epochs: 7
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | No log | 1.0 | 40 | 1.6582 | 0.475 |
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- | No log | 2.0 | 80 | 1.4117 | 0.4375 |
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- | No log | 3.0 | 120 | 1.3116 | 0.55 |
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- | No log | 4.0 | 160 | 1.2137 | 0.5437 |
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- | No log | 5.0 | 200 | 1.1592 | 0.5813 |
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- | No log | 6.0 | 240 | 1.1247 | 0.5875 |
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- | No log | 7.0 | 280 | 1.1440 | 0.6188 |
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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: 0.5
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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: 1.4124
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+ - Accuracy: 0.5
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  ## Model description
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  - seed: 42
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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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+ - num_epochs: 4
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | No log | 1.0 | 40 | 1.8082 | 0.3 |
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+ | No log | 2.0 | 80 | 1.5637 | 0.3688 |
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+ | No log | 3.0 | 120 | 1.4570 | 0.4562 |
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+ | No log | 4.0 | 160 | 1.4012 | 0.525 |
 
 
 
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  ### Framework versions