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vit_itri_2class_downsample

Image Classification
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vit
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End of training

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  1. README.md +19 -19
  2. model.safetensors +1 -1
README.md CHANGED
@@ -26,16 +26,16 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.8977272727272727
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  - name: Precision
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  type: precision
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- value: 0.9210261342224775
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  - name: Recall
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  type: recall
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- value: 0.8977272727272727
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  - name: F1
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  type: f1
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- value: 0.9067029271654793
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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: 0.0067
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- - Accuracy: 0.8977
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- - Precision: 0.9210
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- - Recall: 0.8977
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- - F1: 0.9067
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  ## Model description
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@@ -80,16 +80,16 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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- | 0.0067 | 1.0 | 197 | 0.0019 | 0.8628 | 0.9198 | 0.8628 | 0.8826 |
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- | 0.0005 | 2.0 | 394 | 0.0013 | 0.9040 | 0.9025 | 0.9040 | 0.9032 |
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- | 0.0005 | 3.0 | 591 | 0.0016 | 0.9191 | 0.9054 | 0.9191 | 0.8959 |
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- | 0.0005 | 4.0 | 788 | 0.0021 | 0.8679 | 0.9180 | 0.8679 | 0.8858 |
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- | 0.0001 | 5.0 | 985 | 0.0033 | 0.9102 | 0.9187 | 0.9102 | 0.9139 |
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- | 0.0001 | 6.0 | 1182 | 0.0025 | 0.9151 | 0.9254 | 0.9151 | 0.9194 |
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- | 0.0 | 7.0 | 1379 | 0.0038 | 0.8945 | 0.9218 | 0.8945 | 0.9048 |
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- | 0.0 | 8.0 | 1576 | 0.0048 | 0.9090 | 0.9255 | 0.9090 | 0.9155 |
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- | 0.0 | 9.0 | 1773 | 0.0066 | 0.8917 | 0.9198 | 0.8917 | 0.9024 |
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- | 0.0 | 10.0 | 1970 | 0.0067 | 0.8977 | 0.9210 | 0.8977 | 0.9067 |
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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: 0.8677685950413223
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  - name: Precision
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  type: precision
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+ value: 0.9081284623394801
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  - name: Recall
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  type: recall
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+ value: 0.8677685950413223
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  - name: F1
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  type: f1
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+ value: 0.8832453953563496
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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: 0.8733
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+ - Accuracy: 0.8678
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+ - Precision: 0.9081
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+ - Recall: 0.8678
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+ - F1: 0.8832
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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+ | 0.2762 | 1.0 | 197 | 0.2583 | 0.9268 | 0.9254 | 0.9268 | 0.9261 |
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+ | 0.0972 | 2.0 | 394 | 0.6026 | 0.8398 | 0.9182 | 0.8398 | 0.8663 |
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+ | 0.05 | 3.0 | 591 | 0.3871 | 0.9175 | 0.9248 | 0.9175 | 0.9207 |
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+ | 0.0323 | 4.0 | 788 | 0.3336 | 0.9112 | 0.9187 | 0.9112 | 0.9145 |
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+ | 0.0194 | 5.0 | 985 | 0.5212 | 0.9153 | 0.9193 | 0.9153 | 0.9171 |
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+ | 0.0211 | 6.0 | 1182 | 0.4201 | 0.9125 | 0.9167 | 0.9125 | 0.9145 |
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+ | 0.0074 | 7.0 | 1379 | 0.4826 | 0.9151 | 0.9141 | 0.9151 | 0.9146 |
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+ | 0.0014 | 8.0 | 1576 | 0.5316 | 0.9075 | 0.9190 | 0.9075 | 0.9124 |
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+ | 0.003 | 9.0 | 1773 | 0.9022 | 0.8623 | 0.9073 | 0.8623 | 0.8794 |
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+ | 0.0001 | 10.0 | 1970 | 0.8733 | 0.8678 | 0.9081 | 0.8678 | 0.8832 |
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  ### Framework versions
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