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# kematangan-pisang-vit-h-14-100eph-224-telyu

This model is a fine-tuned version of [google/vit-huge-patch14-224-in21k](https://huggingface.co/google/vit-huge-patch14-224-in21k) on the dataset kematangan pisang primer.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Recall | Specificity | Precision | Npv    | Accuracy | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-----------:|:---------:|:------:|:--------:|:------:|
| No log        | 1.0   | 187   | 0.3757          | 0.8980 | 0.9768      | 0.9387    | 0.9798 | 0.9307   | 0.9066 |
| No log        | 2.0   | 374   | 0.2434          | 0.9469 | 0.9854      | 0.9464    | 0.9851 | 0.9547   | 0.9464 |
| 0.3226        | 3.0   | 561   | 0.1817          | 0.9616 | 0.9899      | 0.9593    | 0.9896 | 0.968    | 0.9604 |
| 0.3226        | 4.0   | 748   | 0.1778          | 0.9436 | 0.9862      | 0.9498    | 0.9865 | 0.9573   | 0.9459 |
| 0.3226        | 5.0   | 935   | 0.1710          | 0.9395 | 0.9842      | 0.9490    | 0.9847 | 0.952    | 0.9428 |
| 0.1007        | 6.0   | 1122  | 0.1100          | 0.9658 | 0.9907      | 0.9636    | 0.9904 | 0.9707   | 0.9646 |
| 0.1007        | 7.0   | 1309  | 0.1017          | 0.9689 | 0.9918      | 0.9640    | 0.9914 | 0.9733   | 0.9661 |
| 0.1007        | 8.0   | 1496  | 0.0911          | 0.9715 | 0.9926      | 0.9677    | 0.9923 | 0.976    | 0.9694 |
| 0.0882        | 9.0   | 1683  | 0.2328          | 0.8882 | 0.9735      | 0.9223    | 0.9762 | 0.92     | 0.8943 |
| 0.0882        | 10.0  | 1870  | 0.1409          | 0.9405 | 0.9851      | 0.9518    | 0.9857 | 0.9547   | 0.9442 |
| 0.0606        | 11.0  | 2057  | 0.1400          | 0.9388 | 0.9859      | 0.9570    | 0.9871 | 0.9573   | 0.9446 |
| 0.0606        | 12.0  | 2244  | 0.0992          | 0.9541 | 0.9889      | 0.9577    | 0.9890 | 0.9653   | 0.9556 |
| 0.0606        | 13.0  | 2431  | 0.0883          | 0.9747 | 0.9928      | 0.9669    | 0.9922 | 0.976    | 0.9697 |
| 0.0495        | 14.0  | 2618  | 0.1169          | 0.9601 | 0.9878      | 0.9492    | 0.9869 | 0.96     | 0.9521 |
| 0.0495        | 15.0  | 2805  | 0.1043          | 0.9547 | 0.9888      | 0.9595    | 0.9890 | 0.9653   | 0.9566 |
| 0.0495        | 16.0  | 2992  | 0.1056          | 0.9572 | 0.9903      | 0.9708    | 0.9912 | 0.9707   | 0.9624 |
| 0.0524        | 17.0  | 3179  | 0.2722          | 0.8751 | 0.9733      | 0.9335    | 0.9779 | 0.92     | 0.8825 |
| 0.0524        | 18.0  | 3366  | 0.0712          | 0.9667 | 0.9923      | 0.9713    | 0.9926 | 0.976    | 0.9688 |
| 0.0343        | 19.0  | 3553  | 0.0973          | 0.9576 | 0.9911      | 0.9762    | 0.9923 | 0.9733   | 0.9644 |
| 0.0343        | 20.0  | 3740  | 0.0733          | 0.9626 | 0.9905      | 0.9664    | 0.9906 | 0.9707   | 0.9642 |
| 0.0343        | 21.0  | 3927  | 0.0747          | 0.9789 | 0.9937      | 0.9701    | 0.9931 | 0.9787   | 0.9731 |
| 0.0359        | 22.0  | 4114  | 0.1891          | 0.9069 | 0.9794      | 0.9498    | 0.9825 | 0.9387   | 0.9164 |
| 0.0359        | 23.0  | 4301  | 0.0650          | 0.9773 | 0.9936      | 0.9703    | 0.9931 | 0.9787   | 0.9730 |
| 0.0359        | 24.0  | 4488  | 0.2470          | 0.8837 | 0.9741      | 0.9389    | 0.9781 | 0.9227   | 0.8924 |
| 0.0345        | 25.0  | 4675  | 0.0792          | 0.9619 | 0.9920      | 0.9784    | 0.9931 | 0.976    | 0.9681 |
| 0.0345        | 26.0  | 4862  | 0.1752          | 0.9323 | 0.9848      | 0.9610    | 0.9867 | 0.9547   | 0.9407 |
| 0.0329        | 27.0  | 5049  | 0.0451          | 0.9801 | 0.9939      | 0.9789    | 0.9937 | 0.9813   | 0.9794 |
| 0.0329        | 28.0  | 5236  | 0.0707          | 0.9786 | 0.9924      | 0.9708    | 0.9917 | 0.976    | 0.9740 |
| 0.0329        | 29.0  | 5423  | 0.1154          | 0.9594 | 0.9903      | 0.9706    | 0.9909 | 0.9707   | 0.9637 |
| 0.0206        | 30.0  | 5610  | 0.1213          | 0.9594 | 0.9903      | 0.9706    | 0.9909 | 0.9707   | 0.9637 |
| 0.0206        | 31.0  | 5797  | 0.0892          | 0.9652 | 0.9912      | 0.9708    | 0.9915 | 0.9733   | 0.9676 |
| 0.0206        | 32.0  | 5984  | 0.0765          | 0.9589 | 0.9895      | 0.9659    | 0.9898 | 0.968    | 0.9617 |
| 0.0208        | 33.0  | 6171  | 0.1340          | 0.9598 | 0.9911      | 0.9759    | 0.9921 | 0.9733   | 0.9658 |
| 0.0208        | 34.0  | 6358  | 0.0987          | 0.9572 | 0.9903      | 0.9708    | 0.9912 | 0.9707   | 0.9624 |
| 0.0242        | 35.0  | 6545  | 0.1154          | 0.9572 | 0.9903      | 0.9708    | 0.9912 | 0.9707   | 0.9624 |
| 0.0242        | 36.0  | 6732  | 0.3260          | 0.8859 | 0.9741      | 0.9382    | 0.9778 | 0.9227   | 0.8946 |
| 0.0242        | 37.0  | 6919  | 0.1599          | 0.9557 | 0.9892      | 0.9710    | 0.9901 | 0.968    | 0.9612 |
| 0.0218        | 38.0  | 7106  | 0.1251          | 0.9535 | 0.9893      | 0.9712    | 0.9904 | 0.968    | 0.9598 |
| 0.0218        | 39.0  | 7293  | 0.2713          | 0.9112 | 0.9803      | 0.9516    | 0.9832 | 0.9413   | 0.9206 |
| 0.0218        | 40.0  | 7480  | 0.1112          | 0.9598 | 0.9911      | 0.9759    | 0.9921 | 0.9733   | 0.9658 |
| 0.0175        | 41.0  | 7667  | 0.0745          | 0.9695 | 0.9921      | 0.9734    | 0.9923 | 0.976    | 0.9711 |
| 0.0175        | 42.0  | 7854  | 0.0876          | 0.9610 | 0.9904      | 0.9683    | 0.9908 | 0.9707   | 0.9640 |
| 0.02          | 43.0  | 8041  | 0.0901          | 0.9588 | 0.9904      | 0.9683    | 0.9910 | 0.9707   | 0.9626 |
| 0.02          | 44.0  | 8228  | 0.1402          | 0.9529 | 0.9894      | 0.9685    | 0.9904 | 0.968    | 0.9587 |
| 0.02          | 45.0  | 8415  | 0.1325          | 0.9572 | 0.9903      | 0.9708    | 0.9912 | 0.9707   | 0.9624 |
| 0.0163        | 46.0  | 8602  | 0.0987          | 0.9657 | 0.9921      | 0.9755    | 0.9927 | 0.976    | 0.9697 |
| 0.0163        | 47.0  | 8789  | 0.1056          | 0.9657 | 0.9921      | 0.9755    | 0.9927 | 0.976    | 0.9697 |
| 0.0163        | 48.0  | 8976  | 0.0599          | 0.9800 | 0.9948      | 0.9811    | 0.9949 | 0.984    | 0.9805 |
| 0.0091        | 49.0  | 9163  | 0.0611          | 0.9705 | 0.9929      | 0.9780    | 0.9934 | 0.9787   | 0.9735 |
| 0.0091        | 50.0  | 9350  | 0.0606          | 0.9757 | 0.9939      | 0.9784    | 0.9941 | 0.9813   | 0.9770 |
| 0.0123        | 51.0  | 9537  | 0.0622          | 0.9816 | 0.9949      | 0.9797    | 0.9948 | 0.984    | 0.9806 |
| 0.0123        | 52.0  | 9724  | 0.1036          | 0.9588 | 0.9904      | 0.9683    | 0.9910 | 0.9707   | 0.9626 |
| 0.0123        | 53.0  | 9911  | 0.0854          | 0.9715 | 0.9930      | 0.9758    | 0.9933 | 0.9787   | 0.9734 |
| 0.0076        | 54.0  | 10098 | 0.0600          | 0.9773 | 0.9940      | 0.9769    | 0.9940 | 0.9813   | 0.9771 |
| 0.0076        | 55.0  | 10285 | 0.1901          | 0.9429 | 0.9875      | 0.9674    | 0.9891 | 0.9627   | 0.9508 |
| 0.0076        | 56.0  | 10472 | 0.1208          | 0.9614 | 0.9912      | 0.9731    | 0.9919 | 0.9733   | 0.9660 |
| 0.0118        | 57.0  | 10659 | 0.2020          | 0.9386 | 0.9866      | 0.9654    | 0.9884 | 0.96     | 0.9469 |
| 0.0118        | 58.0  | 10846 | 0.0568          | 0.9806 | 0.9942      | 0.9749    | 0.9938 | 0.9813   | 0.9774 |
| 0.0161        | 59.0  | 11033 | 0.0507          | 0.9806 | 0.9942      | 0.9749    | 0.9938 | 0.9813   | 0.9774 |
| 0.0161        | 60.0  | 11220 | 0.0830          | 0.9757 | 0.9939      | 0.9784    | 0.9941 | 0.9813   | 0.9770 |
| 0.0161        | 61.0  | 11407 | 0.0931          | 0.9757 | 0.9939      | 0.9784    | 0.9941 | 0.9813   | 0.9770 |
| 0.0086        | 62.0  | 11594 | 0.1000          | 0.9715 | 0.9930      | 0.9758    | 0.9933 | 0.9787   | 0.9734 |
| 0.0086        | 63.0  | 11781 | 0.1215          | 0.9588 | 0.9904      | 0.9683    | 0.9910 | 0.9707   | 0.9626 |
| 0.0086        | 64.0  | 11968 | 0.0695          | 0.9858 | 0.9958      | 0.9825    | 0.9956 | 0.9867   | 0.9841 |
| 0.0048        | 65.0  | 12155 | 0.0747          | 0.9752 | 0.9941      | 0.9767    | 0.9942 | 0.9813   | 0.9759 |
| 0.0048        | 66.0  | 12342 | 0.0733          | 0.9795 | 0.9940      | 0.9772    | 0.9938 | 0.9813   | 0.9783 |
| 0.0066        | 67.0  | 12529 | 0.1429          | 0.9551 | 0.9894      | 0.9683    | 0.9902 | 0.968    | 0.9601 |
| 0.0066        | 68.0  | 12716 | 0.1146          | 0.9673 | 0.9922      | 0.9733    | 0.9925 | 0.976    | 0.9699 |
| 0.0066        | 69.0  | 12903 | 0.1022          | 0.9737 | 0.9930      | 0.9760    | 0.9931 | 0.9787   | 0.9747 |
| 0.0068        | 70.0  | 13090 | 0.0850          | 0.9789 | 0.9941      | 0.9757    | 0.9939 | 0.9813   | 0.9772 |
| 0.0068        | 71.0  | 13277 | 0.1619          | 0.9509 | 0.9885      | 0.9660    | 0.9894 | 0.9653   | 0.9564 |
| 0.0068        | 72.0  | 13464 | 0.1334          | 0.9636 | 0.9911      | 0.9730    | 0.9917 | 0.9733   | 0.9674 |
| 0.0046        | 73.0  | 13651 | 0.1099          | 0.9636 | 0.9911      | 0.9730    | 0.9917 | 0.9733   | 0.9674 |
| 0.0046        | 74.0  | 13838 | 0.2110          | 0.9493 | 0.9884      | 0.9691    | 0.9896 | 0.9653   | 0.9561 |
| 0.0087        | 75.0  | 14025 | 0.1417          | 0.9572 | 0.9903      | 0.9708    | 0.9912 | 0.9707   | 0.9624 |
| 0.0087        | 76.0  | 14212 | 0.2051          | 0.9471 | 0.9884      | 0.9695    | 0.9899 | 0.9653   | 0.9546 |
| 0.0087        | 77.0  | 14399 | 0.1403          | 0.9641 | 0.9920      | 0.9781    | 0.9928 | 0.976    | 0.9694 |
| 0.0061        | 78.0  | 14586 | 0.2232          | 0.9408 | 0.9866      | 0.9650    | 0.9882 | 0.96     | 0.9485 |
| 0.0061        | 79.0  | 14773 | 0.0962          | 0.9757 | 0.9939      | 0.9784    | 0.9941 | 0.9813   | 0.9770 |
| 0.0061        | 80.0  | 14960 | 0.1113          | 0.9683 | 0.9928      | 0.9803    | 0.9936 | 0.9787   | 0.9731 |
| 0.0076        | 81.0  | 15147 | 0.1090          | 0.9683 | 0.9928      | 0.9803    | 0.9936 | 0.9787   | 0.9731 |
| 0.0076        | 82.0  | 15334 | 0.0826          | 0.9741 | 0.9938      | 0.9803    | 0.9942 | 0.9813   | 0.9768 |
| 0.0023        | 83.0  | 15521 | 0.0644          | 0.9832 | 0.9950      | 0.9787    | 0.9947 | 0.984    | 0.9807 |
| 0.0023        | 84.0  | 15708 | 0.0605          | 0.9832 | 0.9950      | 0.9787    | 0.9947 | 0.984    | 0.9807 |
| 0.0023        | 85.0  | 15895 | 0.1132          | 0.9594 | 0.9903      | 0.9706    | 0.9909 | 0.9707   | 0.9637 |
| 0.0061        | 86.0  | 16082 | 0.0592          | 0.9773 | 0.9940      | 0.9769    | 0.9940 | 0.9813   | 0.9771 |
| 0.0061        | 87.0  | 16269 | 0.1200          | 0.9657 | 0.9921      | 0.9755    | 0.9927 | 0.976    | 0.9697 |
| 0.0061        | 88.0  | 16456 | 0.1636          | 0.9578 | 0.9902      | 0.9734    | 0.9911 | 0.9707   | 0.9635 |
| 0.0011        | 89.0  | 16643 | 0.1011          | 0.9699 | 0.9929      | 0.9779    | 0.9934 | 0.9787   | 0.9733 |
| 0.0011        | 90.0  | 16830 | 0.1491          | 0.9598 | 0.9911      | 0.9759    | 0.9921 | 0.9733   | 0.9658 |
| 0.0012        | 91.0  | 17017 | 0.1201          | 0.9657 | 0.9921      | 0.9755    | 0.9927 | 0.976    | 0.9697 |
| 0.0012        | 92.0  | 17204 | 0.0848          | 0.9657 | 0.9921      | 0.9755    | 0.9927 | 0.976    | 0.9697 |
| 0.0012        | 93.0  | 17391 | 0.0739          | 0.9757 | 0.9939      | 0.9784    | 0.9941 | 0.9813   | 0.9770 |
| 0.0014        | 94.0  | 17578 | 0.0755          | 0.9757 | 0.9939      | 0.9784    | 0.9941 | 0.9813   | 0.9770 |
| 0.0014        | 95.0  | 17765 | 0.1235          | 0.9657 | 0.9921      | 0.9755    | 0.9927 | 0.976    | 0.9697 |
| 0.0014        | 96.0  | 17952 | 0.1216          | 0.9657 | 0.9921      | 0.9755    | 0.9927 | 0.976    | 0.9697 |
| 0.0017        | 97.0  | 18139 | 0.1223          | 0.9657 | 0.9921      | 0.9755    | 0.9927 | 0.976    | 0.9697 |
| 0.0017        | 98.0  | 18326 | 0.1069          | 0.9657 | 0.9921      | 0.9755    | 0.9927 | 0.976    | 0.9697 |
| 0.0035        | 99.0  | 18513 | 0.1174          | 0.9657 | 0.9921      | 0.9755    | 0.9927 | 0.976    | 0.9697 |
| 0.0035        | 100.0 | 18700 | 0.1179          | 0.9657 | 0.9921      | 0.9755    | 0.9927 | 0.976    | 0.9697 |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
{"tags": ["generated_from_trainer"], "datasets": ["aryap2/kematangan-pisang-224_v4.7"], "pipeline_tag": "image-classification"}
transformers pytorch vit image-classification generated_from_trainer dataset:aryap2/kematangan-pisang-224_v4.7 autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #vit #image-classification #generated_from_trainer #dataset-aryap2/kematangan-pisang-224_v4.7 #autotrain_compatible #endpoints_compatible #has_space #region-us 

kematangan-pisang-vit-h-14-100eph-224-telyu
===========================================


This model is a fine-tuned version of google/vit-huge-patch14-224-in21k on the dataset kematangan pisang primer.


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 100


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 100 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #vit #image-classification #generated_from_trainer #dataset-aryap2/kematangan-pisang-224_v4.7 #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 100 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3