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Augusto777
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vit-base-patch16-224-RU4-40

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  1. README.md +45 -45
  2. model.safetensors +1 -1
  3. runs/May24_07-19-00_DESKTOP-SKBE9FB/events.out.tfevents.1716556742.DESKTOP-SKBE9FB.7992.0 +2 -2
README.md CHANGED
@@ -22,7 +22,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.7833333333333333
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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
@@ -32,8 +32,8 @@ 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: 49869186446092573277078519432609792.0000
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- - Accuracy: 0.7833
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  ## Model description
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@@ -65,48 +65,48 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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- |:----------------------------------------:|:-----:|:----:|:----------------------------------------:|:--------:|
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- | 62336480581735638025587599492513792.0000 | 0.99 | 19 | 49869186446092573277078519432609792.0000 | 0.4667 |
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- | 58440439651504818626990770153848832.0000 | 1.97 | 38 | 49869186446092573277078519432609792.0000 | 0.6 |
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- | 50648389482308178156834519410278400.0000 | 2.96 | 57 | 49869186446092573277078519432609792.0000 | 0.7333 |
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- | 56102820639337698928052607882625024.0000 | 4.0 | 77 | 49869186446092573277078519432609792.0000 | 0.75 |
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- | 62336476620327508623021905073405952.0000 | 4.99 | 96 | 49869186446092573277078519432609792.0000 | 0.7667 |
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- | 56882041501889867672610159036727296.0000 | 5.97 | 115 | 49869186446092573277078519432609792.0000 | 0.75 |
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- | 51817195026983603992051887699394560.0000 | 6.96 | 134 | 49869186446092573277078519432609792.0000 | 0.75 |
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- | 52986004533067168453206987262394368.0000 | 8.0 | 154 | 49869186446092573277078519432609792.0000 | 0.6667 |
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- | 55713231995806303211455059473727488.0000 | 8.99 | 173 | 49869186446092573277078519432609792.0000 | 0.6833 |
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- | 54934019056070393272028897157840896.0000 | 9.97 | 192 | 49869186446092573277078519432609792.0000 | 0.7833 |
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- | 51427598460635967917067024161832960.0000 | 10.96 | 211 | 49869186446092573277078519432609792.0000 | 0.7167 |
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- | 51817198988391733394617582118502400.0000 | 12.0 | 231 | 49869186446092573277078519432609792.0000 | 0.7 |
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- | 57661238595993278448517617385734144.0000 | 12.99 | 250 | 49869186446092573277078519432609792.0000 | 0.7333 |
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- | 58050851007973422910393221744951296.0000 | 13.97 | 269 | 49869186446092573277078519432609792.0000 | 0.75 |
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- | 56882045463297987851803816601059328.0000 | 14.96 | 288 | 49869186446092573277078519432609792.0000 | 0.7333 |
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- | 56102844407786447673330663832944640.0000 | 16.0 | 308 | 49869186446092573277078519432609792.0000 | 0.75 |
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- | 56492437012725972792493906660950016.0000 | 16.99 | 327 | 49869186446092573277078519432609792.0000 | 0.7833 |
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- | 51817195026983603992051887699394560.0000 | 17.97 | 346 | 49869186446092573277078519432609792.0000 | 0.7667 |
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- | 48310774431549178637090014703386624.0000 | 18.96 | 365 | 49869186446092573277078519432609792.0000 | 0.7667 |
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- | 58440451535729188387943779701620736.0000 | 20.0 | 385 | 49869186446092573277078519432609792.0000 | 0.7667 |
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- | 51037990010063943634385077366947840.0000 | 20.99 | 404 | 49869186446092573277078519432609792.0000 | 0.7667 |
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- | 52206811400371877856493186477719552.0000 | 21.97 | 423 | 49869186446092573277078519432609792.0000 | 0.7333 |
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- | 54934023017478513451222554722172928.0000 | 22.96 | 442 | 49869186446092573277078519432609792.0000 | 0.75 |
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- | 58830048102076833686300680093958144.0000 | 24.0 | 462 | 49869186446092573277078519432609792.0000 | 0.75 |
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- | 52986004533067168453206987262394368.0000 | 24.99 | 481 | 49869186446092573277078519432609792.0000 | 0.7667 |
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- | 54544418528314618571106302346395648.0000 | 25.97 | 500 | 49869186446092573277078519432609792.0000 | 0.7667 |
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- | 51644037916400559103047539831603200.0000 | 26.96 | 519 | 49869186446092573277078519432609792.0000 | 0.75 |
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- | 56102840446378327494137006268612608.0000 | 28.0 | 539 | 49869186446092573277078519432609792.0000 | 0.7667 |
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- | 59998853646752268744890085237850112.0000 | 28.99 | 558 | 49869186446092573277078519432609792.0000 | 0.7833 |
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- | 51427598460635967917067024161832960.0000 | 29.97 | 577 | 49869186446092573277078519432609792.0000 | 0.7833 |
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- | 53375620906455442317648286040719360.0000 | 30.96 | 596 | 49869186446092573277078519432609792.0000 | 0.7667 |
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- | 58830044140668713507107022529626112.0000 | 32.0 | 616 | 49869186446092573277078519432609792.0000 | 0.75 |
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- | 52596404005311393752284392450949120.0000 | 32.99 | 635 | 49869186446092573277078519432609792.0000 | 0.7333 |
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- | 57661234634585149045951922966626304.0000 | 33.97 | 654 | 49869186446092573277078519432609792.0000 | 0.7833 |
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- | 50648393443716298336028176974610432.0000 | 34.96 | 673 | 49869186446092573277078519432609792.0000 | 0.7667 |
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- | 56882045463297987851803816601059328.0000 | 36.0 | 693 | 49869186446092573277078519432609792.0000 | 0.7667 |
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- | 55713231995806303211455059473727488.0000 | 36.99 | 712 | 49869186446092573277078519432609792.0000 | 0.7833 |
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- | 51427594499227838514501329742725120.0000 | 37.97 | 731 | 49869186446092573277078519432609792.0000 | 0.7833 |
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- | 52596407966719523154850086870056960.0000 | 38.96 | 750 | 49869186446092573277078519432609792.0000 | 0.7833 |
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- | 53505475864816321566964104575320064.0000 | 39.48 | 760 | 49869186446092573277078519432609792.0000 | 0.7833 |
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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: 0.8166666666666667
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  ---
27
 
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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: 0.9448
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+ - Accuracy: 0.8167
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  ## Model description
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 1.3822 | 0.99 | 19 | 1.3130 | 0.4833 |
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+ | 1.2724 | 1.97 | 38 | 1.0987 | 0.6 |
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+ | 0.9711 | 2.96 | 57 | 0.8624 | 0.6667 |
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+ | 0.6349 | 4.0 | 77 | 0.7397 | 0.7333 |
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+ | 0.4068 | 4.99 | 96 | 0.6979 | 0.75 |
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+ | 0.2877 | 5.97 | 115 | 0.6270 | 0.7833 |
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+ | 0.2217 | 6.96 | 134 | 0.6467 | 0.8333 |
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+ | 0.195 | 8.0 | 154 | 0.6858 | 0.7833 |
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+ | 0.1392 | 8.99 | 173 | 0.6505 | 0.8167 |
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+ | 0.1534 | 9.97 | 192 | 0.6320 | 0.8167 |
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+ | 0.1136 | 10.96 | 211 | 0.8346 | 0.7833 |
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+ | 0.1025 | 12.0 | 231 | 0.6810 | 0.8 |
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+ | 0.0894 | 12.99 | 250 | 0.8258 | 0.7667 |
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+ | 0.1308 | 13.97 | 269 | 0.9456 | 0.75 |
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+ | 0.0836 | 14.96 | 288 | 0.9084 | 0.8 |
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+ | 0.0813 | 16.0 | 308 | 0.8688 | 0.8167 |
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+ | 0.1017 | 16.99 | 327 | 0.8609 | 0.8 |
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+ | 0.076 | 17.97 | 346 | 0.9015 | 0.8 |
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+ | 0.0726 | 18.96 | 365 | 0.9918 | 0.7833 |
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+ | 0.0549 | 20.0 | 385 | 0.9064 | 0.8 |
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+ | 0.0676 | 20.99 | 404 | 0.8819 | 0.75 |
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+ | 0.0717 | 21.97 | 423 | 0.8607 | 0.8167 |
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+ | 0.0547 | 22.96 | 442 | 0.8859 | 0.8 |
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+ | 0.0466 | 24.0 | 462 | 0.9328 | 0.8167 |
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+ | 0.0715 | 24.99 | 481 | 1.0178 | 0.7667 |
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+ | 0.0446 | 25.97 | 500 | 1.0094 | 0.7667 |
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+ | 0.0468 | 26.96 | 519 | 0.9175 | 0.8167 |
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+ | 0.0458 | 28.0 | 539 | 0.8580 | 0.8 |
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+ | 0.0392 | 28.99 | 558 | 1.0589 | 0.7833 |
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+ | 0.0469 | 29.97 | 577 | 1.0905 | 0.8 |
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+ | 0.0425 | 30.96 | 596 | 1.0078 | 0.7833 |
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+ | 0.0464 | 32.0 | 616 | 1.0206 | 0.7833 |
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+ | 0.0336 | 32.99 | 635 | 0.9653 | 0.8167 |
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+ | 0.0302 | 33.97 | 654 | 0.9574 | 0.8 |
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+ | 0.0353 | 34.96 | 673 | 0.9621 | 0.8167 |
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+ | 0.0344 | 36.0 | 693 | 0.9792 | 0.8167 |
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+ | 0.0195 | 36.99 | 712 | 0.9459 | 0.8167 |
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+ | 0.031 | 37.97 | 731 | 0.9488 | 0.8167 |
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+ | 0.0224 | 38.96 | 750 | 0.9440 | 0.8167 |
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+ | 0.0309 | 39.48 | 760 | 0.9448 | 0.8167 |
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  ### Framework versions
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