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# resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_og_simkd

This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3748
- Accuracy: 0.8023
- Brier Loss: 0.2845
- Nll: 1.8818
- F1 Micro: 0.8023
- F1 Macro: 0.8020
- Ece: 0.0375
- Aurc: 0.0534

## 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   | 0.8880          | 0.1955   | 0.8872     | 5.3865 | 0.1955   | 0.1551   | 0.0582 | 0.7111 |
| 0.9199        | 2.0   | 500   | 0.6464          | 0.407    | 0.7284     | 5.2363 | 0.4070   | 0.3745   | 0.0770 | 0.4284 |
| 0.9199        | 3.0   | 750   | 0.5608          | 0.5945   | 0.5337     | 3.5976 | 0.5945   | 0.5912   | 0.0561 | 0.1950 |
| 0.563         | 4.0   | 1000  | 0.4962          | 0.6905   | 0.4235     | 2.6948 | 0.6905   | 0.6885   | 0.0474 | 0.1170 |
| 0.563         | 5.0   | 1250  | 0.4613          | 0.7177   | 0.3858     | 2.5472 | 0.7178   | 0.7181   | 0.0512 | 0.0964 |
| 0.4567        | 6.0   | 1500  | 0.4372          | 0.742    | 0.3584     | 2.3396 | 0.7420   | 0.7425   | 0.0527 | 0.0824 |
| 0.4567        | 7.0   | 1750  | 0.4271          | 0.7595   | 0.3406     | 2.2123 | 0.7595   | 0.7596   | 0.0459 | 0.0756 |
| 0.4103        | 8.0   | 2000  | 0.4129          | 0.7658   | 0.3308     | 2.1667 | 0.7658   | 0.7666   | 0.0439 | 0.0704 |
| 0.4103        | 9.0   | 2250  | 0.4070          | 0.7678   | 0.3296     | 2.1663 | 0.7678   | 0.7692   | 0.0485 | 0.0699 |
| 0.3836        | 10.0  | 2500  | 0.4017          | 0.7725   | 0.3209     | 2.1207 | 0.7725   | 0.7732   | 0.0426 | 0.0667 |
| 0.3836        | 11.0  | 2750  | 0.3984          | 0.7768   | 0.3153     | 2.0353 | 0.7768   | 0.7771   | 0.0454 | 0.0651 |
| 0.3645        | 12.0  | 3000  | 0.3961          | 0.7752   | 0.3124     | 2.0755 | 0.7752   | 0.7754   | 0.0428 | 0.0642 |
| 0.3645        | 13.0  | 3250  | 0.3961          | 0.786    | 0.3071     | 1.9949 | 0.786    | 0.7861   | 0.0407 | 0.0612 |
| 0.3497        | 14.0  | 3500  | 0.3899          | 0.7823   | 0.3053     | 1.9769 | 0.7823   | 0.7823   | 0.0435 | 0.0606 |
| 0.3497        | 15.0  | 3750  | 0.3873          | 0.7853   | 0.3021     | 1.9881 | 0.7853   | 0.7849   | 0.0479 | 0.0594 |
| 0.3378        | 16.0  | 4000  | 0.3861          | 0.7833   | 0.3026     | 1.9263 | 0.7833   | 0.7834   | 0.0431 | 0.0593 |
| 0.3378        | 17.0  | 4250  | 0.3853          | 0.7913   | 0.2970     | 1.9108 | 0.7913   | 0.7917   | 0.0390 | 0.0571 |
| 0.3271        | 18.0  | 4500  | 0.3840          | 0.7903   | 0.2978     | 1.9643 | 0.7903   | 0.7902   | 0.0377 | 0.0576 |
| 0.3271        | 19.0  | 4750  | 0.3828          | 0.7915   | 0.2967     | 1.9332 | 0.7915   | 0.7914   | 0.0393 | 0.0572 |
| 0.3186        | 20.0  | 5000  | 0.3806          | 0.7913   | 0.2938     | 1.9410 | 0.7913   | 0.7909   | 0.0410 | 0.0563 |
| 0.3186        | 21.0  | 5250  | 0.3815          | 0.7953   | 0.2921     | 1.9285 | 0.7953   | 0.7949   | 0.0387 | 0.0566 |
| 0.3111        | 22.0  | 5500  | 0.3838          | 0.7895   | 0.2949     | 1.9126 | 0.7895   | 0.7894   | 0.0382 | 0.0570 |
| 0.3111        | 23.0  | 5750  | 0.3799          | 0.7955   | 0.2902     | 1.9332 | 0.7955   | 0.7955   | 0.0373 | 0.0558 |
| 0.305         | 24.0  | 6000  | 0.3796          | 0.7947   | 0.2912     | 1.8615 | 0.7947   | 0.7940   | 0.0418 | 0.0561 |
| 0.305         | 25.0  | 6250  | 0.3805          | 0.7947   | 0.2912     | 1.8999 | 0.7947   | 0.7940   | 0.0413 | 0.0558 |
| 0.2993        | 26.0  | 6500  | 0.3842          | 0.7925   | 0.2913     | 1.9451 | 0.7925   | 0.7927   | 0.0339 | 0.0559 |
| 0.2993        | 27.0  | 6750  | 0.3784          | 0.794    | 0.2908     | 1.9151 | 0.7940   | 0.7942   | 0.0389 | 0.0553 |
| 0.2943        | 28.0  | 7000  | 0.3779          | 0.7957   | 0.2895     | 1.8758 | 0.7957   | 0.7957   | 0.0392 | 0.0549 |
| 0.2943        | 29.0  | 7250  | 0.3776          | 0.7955   | 0.2892     | 1.8785 | 0.7955   | 0.7947   | 0.0445 | 0.0549 |
| 0.2905        | 30.0  | 7500  | 0.3775          | 0.7973   | 0.2879     | 1.8786 | 0.7973   | 0.7972   | 0.0379 | 0.0550 |
| 0.2905        | 31.0  | 7750  | 0.3773          | 0.7945   | 0.2903     | 1.9039 | 0.7945   | 0.7942   | 0.0405 | 0.0551 |
| 0.2863        | 32.0  | 8000  | 0.3764          | 0.7963   | 0.2880     | 1.8569 | 0.7963   | 0.7962   | 0.0375 | 0.0549 |
| 0.2863        | 33.0  | 8250  | 0.3775          | 0.7925   | 0.2884     | 1.9070 | 0.7925   | 0.7917   | 0.0411 | 0.0544 |
| 0.2831        | 34.0  | 8500  | 0.3762          | 0.7935   | 0.2873     | 1.8608 | 0.7935   | 0.7933   | 0.0389 | 0.0547 |
| 0.2831        | 35.0  | 8750  | 0.3765          | 0.7973   | 0.2868     | 1.9316 | 0.7973   | 0.7970   | 0.0385 | 0.0540 |
| 0.28          | 36.0  | 9000  | 0.3750          | 0.7967   | 0.2857     | 1.8871 | 0.7967   | 0.7965   | 0.0375 | 0.0540 |
| 0.28          | 37.0  | 9250  | 0.3761          | 0.793    | 0.2874     | 1.8977 | 0.793    | 0.7926   | 0.0405 | 0.0543 |
| 0.2775        | 38.0  | 9500  | 0.3760          | 0.7983   | 0.2861     | 1.8613 | 0.7983   | 0.7987   | 0.0422 | 0.0540 |
| 0.2775        | 39.0  | 9750  | 0.3761          | 0.7955   | 0.2870     | 1.8744 | 0.7955   | 0.7957   | 0.0412 | 0.0545 |
| 0.2755        | 40.0  | 10000 | 0.3753          | 0.8007   | 0.2852     | 1.8640 | 0.8007   | 0.8006   | 0.0345 | 0.0532 |
| 0.2755        | 41.0  | 10250 | 0.3753          | 0.8023   | 0.2857     | 1.8637 | 0.8023   | 0.8025   | 0.0363 | 0.0535 |
| 0.2735        | 42.0  | 10500 | 0.3751          | 0.7995   | 0.2851     | 1.9134 | 0.7995   | 0.7994   | 0.0403 | 0.0531 |
| 0.2735        | 43.0  | 10750 | 0.3753          | 0.8      | 0.2857     | 1.8832 | 0.8000   | 0.7996   | 0.0406 | 0.0538 |
| 0.2717        | 44.0  | 11000 | 0.3746          | 0.7985   | 0.2851     | 1.8545 | 0.7985   | 0.7982   | 0.0432 | 0.0532 |
| 0.2717        | 45.0  | 11250 | 0.3747          | 0.7985   | 0.2847     | 1.8730 | 0.7985   | 0.7984   | 0.0400 | 0.0534 |
| 0.2701        | 46.0  | 11500 | 0.3744          | 0.801    | 0.2843     | 1.8783 | 0.801    | 0.8007   | 0.0411 | 0.0532 |
| 0.2701        | 47.0  | 11750 | 0.3744          | 0.798    | 0.2852     | 1.8843 | 0.798    | 0.7975   | 0.0420 | 0.0535 |
| 0.2694        | 48.0  | 12000 | 0.3753          | 0.7993   | 0.2857     | 1.8875 | 0.7993   | 0.7988   | 0.0405 | 0.0532 |
| 0.2694        | 49.0  | 12250 | 0.3758          | 0.7965   | 0.2868     | 1.8927 | 0.7965   | 0.7964   | 0.0415 | 0.0539 |
| 0.2684        | 50.0  | 12500 | 0.3748          | 0.8023   | 0.2845     | 1.8818 | 0.8023   | 0.8020   | 0.0375 | 0.0534 |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.2.0.dev20231002
- Datasets 2.7.1
- Tokenizers 0.13.3

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