synergyai-jaeung commited on
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Model save

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README.md CHANGED
@@ -5,9 +5,36 @@ tags:
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  - generated_from_trainer
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  datasets:
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  - imagefolder
 
 
 
 
 
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  model-index:
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  - name: vit-base-patch16-224-in21k_covid_19_ct_scans
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- results: []
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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
@@ -17,17 +44,12 @@ 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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- - eval_loss: 0.2255
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- - eval_accuracy: 0.9671
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- - eval_F1: 0.9163
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- - eval_AUC: 0.8704
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- - eval_Recall: 1.0
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- - eval_Precision: 0.9637
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- - eval_runtime: 4.1611
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- - eval_samples_per_second: 51.189
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- - eval_steps_per_second: 3.365
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- - epoch: 20.0
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- - step: 1080
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  ## Model description
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@@ -52,7 +74,25 @@ The following hyperparameters were used during training:
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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: 100
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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  - generated_from_trainer
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  datasets:
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  - imagefolder
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+ metrics:
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+ - accuracy
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+ - f1
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+ - recall
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+ - precision
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  model-index:
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  - name: vit-base-patch16-224-in21k_covid_19_ct_scans
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+ results:
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+ - task:
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+ name: Image Classification
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+ type: image-classification
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+ dataset:
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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: 0.9765258215962441
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+ - name: F1
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+ type: f1
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+ value: 0.9294374875770225
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+ - name: Recall
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+ type: recall
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+ value: 1.0
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+ - name: Precision
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+ type: precision
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+ value: 0.9744897959183674
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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: 0.1064
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+ - Accuracy: 0.9765
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+ - F1: 0.9294
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+ - Auc: 0.8864
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+ - Recall: 1.0
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+ - Precision: 0.9745
 
 
 
 
 
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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: 12
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Auc | Recall | Precision |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:|:---------:|
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+ | 0.6804 | 1.0 | 54 | 0.3821 | 0.8967 | 0.4728 | 0.5 | 1.0 | 0.8967 |
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+ | 0.6804 | 2.0 | 108 | 0.4134 | 0.8967 | 0.4728 | 0.5 | 1.0 | 0.8967 |
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+ | 0.6804 | 3.0 | 162 | 0.2708 | 0.9061 | 0.5585 | 0.5455 | 1.0 | 0.9052 |
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+ | 0.6804 | 4.0 | 216 | 0.2405 | 0.9437 | 0.7973 | 0.7273 | 1.0 | 0.9409 |
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+ | 0.6804 | 5.0 | 270 | 0.2193 | 0.9437 | 0.7973 | 0.7273 | 1.0 | 0.9409 |
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+ | 0.6804 | 6.0 | 324 | 0.1719 | 0.9484 | 0.8775 | 0.9310 | 0.9529 | 0.9891 |
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+ | 0.6804 | 7.0 | 378 | 0.0525 | 0.9859 | 0.9612 | 0.9519 | 0.9948 | 0.9896 |
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+ | 0.6804 | 8.0 | 432 | 0.0482 | 0.9906 | 0.9736 | 0.9545 | 1.0 | 0.9896 |
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+ | 0.6804 | 9.0 | 486 | 0.0907 | 0.9765 | 0.9294 | 0.8864 | 1.0 | 0.9745 |
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+ | 0.1258 | 10.0 | 540 | 0.1009 | 0.9765 | 0.9294 | 0.8864 | 1.0 | 0.9745 |
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+ | 0.1258 | 11.0 | 594 | 0.1051 | 0.9765 | 0.9294 | 0.8864 | 1.0 | 0.9745 |
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+ | 0.1258 | 12.0 | 648 | 0.1064 | 0.9765 | 0.9294 | 0.8864 | 1.0 | 0.9745 |
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+
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  ### Framework versions
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