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End of training

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  1. README.md +15 -16
  2. model.safetensors +1 -1
README.md CHANGED
@@ -23,16 +23,16 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.9253901789113057
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  - name: F1
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  type: f1
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- value: 0.9052377115229654
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  - name: Precision
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  type: precision
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- value: 0.9233171693926194
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  - name: Recall
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  type: recall
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- value: 0.8878526831581444
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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
@@ -42,11 +42,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model was trained from scratch on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1768
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- - Accuracy: 0.9254
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- - F1: 0.9052
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- - Precision: 0.9233
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- - Recall: 0.8879
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  ## Model description
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@@ -65,7 +65,7 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 1e-05
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  - train_batch_size: 16
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  - eval_batch_size: 16
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  - seed: 42
@@ -74,17 +74,16 @@ The following hyperparameters were used during training:
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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: 0.1
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- - num_epochs: 5
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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- | 0.3786 | 1.0 | 1970 | 0.3166 | 0.8590 | 0.8184 | 0.8469 | 0.7917 |
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- | 0.2976 | 2.0 | 3941 | 0.2426 | 0.8952 | 0.8621 | 0.9138 | 0.8159 |
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- | 0.2525 | 3.0 | 5911 | 0.2015 | 0.9144 | 0.8908 | 0.9132 | 0.8694 |
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- | 0.2319 | 4.0 | 7882 | 0.1859 | 0.9216 | 0.9026 | 0.8996 | 0.9056 |
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- | 0.206 | 5.0 | 9850 | 0.1768 | 0.9254 | 0.9052 | 0.9233 | 0.8879 |
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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: 0.6404109589041096
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  - name: F1
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  type: f1
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+ value: 0.5016949152542373
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  - name: Precision
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  type: precision
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+ value: 0.6290224650880388
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  - name: Recall
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  type: recall
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+ value: 0.41723721304873135
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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 was trained from scratch on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.7208
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+ - Accuracy: 0.6404
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+ - F1: 0.5017
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+ - Precision: 0.6290
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+ - Recall: 0.4172
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 1e-06
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  - train_batch_size: 16
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  - eval_batch_size: 16
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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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  - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 4
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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+ | 0.4992 | 1.0 | 2015 | 0.7072 | 0.6189 | 0.4517 | 0.6009 | 0.3619 |
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+ | 0.4581 | 2.0 | 4031 | 0.7145 | 0.6383 | 0.4787 | 0.6387 | 0.3828 |
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+ | 0.4229 | 3.0 | 6047 | 0.7146 | 0.6434 | 0.5077 | 0.6329 | 0.4238 |
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+ | 0.4096 | 4.0 | 8060 | 0.7208 | 0.6404 | 0.5017 | 0.6290 | 0.4172 |
 
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  ### Framework versions
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