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convnext-tiny-224_album_vitVMMRdb_make_model_album_pred

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convnext
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@@ -19,11 +19,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.4384
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- - Accuracy: 0.8814
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- - Precision: 0.8793
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- - Recall: 0.8814
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- - F1: 0.8772
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  ## Model description
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@@ -51,37 +51,27 @@ 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: 25
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
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- | 4.8445 | 1.0 | 944 | 4.7488 | 0.0919 | 0.0214 | 0.0919 | 0.0266 |
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- | 3.8243 | 2.0 | 1888 | 3.6914 | 0.2379 | 0.1520 | 0.2379 | 0.1447 |
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- | 2.8783 | 3.0 | 2832 | 2.7011 | 0.4105 | 0.3433 | 0.4105 | 0.3235 |
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- | 2.1348 | 4.0 | 3776 | 1.9752 | 0.5652 | 0.5279 | 0.5652 | 0.5069 |
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- | 1.6456 | 5.0 | 4720 | 1.5225 | 0.6529 | 0.6274 | 0.6529 | 0.6134 |
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- | 1.3835 | 6.0 | 5664 | 1.2167 | 0.7106 | 0.6996 | 0.7106 | 0.6845 |
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- | 1.1258 | 7.0 | 6608 | 1.0067 | 0.7491 | 0.7394 | 0.7491 | 0.7272 |
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- | 1.0181 | 8.0 | 7552 | 0.8722 | 0.7819 | 0.7755 | 0.7819 | 0.7678 |
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- | 0.7829 | 9.0 | 8496 | 0.7752 | 0.8018 | 0.7987 | 0.8018 | 0.7899 |
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- | 0.7503 | 10.0 | 9440 | 0.6983 | 0.8202 | 0.8189 | 0.8202 | 0.8121 |
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- | 0.6534 | 11.0 | 10384 | 0.6392 | 0.8301 | 0.8280 | 0.8301 | 0.8220 |
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- | 0.6108 | 12.0 | 11328 | 0.5941 | 0.8422 | 0.8384 | 0.8422 | 0.8343 |
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- | 0.5087 | 13.0 | 12272 | 0.5659 | 0.8487 | 0.8462 | 0.8487 | 0.8416 |
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- | 0.528 | 14.0 | 13216 | 0.5379 | 0.8554 | 0.8536 | 0.8554 | 0.8495 |
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- | 0.4489 | 15.0 | 14160 | 0.5189 | 0.8589 | 0.8566 | 0.8589 | 0.8528 |
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- | 0.4252 | 16.0 | 15104 | 0.5072 | 0.8626 | 0.8610 | 0.8626 | 0.8579 |
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- | 0.4239 | 17.0 | 16048 | 0.4857 | 0.8686 | 0.8678 | 0.8686 | 0.8645 |
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- | 0.3951 | 18.0 | 16992 | 0.4796 | 0.8695 | 0.8675 | 0.8695 | 0.8645 |
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- | 0.3679 | 19.0 | 17936 | 0.4685 | 0.8739 | 0.8724 | 0.8739 | 0.8695 |
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- | 0.3694 | 20.0 | 18880 | 0.4604 | 0.8751 | 0.8720 | 0.8751 | 0.8697 |
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- | 0.3435 | 21.0 | 19824 | 0.4555 | 0.8777 | 0.8755 | 0.8777 | 0.8739 |
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- | 0.3204 | 22.0 | 20768 | 0.4479 | 0.8783 | 0.8763 | 0.8783 | 0.8744 |
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- | 0.3475 | 23.0 | 21712 | 0.4433 | 0.8794 | 0.8773 | 0.8794 | 0.8753 |
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- | 0.338 | 24.0 | 22656 | 0.4408 | 0.8809 | 0.8785 | 0.8809 | 0.8767 |
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- | 0.3437 | 25.0 | 23600 | 0.4384 | 0.8814 | 0.8793 | 0.8814 | 0.8772 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.7064
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+ - Accuracy: 0.8177
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+ - Precision: 0.8089
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+ - Recall: 0.8177
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+ - F1: 0.8067
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  ## Model description
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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: 15
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
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+ | 4.6105 | 1.0 | 839 | 4.5248 | 0.1097 | 0.0579 | 0.1097 | 0.0403 |
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+ | 3.4711 | 2.0 | 1678 | 3.3162 | 0.3000 | 0.2302 | 0.3000 | 0.2097 |
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+ | 2.6202 | 3.0 | 2517 | 2.4445 | 0.4709 | 0.4120 | 0.4709 | 0.3939 |
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+ | 2.0614 | 4.0 | 3356 | 1.8839 | 0.5742 | 0.5389 | 0.5742 | 0.5168 |
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+ | 1.7026 | 5.0 | 4195 | 1.5247 | 0.6436 | 0.6180 | 0.6436 | 0.6013 |
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+ | 1.4288 | 6.0 | 5034 | 1.2768 | 0.6979 | 0.6810 | 0.6979 | 0.6686 |
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+ | 1.1953 | 7.0 | 5873 | 1.0960 | 0.7323 | 0.7218 | 0.7323 | 0.7077 |
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+ | 1.058 | 8.0 | 6712 | 0.9828 | 0.7548 | 0.7441 | 0.7548 | 0.7350 |
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+ | 0.9691 | 9.0 | 7551 | 0.9018 | 0.7718 | 0.7616 | 0.7718 | 0.7536 |
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+ | 0.8757 | 10.0 | 8390 | 0.8380 | 0.7893 | 0.7806 | 0.7893 | 0.7756 |
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+ | 0.8446 | 11.0 | 9229 | 0.7905 | 0.7982 | 0.7913 | 0.7982 | 0.7859 |
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+ | 0.7711 | 12.0 | 10068 | 0.7524 | 0.8069 | 0.7995 | 0.8069 | 0.7950 |
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+ | 0.7689 | 13.0 | 10907 | 0.7283 | 0.8123 | 0.8043 | 0.8123 | 0.8009 |
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+ | 0.6919 | 14.0 | 11746 | 0.7133 | 0.8148 | 0.8061 | 0.8148 | 0.8036 |
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+ | 0.694 | 15.0 | 12585 | 0.7064 | 0.8177 | 0.8089 | 0.8177 | 0.8067 |
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions