synergyai-jaeung
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README.md
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.
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- name: F1
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type: f1
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value: 0.
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- name: Recall
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type: recall
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value: 0.
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- name: Precision
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type: precision
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value: 0.
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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.
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- Accuracy: 0.
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- F1: 0.
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- Auc: 0.
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- Recall: 0.
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- Precision: 0.
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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:
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Auc | Recall | Precision |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:|:---------:|
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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: 0.9010416666666666
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- name: F1
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type: f1
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value: 0.473972602739726
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- name: Recall
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type: recall
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value: 0.9942528735632183
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- name: Precision
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type: precision
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value: 0.9057591623036649
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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: 0.6385
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- Accuracy: 0.9010
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- F1: 0.4740
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- Auc: 0.4971
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- Recall: 0.9943
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- Precision: 0.9058
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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: 15
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Auc | Recall | Precision |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:|:---------:|
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| 0.7218 | 1.0 | 55 | 0.3383 | 0.9062 | 0.4754 | 0.5 | 1.0 | 0.9062 |
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| 0.7218 | 2.0 | 110 | 0.3823 | 0.9062 | 0.4754 | 0.5 | 1.0 | 0.9062 |
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| 0.7218 | 3.0 | 165 | 0.3957 | 0.9062 | 0.4754 | 0.5 | 1.0 | 0.9062 |
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| 0.7218 | 4.0 | 220 | 0.4485 | 0.9062 | 0.4754 | 0.5 | 1.0 | 0.9062 |
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| 0.7218 | 5.0 | 275 | 0.4786 | 0.8958 | 0.4725 | 0.4943 | 0.9885 | 0.9053 |
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| 0.7218 | 6.0 | 330 | 0.5316 | 0.9010 | 0.4740 | 0.4971 | 0.9943 | 0.9058 |
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| 0.7218 | 7.0 | 385 | 0.5539 | 0.9010 | 0.4740 | 0.4971 | 0.9943 | 0.9058 |
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| 0.7218 | 8.0 | 440 | 0.5800 | 0.9010 | 0.4740 | 0.4971 | 0.9943 | 0.9058 |
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| 0.7218 | 9.0 | 495 | 0.5977 | 0.9010 | 0.4740 | 0.4971 | 0.9943 | 0.9058 |
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| 0.0987 | 10.0 | 550 | 0.6110 | 0.9010 | 0.4740 | 0.4971 | 0.9943 | 0.9058 |
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| 0.0987 | 11.0 | 605 | 0.6211 | 0.9010 | 0.4740 | 0.4971 | 0.9943 | 0.9058 |
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| 0.0987 | 12.0 | 660 | 0.6288 | 0.9010 | 0.4740 | 0.4971 | 0.9943 | 0.9058 |
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| 0.0987 | 13.0 | 715 | 0.6341 | 0.9010 | 0.4740 | 0.4971 | 0.9943 | 0.9058 |
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| 0.0987 | 14.0 | 770 | 0.6374 | 0.9010 | 0.4740 | 0.4971 | 0.9943 | 0.9058 |
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| 0.0987 | 15.0 | 825 | 0.6385 | 0.9010 | 0.4740 | 0.4971 | 0.9943 | 0.9058 |
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### Framework versions
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model.safetensors
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