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# resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_simkd_rand

This model is a fine-tuned version of [bdpc/resnet101_rvl-cdip](https://huggingface.co/bdpc/resnet101_rvl-cdip) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4174
- Accuracy: 0.7665
- Brier Loss: 0.3263
- Nll: 2.0962
- F1 Micro: 0.7665
- F1 Macro: 0.7661
- Ece: 0.0504
- Aurc: 0.0700

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Brier Loss | Nll    | F1 Micro | F1 Macro | Ece    | Aurc   |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| No log        | 1.0   | 250   | 0.8645          | 0.1192   | 0.9514     | 3.2233 | 0.1192   | 0.0652   | 0.1115 | 0.8122 |
| 1.2523        | 2.0   | 500   | 0.7139          | 0.1797   | 0.8939     | 3.1527 | 0.1798   | 0.1283   | 0.0795 | 0.6807 |
| 1.2523        | 3.0   | 750   | 0.6662          | 0.3145   | 0.8040     | 6.3258 | 0.3145   | 0.2485   | 0.0647 | 0.4987 |
| 0.6553        | 4.0   | 1000  | 0.6265          | 0.3738   | 0.7356     | 6.0830 | 0.3738   | 0.3459   | 0.0768 | 0.4070 |
| 0.6553        | 5.0   | 1250  | 0.5609          | 0.531    | 0.6047     | 4.7056 | 0.531    | 0.5234   | 0.0639 | 0.2463 |
| 0.5525        | 6.0   | 1500  | 0.5341          | 0.589    | 0.5450     | 3.9772 | 0.589    | 0.5948   | 0.0718 | 0.1912 |
| 0.5525        | 7.0   | 1750  | 0.4938          | 0.6468   | 0.4733     | 3.3676 | 0.6468   | 0.6486   | 0.0670 | 0.1408 |
| 0.4842        | 8.0   | 2000  | 0.4765          | 0.7      | 0.4288     | 2.8692 | 0.7      | 0.6960   | 0.0666 | 0.1181 |
| 0.4842        | 9.0   | 2250  | 0.5359          | 0.5938   | 0.5534     | 3.9887 | 0.5938   | 0.6011   | 0.1211 | 0.1809 |
| 0.4476        | 10.0  | 2500  | 0.4611          | 0.7037   | 0.4122     | 2.7429 | 0.7037   | 0.6991   | 0.0679 | 0.1097 |
| 0.4476        | 11.0  | 2750  | 0.4460          | 0.7225   | 0.3913     | 2.6158 | 0.7225   | 0.7240   | 0.0725 | 0.0967 |
| 0.4219        | 12.0  | 3000  | 0.4387          | 0.7388   | 0.3752     | 2.4639 | 0.7388   | 0.7356   | 0.0696 | 0.0892 |
| 0.4219        | 13.0  | 3250  | 0.4399          | 0.7378   | 0.3724     | 2.4683 | 0.7378   | 0.7381   | 0.0550 | 0.0898 |
| 0.4007        | 14.0  | 3500  | 0.4441          | 0.737    | 0.3738     | 2.4680 | 0.737    | 0.7334   | 0.0581 | 0.0906 |
| 0.4007        | 15.0  | 3750  | 0.4517          | 0.7248   | 0.3906     | 2.5901 | 0.7248   | 0.7302   | 0.0653 | 0.0961 |
| 0.3825        | 16.0  | 4000  | 0.4430          | 0.737    | 0.3727     | 2.5633 | 0.737    | 0.7350   | 0.0595 | 0.0884 |
| 0.3825        | 17.0  | 4250  | 0.4345          | 0.7482   | 0.3518     | 2.3938 | 0.7482   | 0.7473   | 0.0541 | 0.0784 |
| 0.3672        | 18.0  | 4500  | 0.4642          | 0.7385   | 0.3690     | 2.4016 | 0.7385   | 0.7367   | 0.0571 | 0.0891 |
| 0.3672        | 19.0  | 4750  | 0.4309          | 0.7432   | 0.3585     | 2.3331 | 0.7432   | 0.7464   | 0.0558 | 0.0824 |
| 0.3547        | 20.0  | 5000  | 0.4205          | 0.7602   | 0.3418     | 2.2097 | 0.7602   | 0.7617   | 0.0470 | 0.0744 |
| 0.3547        | 21.0  | 5250  | 0.4174          | 0.7602   | 0.3387     | 2.2020 | 0.7602   | 0.7594   | 0.0488 | 0.0748 |
| 0.3442        | 22.0  | 5500  | 0.4207          | 0.7515   | 0.3458     | 2.2370 | 0.7515   | 0.7543   | 0.0540 | 0.0777 |
| 0.3442        | 23.0  | 5750  | 0.4465          | 0.733    | 0.3783     | 2.5113 | 0.733    | 0.7295   | 0.0576 | 0.0919 |
| 0.3355        | 24.0  | 6000  | 0.4391          | 0.7425   | 0.3649     | 2.4598 | 0.7425   | 0.7459   | 0.0534 | 0.0830 |
| 0.3355        | 25.0  | 6250  | 0.4233          | 0.7598   | 0.3352     | 2.2321 | 0.7598   | 0.7609   | 0.0495 | 0.0729 |
| 0.3274        | 26.0  | 6500  | 0.4174          | 0.7665   | 0.3305     | 2.2062 | 0.7665   | 0.7673   | 0.0482 | 0.0699 |
| 0.3274        | 27.0  | 6750  | 0.4153          | 0.7598   | 0.3389     | 2.2158 | 0.7598   | 0.7583   | 0.0549 | 0.0740 |
| 0.3206        | 28.0  | 7000  | 0.4175          | 0.763    | 0.3323     | 2.1843 | 0.763    | 0.7610   | 0.0494 | 0.0721 |
| 0.3206        | 29.0  | 7250  | 0.4201          | 0.7522   | 0.3467     | 2.2627 | 0.7522   | 0.7495   | 0.0576 | 0.0783 |
| 0.3147        | 30.0  | 7500  | 0.4133          | 0.7625   | 0.3334     | 2.1459 | 0.7625   | 0.7631   | 0.0477 | 0.0733 |
| 0.3147        | 31.0  | 7750  | 0.4213          | 0.7558   | 0.3421     | 2.2877 | 0.7558   | 0.7535   | 0.0567 | 0.0758 |
| 0.3092        | 32.0  | 8000  | 0.4136          | 0.7668   | 0.3294     | 2.1791 | 0.7668   | 0.7662   | 0.0465 | 0.0702 |
| 0.3092        | 33.0  | 8250  | 0.4114          | 0.7638   | 0.3331     | 2.1993 | 0.7638   | 0.7613   | 0.0517 | 0.0722 |
| 0.3046        | 34.0  | 8500  | 0.4154          | 0.764    | 0.3294     | 2.1689 | 0.764    | 0.7639   | 0.0489 | 0.0714 |
| 0.3046        | 35.0  | 8750  | 0.4119          | 0.7638   | 0.3327     | 2.1482 | 0.7638   | 0.7628   | 0.0449 | 0.0725 |
| 0.3001        | 36.0  | 9000  | 0.4183          | 0.759    | 0.3348     | 2.1775 | 0.7590   | 0.7605   | 0.0513 | 0.0731 |
| 0.3001        | 37.0  | 9250  | 0.4097          | 0.7578   | 0.3344     | 2.2029 | 0.7577   | 0.7571   | 0.0525 | 0.0736 |
| 0.2964        | 38.0  | 9500  | 0.4126          | 0.7655   | 0.3292     | 2.1374 | 0.7655   | 0.7657   | 0.0481 | 0.0710 |
| 0.2964        | 39.0  | 9750  | 0.4235          | 0.7642   | 0.3287     | 2.1640 | 0.7642   | 0.7639   | 0.0543 | 0.0707 |
| 0.293         | 40.0  | 10000 | 0.4168          | 0.7678   | 0.3284     | 2.1264 | 0.7678   | 0.7681   | 0.0494 | 0.0702 |
| 0.293         | 41.0  | 10250 | 0.4118          | 0.7682   | 0.3270     | 2.1387 | 0.7682   | 0.7684   | 0.0462 | 0.0702 |
| 0.29          | 42.0  | 10500 | 0.4151          | 0.7618   | 0.3288     | 2.1464 | 0.7618   | 0.7609   | 0.0493 | 0.0718 |
| 0.29          | 43.0  | 10750 | 0.4172          | 0.7608   | 0.3283     | 2.1341 | 0.7608   | 0.7607   | 0.0538 | 0.0708 |
| 0.2876        | 44.0  | 11000 | 0.4159          | 0.7612   | 0.3278     | 2.1561 | 0.7612   | 0.7601   | 0.0514 | 0.0707 |
| 0.2876        | 45.0  | 11250 | 0.4173          | 0.761    | 0.3291     | 2.1825 | 0.761    | 0.7602   | 0.0493 | 0.0711 |
| 0.2855        | 46.0  | 11500 | 0.4137          | 0.761    | 0.3295     | 2.1514 | 0.761    | 0.7598   | 0.0507 | 0.0709 |
| 0.2855        | 47.0  | 11750 | 0.4143          | 0.764    | 0.3278     | 2.1414 | 0.764    | 0.7630   | 0.0483 | 0.0705 |
| 0.2841        | 48.0  | 12000 | 0.4162          | 0.7668   | 0.3262     | 2.1191 | 0.7668   | 0.7666   | 0.0451 | 0.0699 |
| 0.2841        | 49.0  | 12250 | 0.4190          | 0.765    | 0.3271     | 2.1267 | 0.765    | 0.7647   | 0.0496 | 0.0701 |
| 0.283         | 50.0  | 12500 | 0.4174          | 0.7665   | 0.3263     | 2.0962 | 0.7665   | 0.7661   | 0.0504 | 0.0700 |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.2

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