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vit_itri_2class_focalloss

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: 0.9123376623376623
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  - name: Precision
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  type: precision
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- value: 0.9075438235806461
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  - name: Recall
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  type: recall
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- value: 0.9123376623376623
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  - name: F1
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  type: f1
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- value: 0.9097163522535855
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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: 0.0178
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- - Accuracy: 0.9123
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- - Precision: 0.9075
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- - Recall: 0.9123
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- - F1: 0.9097
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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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- | 0.0029 | 1.0 | 759 | 0.0050 | 0.9214 | 0.9151 | 0.9214 | 0.9177 |
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- | 0.0007 | 2.0 | 1518 | 0.0076 | 0.9106 | 0.8981 | 0.9106 | 0.9027 |
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- | 0.0008 | 3.0 | 2277 | 0.0086 | 0.9131 | 0.9082 | 0.9131 | 0.9104 |
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- | 0.0005 | 4.0 | 3036 | 0.0091 | 0.8978 | 0.9153 | 0.8978 | 0.9050 |
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- | 0.0004 | 5.0 | 3795 | 0.0152 | 0.9082 | 0.9066 | 0.9082 | 0.9074 |
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- | 0.0002 | 6.0 | 4554 | 0.0125 | 0.9271 | 0.9207 | 0.9271 | 0.9230 |
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- | 0.0002 | 7.0 | 5313 | 0.0164 | 0.9095 | 0.9077 | 0.9095 | 0.9086 |
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- | 0.0001 | 8.0 | 6072 | 0.0173 | 0.9181 | 0.9088 | 0.9181 | 0.9122 |
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- | 0.0 | 9.0 | 6831 | 0.0182 | 0.9250 | 0.9161 | 0.9250 | 0.9188 |
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- | 0.0 | 10.0 | 7590 | 0.0178 | 0.9123 | 0.9075 | 0.9123 | 0.9097 |
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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: 0.9154860291223927
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  - name: Precision
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  type: precision
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+ value: 0.9063125299043086
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  - name: Recall
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  type: recall
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+ value: 0.9154860291223927
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  - name: F1
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  type: f1
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+ value: 0.909840882104917
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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: 0.6180
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+ - Accuracy: 0.9155
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+ - Precision: 0.9063
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+ - Recall: 0.9155
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+ - F1: 0.9098
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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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+ | 0.1198 | 1.0 | 759 | 0.2930 | 0.9360 | 0.9383 | 0.9360 | 0.9204 |
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+ | 0.0457 | 2.0 | 1518 | 0.2589 | 0.9342 | 0.9266 | 0.9342 | 0.9275 |
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+ | 0.027 | 3.0 | 2277 | 0.3913 | 0.9164 | 0.9137 | 0.9164 | 0.9150 |
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+ | 0.0187 | 4.0 | 3036 | 0.4602 | 0.9162 | 0.9012 | 0.9162 | 0.9049 |
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+ | 0.0129 | 5.0 | 3795 | 0.4067 | 0.9202 | 0.9236 | 0.9202 | 0.9218 |
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+ | 0.0079 | 6.0 | 4554 | 0.5165 | 0.9327 | 0.9246 | 0.9327 | 0.9256 |
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+ | 0.0057 | 7.0 | 5313 | 0.8537 | 0.8791 | 0.8902 | 0.8791 | 0.8842 |
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+ | 0.0039 | 8.0 | 6072 | 0.7689 | 0.9148 | 0.8972 | 0.9148 | 0.9005 |
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+ | 0.0023 | 9.0 | 6831 | 0.6286 | 0.9140 | 0.9040 | 0.9140 | 0.9078 |
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+ | 0.0008 | 10.0 | 7590 | 0.6180 | 0.9155 | 0.9063 | 0.9155 | 0.9098 |
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  ### Framework versions
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