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vit_itri_gerd

Image Classification
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vit
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End of training

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  1. README.md +26 -21
  2. model.safetensors +1 -1
README.md CHANGED
@@ -21,21 +21,21 @@ model-index:
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  name: imagefolder
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  type: imagefolder
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  config: default
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- split: test
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  args: default
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.8846153846153846
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  - name: Precision
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  type: precision
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- value: 0.8854921550693027
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  - name: Recall
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  type: recall
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- value: 0.8846153846153846
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  - name: F1
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  type: f1
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- value: 0.8843087895951333
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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
@@ -45,11 +45,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5007
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- - Accuracy: 0.8846
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- - Precision: 0.8855
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- - Recall: 0.8846
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- - F1: 0.8843
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  ## Model description
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@@ -74,22 +74,27 @@ The following hyperparameters were used during training:
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  - seed: 42
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  - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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- - num_epochs: 10
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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- | 0.6349 | 1.0 | 70 | 0.4018 | 0.8173 | 0.8315 | 0.8173 | 0.8167 |
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- | 0.4485 | 2.0 | 140 | 0.4229 | 0.8341 | 0.8553 | 0.8341 | 0.8300 |
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- | 0.385 | 3.0 | 210 | 0.5430 | 0.8005 | 0.8398 | 0.8005 | 0.7916 |
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- | 0.3245 | 4.0 | 280 | 0.3723 | 0.8534 | 0.8565 | 0.8534 | 0.8535 |
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- | 0.2628 | 5.0 | 350 | 0.3668 | 0.8389 | 0.8509 | 0.8389 | 0.8363 |
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- | 0.209 | 6.0 | 420 | 0.5292 | 0.8365 | 0.8505 | 0.8365 | 0.8335 |
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- | 0.1544 | 7.0 | 490 | 0.3826 | 0.8774 | 0.8806 | 0.8774 | 0.8767 |
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- | 0.1261 | 8.0 | 560 | 0.4225 | 0.875 | 0.8801 | 0.875 | 0.8740 |
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- | 0.0683 | 9.0 | 630 | 0.4437 | 0.8726 | 0.8726 | 0.8726 | 0.8726 |
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- | 0.047 | 10.0 | 700 | 0.5007 | 0.8846 | 0.8855 | 0.8846 | 0.8843 |
 
 
 
 
 
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  ### Framework versions
 
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  name: imagefolder
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  type: imagefolder
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  config: default
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+ split: train
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  args: default
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.8802395209580839
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  - name: Precision
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  type: precision
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+ value: 0.8810801871515888
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  - name: Recall
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  type: recall
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+ value: 0.8802395209580839
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  - name: F1
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  type: f1
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+ value: 0.8801535602352574
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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-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.8160
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+ - Accuracy: 0.8802
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+ - Precision: 0.8811
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+ - Recall: 0.8802
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+ - F1: 0.8802
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  ## Model description
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  - seed: 42
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  - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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+ - num_epochs: 15
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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+ | 0.6915 | 1.0 | 63 | 0.4305 | 0.7904 | 0.7926 | 0.7904 | 0.7901 |
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+ | 0.455 | 2.0 | 126 | 0.7307 | 0.7605 | 0.7836 | 0.7605 | 0.7552 |
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+ | 0.372 | 3.0 | 189 | 0.4026 | 0.8024 | 0.8123 | 0.8024 | 0.8007 |
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+ | 0.3159 | 4.0 | 252 | 0.3805 | 0.8323 | 0.8340 | 0.8323 | 0.8321 |
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+ | 0.2906 | 5.0 | 315 | 0.4334 | 0.8323 | 0.8326 | 0.8323 | 0.8323 |
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+ | 0.2589 | 6.0 | 378 | 0.4235 | 0.8084 | 0.8232 | 0.8084 | 0.8060 |
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+ | 0.2024 | 7.0 | 441 | 0.4003 | 0.8503 | 0.8516 | 0.8503 | 0.8502 |
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+ | 0.1218 | 8.0 | 504 | 0.6308 | 0.8204 | 0.8270 | 0.8204 | 0.8193 |
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+ | 0.1226 | 9.0 | 567 | 0.5468 | 0.8323 | 0.8353 | 0.8323 | 0.8319 |
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+ | 0.0627 | 10.0 | 630 | 0.7390 | 0.8263 | 0.8286 | 0.8263 | 0.8260 |
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+ | 0.0374 | 11.0 | 693 | 0.8669 | 0.8503 | 0.8503 | 0.8503 | 0.8503 |
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+ | 0.0389 | 12.0 | 756 | 0.6790 | 0.8623 | 0.8627 | 0.8623 | 0.8622 |
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+ | 0.0122 | 13.0 | 819 | 0.8346 | 0.8683 | 0.8701 | 0.8683 | 0.8681 |
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+ | 0.0064 | 14.0 | 882 | 0.7985 | 0.8802 | 0.8804 | 0.8802 | 0.8802 |
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+ | 0.0071 | 15.0 | 945 | 0.8160 | 0.8802 | 0.8811 | 0.8802 | 0.8802 |
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  ### Framework versions
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