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fold_4

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End of training Fold 4

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  1. README.md +44 -40
  2. model.safetensors +1 -1
README.md CHANGED
@@ -24,10 +24,10 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.9619377162629758
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  - name: Recall
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  type: recall
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- value: 0.9658753615233592
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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
@@ -37,10 +37,10 @@ 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](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.1305
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- - Accuracy: 0.9619
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- - F1 Score: 0.9652
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- - Recall: 0.9659
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  ## Model description
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@@ -73,40 +73,44 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | Recall |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:------:|
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- | 3.1319 | 1.0 | 19 | 3.1085 | 0.1453 | 0.1309 | 0.1601 |
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- | 2.9606 | 2.0 | 38 | 2.8599 | 0.2526 | 0.2480 | 0.2557 |
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- | 2.6010 | 3.0 | 57 | 2.5562 | 0.4775 | 0.4447 | 0.4458 |
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- | 2.2535 | 4.0 | 76 | 2.2286 | 0.6125 | 0.6151 | 0.6017 |
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- | 1.8319 | 5.0 | 95 | 1.8960 | 0.7405 | 0.7493 | 0.7328 |
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- | 1.5134 | 6.0 | 114 | 1.5738 | 0.8547 | 0.8679 | 0.8626 |
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- | 1.3165 | 7.0 | 133 | 1.3901 | 0.8962 | 0.9047 | 0.8956 |
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- | 1.1793 | 8.0 | 152 | 1.2636 | 0.9446 | 0.9499 | 0.9499 |
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- | 1.1215 | 9.0 | 171 | 1.2150 | 0.9377 | 0.9435 | 0.9413 |
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- | 1.0953 | 10.0 | 190 | 1.1884 | 0.9481 | 0.9530 | 0.9538 |
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- | 1.0713 | 11.0 | 209 | 1.1682 | 0.9516 | 0.9555 | 0.9563 |
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- | 1.0654 | 12.0 | 228 | 1.1829 | 0.9446 | 0.9497 | 0.9524 |
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- | 1.0202 | 13.0 | 247 | 1.1502 | 0.9585 | 0.9615 | 0.9610 |
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- | 1.0255 | 14.0 | 266 | 1.1526 | 0.9550 | 0.9589 | 0.9573 |
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- | 1.0126 | 15.0 | 285 | 1.1521 | 0.9619 | 0.9651 | 0.9646 |
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- | 1.0030 | 16.0 | 304 | 1.1346 | 0.9585 | 0.9621 | 0.9621 |
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- | 0.9920 | 17.0 | 323 | 1.1365 | 0.9550 | 0.9589 | 0.9573 |
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- | 0.9882 | 18.0 | 342 | 1.1356 | 0.9585 | 0.9621 | 0.9621 |
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- | 0.9950 | 19.0 | 361 | 1.1334 | 0.9585 | 0.9621 | 0.9621 |
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- | 0.9819 | 20.0 | 380 | 1.1356 | 0.9585 | 0.9621 | 0.9621 |
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- | 0.9801 | 21.0 | 399 | 1.1312 | 0.9516 | 0.9545 | 0.9547 |
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- | 0.9718 | 22.0 | 418 | 1.1346 | 0.9516 | 0.9545 | 0.9547 |
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- | 0.9679 | 23.0 | 437 | 1.1564 | 0.9481 | 0.9534 | 0.9572 |
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- | 0.9785 | 24.0 | 456 | 1.1227 | 0.9516 | 0.9545 | 0.9547 |
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- | 0.9702 | 25.0 | 475 | 1.1275 | 0.9550 | 0.9589 | 0.9584 |
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- | 0.9742 | 26.0 | 494 | 1.1230 | 0.9585 | 0.9620 | 0.9609 |
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- | 0.9732 | 27.0 | 513 | 1.1303 | 0.9516 | 0.9554 | 0.9548 |
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- | 0.9651 | 28.0 | 532 | 1.1305 | 0.9619 | 0.9652 | 0.9659 |
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- | 0.9683 | 29.0 | 551 | 1.1283 | 0.9585 | 0.9620 | 0.9609 |
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- | 0.9692 | 30.0 | 570 | 1.1373 | 0.9516 | 0.9554 | 0.9548 |
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- | 0.9642 | 31.0 | 589 | 1.1312 | 0.9550 | 0.9589 | 0.9584 |
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- | 0.9642 | 32.0 | 608 | 1.1352 | 0.9550 | 0.9589 | 0.9584 |
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- | 0.9649 | 33.0 | 627 | 1.1363 | 0.9516 | 0.9554 | 0.9548 |
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- | 0.9621 | 34.0 | 646 | 1.1327 | 0.9550 | 0.9589 | 0.9584 |
 
 
 
 
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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: 0.9653979238754326
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  - name: Recall
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  type: recall
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+ value: 0.9683506090481117
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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](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.1328
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+ - Accuracy: 0.9654
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+ - F1 Score: 0.9688
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+ - Recall: 0.9684
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | Recall |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:------:|
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+ | 2.7279 | 1.0 | 19 | 2.6961 | 0.3287 | 0.3257 | 0.3264 |
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+ | 2.6497 | 2.0 | 38 | 2.5197 | 0.4775 | 0.4752 | 0.4600 |
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+ | 2.3856 | 3.0 | 57 | 2.2554 | 0.6574 | 0.6632 | 0.6472 |
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+ | 2.0467 | 4.0 | 76 | 1.9435 | 0.8062 | 0.8186 | 0.8137 |
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+ | 1.6696 | 5.0 | 95 | 1.6132 | 0.8720 | 0.8800 | 0.8750 |
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+ | 1.3967 | 6.0 | 114 | 1.3746 | 0.9170 | 0.9249 | 0.9244 |
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+ | 1.2758 | 7.0 | 133 | 1.2437 | 0.9412 | 0.9473 | 0.9422 |
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+ | 1.1517 | 8.0 | 152 | 1.1767 | 0.9550 | 0.9593 | 0.9559 |
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+ | 1.1069 | 9.0 | 171 | 1.1511 | 0.9516 | 0.9556 | 0.9512 |
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+ | 1.0847 | 10.0 | 190 | 1.1386 | 0.9550 | 0.9589 | 0.9584 |
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+ | 1.0566 | 11.0 | 209 | 1.1328 | 0.9654 | 0.9688 | 0.9684 |
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+ | 1.0535 | 12.0 | 228 | 1.1346 | 0.9550 | 0.9597 | 0.9609 |
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+ | 1.0154 | 13.0 | 247 | 1.1250 | 0.9550 | 0.9595 | 0.9595 |
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+ | 1.0196 | 14.0 | 266 | 1.1252 | 0.9585 | 0.9625 | 0.9609 |
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+ | 1.0003 | 15.0 | 285 | 1.1256 | 0.9481 | 0.9529 | 0.9524 |
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+ | 0.9951 | 16.0 | 304 | 1.1191 | 0.9619 | 0.9653 | 0.9659 |
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+ | 0.9877 | 17.0 | 323 | 1.1133 | 0.9619 | 0.9651 | 0.9646 |
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+ | 0.9841 | 18.0 | 342 | 1.1038 | 0.9619 | 0.9651 | 0.9646 |
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+ | 0.9887 | 19.0 | 361 | 1.1080 | 0.9619 | 0.9650 | 0.9635 |
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+ | 0.9794 | 20.0 | 380 | 1.1105 | 0.9619 | 0.9651 | 0.9646 |
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+ | 0.9786 | 21.0 | 399 | 1.1086 | 0.9654 | 0.9681 | 0.9671 |
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+ | 0.9719 | 22.0 | 418 | 1.1084 | 0.9550 | 0.9591 | 0.9597 |
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+ | 0.9723 | 23.0 | 437 | 1.1242 | 0.9516 | 0.9562 | 0.9584 |
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+ | 0.9813 | 24.0 | 456 | 1.0986 | 0.9654 | 0.9681 | 0.9671 |
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+ | 0.9682 | 25.0 | 475 | 1.0959 | 0.9654 | 0.9681 | 0.9671 |
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+ | 0.9750 | 26.0 | 494 | 1.0990 | 0.9654 | 0.9681 | 0.9671 |
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+ | 0.9716 | 27.0 | 513 | 1.0995 | 0.9619 | 0.9651 | 0.9646 |
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+ | 0.9701 | 28.0 | 532 | 1.0949 | 0.9654 | 0.9681 | 0.9671 |
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+ | 0.9694 | 29.0 | 551 | 1.0997 | 0.9654 | 0.9681 | 0.9671 |
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+ | 0.9637 | 30.0 | 570 | 1.1002 | 0.9654 | 0.9681 | 0.9671 |
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+ | 0.9647 | 31.0 | 589 | 1.0992 | 0.9654 | 0.9681 | 0.9671 |
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+ | 0.9658 | 32.0 | 608 | 1.1084 | 0.9619 | 0.9650 | 0.9635 |
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+ | 0.9686 | 33.0 | 627 | 1.1156 | 0.9619 | 0.9652 | 0.9659 |
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+ | 0.9632 | 34.0 | 646 | 1.1068 | 0.9619 | 0.9650 | 0.9634 |
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+ | 0.9630 | 35.0 | 665 | 1.1040 | 0.9619 | 0.9650 | 0.9634 |
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+ | 0.9680 | 36.0 | 684 | 1.1131 | 0.9619 | 0.9650 | 0.9634 |
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+ | 0.9579 | 37.0 | 703 | 1.1063 | 0.9619 | 0.9650 | 0.9634 |
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+ | 0.9615 | 38.0 | 722 | 1.1063 | 0.9619 | 0.9650 | 0.9634 |
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  ### Framework versions
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