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fold_5

Image Classification
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Safetensors
vit
Generated from Trainer
Eval Results (legacy)
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End of training Fold 5

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@@ -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: 0.9584775086505191
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  - name: Recall
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  type: recall
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- value: 0.96098499101133
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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: 1.1415
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- - Accuracy: 0.9585
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- - F1 Score: 0.9604
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- - Recall: 0.9610
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  ## Model description
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@@ -73,47 +73,36 @@ 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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- | 2.7679 | 1.0 | 19 | 2.7376 | 0.3356 | 0.2925 | 0.3120 |
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- | 2.6412 | 2.0 | 38 | 2.5528 | 0.4567 | 0.3907 | 0.4082 |
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- | 2.2891 | 3.0 | 57 | 2.3004 | 0.6125 | 0.5730 | 0.5668 |
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- | 1.9662 | 4.0 | 76 | 1.9746 | 0.7509 | 0.7537 | 0.7393 |
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- | 1.5390 | 5.0 | 95 | 1.6498 | 0.8374 | 0.8436 | 0.8325 |
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- | 1.3191 | 6.0 | 114 | 1.4345 | 0.8789 | 0.8884 | 0.8848 |
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- | 1.1787 | 7.0 | 133 | 1.3242 | 0.8962 | 0.9025 | 0.8958 |
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- | 1.1229 | 8.0 | 152 | 1.2923 | 0.8927 | 0.8969 | 0.8856 |
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- | 1.0605 | 9.0 | 171 | 1.2239 | 0.9239 | 0.9278 | 0.9253 |
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- | 1.0463 | 10.0 | 190 | 1.2173 | 0.9308 | 0.9339 | 0.9300 |
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- | 1.0295 | 11.0 | 209 | 1.2042 | 0.9412 | 0.9437 | 0.9426 |
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- | 1.0210 | 12.0 | 228 | 1.1941 | 0.9412 | 0.9437 | 0.9426 |
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- | 1.0174 | 13.0 | 247 | 1.1868 | 0.9446 | 0.9474 | 0.9464 |
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- | 1.0169 | 14.0 | 266 | 1.1932 | 0.9343 | 0.9366 | 0.9337 |
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- | 1.0026 | 15.0 | 285 | 1.2058 | 0.9377 | 0.9401 | 0.9362 |
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- | 1.0057 | 16.0 | 304 | 1.1721 | 0.9446 | 0.9467 | 0.9462 |
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- | 0.9959 | 17.0 | 323 | 1.1659 | 0.9412 | 0.9435 | 0.9425 |
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- | 0.9822 | 18.0 | 342 | 1.1760 | 0.9412 | 0.9437 | 0.9412 |
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- | 0.9793 | 19.0 | 361 | 1.1816 | 0.9412 | 0.9434 | 0.9399 |
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- | 0.9772 | 20.0 | 380 | 1.1641 | 0.9516 | 0.9530 | 0.9510 |
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- | 0.9745 | 21.0 | 399 | 1.1622 | 0.9516 | 0.9533 | 0.9522 |
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- | 0.9757 | 22.0 | 418 | 1.1569 | 0.9550 | 0.9573 | 0.9574 |
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- | 0.9639 | 23.0 | 437 | 1.1593 | 0.9446 | 0.9467 | 0.9462 |
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- | 0.9746 | 24.0 | 456 | 1.1542 | 0.9481 | 0.9505 | 0.9500 |
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- | 0.9691 | 25.0 | 475 | 1.1527 | 0.9550 | 0.9567 | 0.9573 |
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- | 0.9656 | 26.0 | 494 | 1.1619 | 0.9481 | 0.9501 | 0.9496 |
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- | 0.9629 | 27.0 | 513 | 1.1539 | 0.9516 | 0.9533 | 0.9534 |
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- | 0.9690 | 28.0 | 532 | 1.1523 | 0.9516 | 0.9539 | 0.9534 |
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- | 0.9660 | 29.0 | 551 | 1.1474 | 0.9550 | 0.9573 | 0.9573 |
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- | 0.9728 | 30.0 | 570 | 1.1424 | 0.9585 | 0.9603 | 0.9608 |
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- | 0.9644 | 31.0 | 589 | 1.1415 | 0.9585 | 0.9604 | 0.9610 |
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- | 0.9610 | 32.0 | 608 | 1.1462 | 0.9550 | 0.9563 | 0.9558 |
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- | 0.9623 | 33.0 | 627 | 1.1423 | 0.9550 | 0.9566 | 0.9571 |
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- | 0.9630 | 34.0 | 646 | 1.1416 | 0.9516 | 0.9536 | 0.9546 |
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- | 0.9664 | 35.0 | 665 | 1.1438 | 0.9550 | 0.9566 | 0.9571 |
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- | 0.9643 | 36.0 | 684 | 1.1454 | 0.9550 | 0.9566 | 0.9571 |
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- | 0.9670 | 37.0 | 703 | 1.1431 | 0.9550 | 0.9566 | 0.9571 |
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- | 0.9631 | 38.0 | 722 | 1.1490 | 0.9481 | 0.9503 | 0.9498 |
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- | 0.9589 | 39.0 | 741 | 1.1512 | 0.9481 | 0.9503 | 0.9498 |
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- | 0.9620 | 40.0 | 760 | 1.1427 | 0.9516 | 0.9535 | 0.9535 |
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- | 0.9552 | 41.0 | 779 | 1.1427 | 0.9516 | 0.9535 | 0.9535 |
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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: 0.9515570934256056
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  - name: Recall
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  type: recall
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+ value: 0.955763200802709
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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: 1.1556
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+ - Accuracy: 0.9516
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+ - F1 Score: 0.9547
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+ - Recall: 0.9558
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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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+ | 3.0831 | 1.0 | 19 | 2.9877 | 0.1834 | 0.1506 | 0.1750 |
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+ | 2.9160 | 2.0 | 38 | 2.7975 | 0.2734 | 0.2517 | 0.2583 |
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+ | 2.5816 | 3.0 | 57 | 2.5431 | 0.4671 | 0.4271 | 0.4325 |
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+ | 2.1999 | 4.0 | 76 | 2.2368 | 0.6263 | 0.6019 | 0.6120 |
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+ | 1.8110 | 5.0 | 95 | 1.9213 | 0.7301 | 0.7278 | 0.7223 |
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+ | 1.4822 | 6.0 | 114 | 1.6147 | 0.8304 | 0.8407 | 0.8357 |
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+ | 1.2515 | 7.0 | 133 | 1.4442 | 0.8581 | 0.8646 | 0.8541 |
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+ | 1.1471 | 8.0 | 152 | 1.3540 | 0.8789 | 0.8857 | 0.8784 |
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+ | 1.0819 | 9.0 | 171 | 1.2801 | 0.8962 | 0.9021 | 0.8978 |
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+ | 1.0613 | 10.0 | 190 | 1.2465 | 0.9100 | 0.9142 | 0.9124 |
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+ | 1.0395 | 11.0 | 209 | 1.2235 | 0.9343 | 0.9386 | 0.9387 |
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+ | 1.0240 | 12.0 | 228 | 1.2106 | 0.9377 | 0.9410 | 0.9397 |
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+ | 1.0257 | 13.0 | 247 | 1.1934 | 0.9308 | 0.9358 | 0.9361 |
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+ | 1.0162 | 14.0 | 266 | 1.1946 | 0.9377 | 0.9402 | 0.9384 |
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+ | 0.9940 | 15.0 | 285 | 1.1879 | 0.9377 | 0.9411 | 0.9397 |
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+ | 1.0097 | 16.0 | 304 | 1.1848 | 0.9412 | 0.9449 | 0.9423 |
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+ | 0.9937 | 17.0 | 323 | 1.1863 | 0.9377 | 0.9408 | 0.9384 |
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+ | 0.9864 | 18.0 | 342 | 1.1935 | 0.9446 | 0.9471 | 0.9446 |
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+ | 0.9830 | 19.0 | 361 | 1.1802 | 0.9446 | 0.9474 | 0.9459 |
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+ | 0.9825 | 20.0 | 380 | 1.1508 | 0.9446 | 0.9490 | 0.9485 |
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+ | 0.9733 | 21.0 | 399 | 1.1568 | 0.9343 | 0.9385 | 0.9385 |
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+ | 0.9778 | 22.0 | 418 | 1.1570 | 0.9412 | 0.9455 | 0.9460 |
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+ | 0.9657 | 23.0 | 437 | 1.1588 | 0.9446 | 0.9487 | 0.9497 |
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+ | 0.9771 | 24.0 | 456 | 1.1637 | 0.9446 | 0.9495 | 0.9510 |
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+ | 0.9647 | 25.0 | 475 | 1.1640 | 0.9481 | 0.9509 | 0.9509 |
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+ | 0.9649 | 26.0 | 494 | 1.1572 | 0.9516 | 0.9546 | 0.9546 |
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+ | 0.9686 | 27.0 | 513 | 1.1667 | 0.9377 | 0.9411 | 0.9397 |
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+ | 0.9681 | 28.0 | 532 | 1.1614 | 0.9377 | 0.9417 | 0.9423 |
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+ | 0.9642 | 29.0 | 551 | 1.1599 | 0.9412 | 0.9444 | 0.9434 |
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+ | 0.9677 | 30.0 | 570 | 1.1556 | 0.9516 | 0.9547 | 0.9558 |
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions