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BTX24
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levit_128.fb_dist_in1k-finetuned-stroke-binary

Image Classification
Transformers
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Safetensors
timm_wrapper
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  1. README.md +17 -13
README.md CHANGED
@@ -22,10 +22,10 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [timm/levit_128.fb_dist_in1k](https://huggingface.co/timm/levit_128.fb_dist_in1k) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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  - Loss: nan
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- - Accuracy: 0.6974
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- - F1: 0.7014
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- - Precision: 0.7298
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- - Recall: 0.6974
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  ## Model description
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@@ -60,15 +60,19 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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  |:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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- | 0.695 | 0.6202 | 100 | nan | 0.5364 | 0.5421 | 0.5541 | 0.5364 |
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- | 0.6804 | 1.2357 | 200 | nan | 0.5798 | 0.5833 | 0.6278 | 0.5798 |
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- | 0.6821 | 1.8558 | 300 | nan | 0.6232 | 0.6280 | 0.6413 | 0.6232 |
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- | 0.6726 | 2.4713 | 400 | nan | 0.6671 | 0.6711 | 0.6829 | 0.6671 |
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- | 0.6546 | 3.0868 | 500 | nan | 0.7024 | 0.7021 | 0.7018 | 0.7024 |
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- | 0.647 | 3.7070 | 600 | nan | 0.7065 | 0.7093 | 0.7159 | 0.7065 |
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- | 0.6263 | 4.3225 | 700 | nan | 0.6956 | 0.6991 | 0.7096 | 0.6956 |
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- | 0.6112 | 4.9426 | 800 | nan | 0.6766 | 0.6807 | 0.7123 | 0.6766 |
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- | 0.5704 | 5.5581 | 900 | nan | 0.6974 | 0.7014 | 0.7298 | 0.6974 |
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [timm/levit_128.fb_dist_in1k](https://huggingface.co/timm/levit_128.fb_dist_in1k) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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  - Loss: nan
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+ - Accuracy: 0.8598
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+ - F1: 0.8577
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+ - Precision: 0.8602
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+ - Recall: 0.8598
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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  |:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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+ | 0.7002 | 0.6202 | 100 | nan | 0.5690 | 0.5387 | 0.5349 | 0.5690 |
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+ | 0.681 | 1.2357 | 200 | nan | 0.5834 | 0.5331 | 0.5372 | 0.5834 |
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+ | 0.6874 | 1.8558 | 300 | nan | 0.6002 | 0.5596 | 0.5665 | 0.6002 |
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+ | 0.6774 | 2.4713 | 400 | nan | 0.6124 | 0.5811 | 0.5867 | 0.6124 |
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+ | 0.6533 | 3.0868 | 500 | nan | 0.6852 | 0.6694 | 0.6767 | 0.6852 |
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+ | 0.6368 | 3.7070 | 600 | nan | 0.7205 | 0.7153 | 0.7153 | 0.7205 |
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+ | 0.6196 | 4.3225 | 700 | nan | 0.7603 | 0.7471 | 0.7650 | 0.7603 |
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+ | 0.5663 | 4.9426 | 800 | nan | 0.7883 | 0.7843 | 0.7864 | 0.7883 |
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+ | 0.5196 | 5.5581 | 900 | nan | 0.8078 | 0.7972 | 0.8206 | 0.8078 |
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+ | 0.4704 | 6.1736 | 1000 | nan | 0.8363 | 0.8317 | 0.8396 | 0.8363 |
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+ | 0.4715 | 6.7938 | 1100 | nan | 0.8349 | 0.8292 | 0.8409 | 0.8349 |
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+ | 0.452 | 7.4093 | 1200 | nan | 0.8503 | 0.8479 | 0.8505 | 0.8503 |
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+ | 0.4538 | 8.0248 | 1300 | nan | 0.8598 | 0.8577 | 0.8602 | 0.8598 |
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  ### Framework versions