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# resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_hint

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: 20.4893
- Accuracy: 0.7622
- Brier Loss: 0.3995
- Nll: 2.6673
- F1 Micro: 0.7622
- F1 Macro: 0.7619
- Ece: 0.1742
- Aurc: 0.0853

## 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   | 27.0152         | 0.144    | 0.9329     | 8.3774 | 0.144    | 0.1293   | 0.0760 | 0.8496 |
| 26.9201       | 2.0   | 500   | 25.8022         | 0.4547   | 0.8625     | 4.1098 | 0.4547   | 0.4194   | 0.3292 | 0.3673 |
| 26.9201       | 3.0   | 750   | 24.5485         | 0.5617   | 0.6135     | 3.0722 | 0.5617   | 0.5439   | 0.1557 | 0.2257 |
| 24.565        | 4.0   | 1000  | 23.9825         | 0.6388   | 0.5062     | 2.7343 | 0.6388   | 0.6354   | 0.1084 | 0.1537 |
| 24.565        | 5.0   | 1250  | 23.8483         | 0.6747   | 0.4518     | 2.5930 | 0.6747   | 0.6686   | 0.0597 | 0.1289 |
| 23.3904       | 6.0   | 1500  | 23.2280         | 0.7137   | 0.3953     | 2.4736 | 0.7138   | 0.7117   | 0.0486 | 0.0997 |
| 23.3904       | 7.0   | 1750  | 23.0275         | 0.725    | 0.3781     | 2.3823 | 0.7250   | 0.7238   | 0.0414 | 0.0911 |
| 22.6462       | 8.0   | 2000  | 22.8213         | 0.7358   | 0.3699     | 2.3745 | 0.7358   | 0.7351   | 0.0539 | 0.0881 |
| 22.6462       | 9.0   | 2250  | 22.6219         | 0.7468   | 0.3629     | 2.3056 | 0.7468   | 0.7465   | 0.0617 | 0.0852 |
| 22.0944       | 10.0  | 2500  | 22.4746         | 0.751    | 0.3593     | 2.3500 | 0.751    | 0.7523   | 0.0637 | 0.0846 |
| 22.0944       | 11.0  | 2750  | 22.3503         | 0.752    | 0.3624     | 2.4245 | 0.752    | 0.7533   | 0.0810 | 0.0834 |
| 21.6411       | 12.0  | 3000  | 22.2263         | 0.7545   | 0.3693     | 2.4277 | 0.7545   | 0.7547   | 0.0972 | 0.0885 |
| 21.6411       | 13.0  | 3250  | 22.1353         | 0.7522   | 0.3740     | 2.4647 | 0.7522   | 0.7532   | 0.1141 | 0.0862 |
| 21.2742       | 14.0  | 3500  | 22.1122         | 0.7475   | 0.3868     | 2.5369 | 0.7475   | 0.7495   | 0.1250 | 0.0922 |
| 21.2742       | 15.0  | 3750  | 22.0040         | 0.7508   | 0.3842     | 2.5364 | 0.7508   | 0.7501   | 0.1304 | 0.0911 |
| 20.9515       | 16.0  | 4000  | 21.8795         | 0.758    | 0.3772     | 2.5474 | 0.7580   | 0.7578   | 0.1324 | 0.0846 |
| 20.9515       | 17.0  | 4250  | 21.7554         | 0.754    | 0.3892     | 2.5498 | 0.754    | 0.7543   | 0.1420 | 0.0923 |
| 20.6695       | 18.0  | 4500  | 21.6863         | 0.749    | 0.3981     | 2.6337 | 0.749    | 0.7507   | 0.1510 | 0.0922 |
| 20.6695       | 19.0  | 4750  | 21.6123         | 0.7498   | 0.4007     | 2.5993 | 0.7498   | 0.7499   | 0.1551 | 0.0921 |
| 20.4239       | 20.0  | 5000  | 21.5128         | 0.7595   | 0.3845     | 2.5510 | 0.7595   | 0.7590   | 0.1498 | 0.0870 |
| 20.4239       | 21.0  | 5250  | 21.4770         | 0.7542   | 0.4005     | 2.6396 | 0.7542   | 0.7547   | 0.1623 | 0.0932 |
| 20.2131       | 22.0  | 5500  | 21.3497         | 0.7612   | 0.3892     | 2.5117 | 0.7612   | 0.7609   | 0.1539 | 0.0891 |
| 20.2131       | 23.0  | 5750  | 21.3489         | 0.7572   | 0.3956     | 2.5227 | 0.7572   | 0.7570   | 0.1608 | 0.0883 |
| 20.0332       | 24.0  | 6000  | 21.2609         | 0.7585   | 0.3939     | 2.5487 | 0.7585   | 0.7595   | 0.1629 | 0.0860 |
| 20.0332       | 25.0  | 6250  | 21.2046         | 0.7552   | 0.3982     | 2.6283 | 0.7552   | 0.7559   | 0.1663 | 0.0878 |
| 19.8699       | 26.0  | 6500  | 21.1515         | 0.7528   | 0.4038     | 2.6730 | 0.7528   | 0.7536   | 0.1721 | 0.0858 |
| 19.8699       | 27.0  | 6750  | 21.0789         | 0.7562   | 0.4003     | 2.6027 | 0.7562   | 0.7575   | 0.1683 | 0.0876 |
| 19.7228       | 28.0  | 7000  | 21.0357         | 0.7565   | 0.3996     | 2.6490 | 0.7565   | 0.7561   | 0.1707 | 0.0844 |
| 19.7228       | 29.0  | 7250  | 20.9975         | 0.758    | 0.3971     | 2.6300 | 0.7580   | 0.7574   | 0.1704 | 0.0835 |
| 19.589        | 30.0  | 7500  | 20.9221         | 0.7568   | 0.4007     | 2.5841 | 0.7568   | 0.7567   | 0.1714 | 0.0860 |
| 19.589        | 31.0  | 7750  | 20.8725         | 0.7562   | 0.3996     | 2.5775 | 0.7562   | 0.7562   | 0.1752 | 0.0847 |
| 19.4738       | 32.0  | 8000  | 20.8438         | 0.7572   | 0.3999     | 2.6441 | 0.7572   | 0.7570   | 0.1693 | 0.0877 |
| 19.4738       | 33.0  | 8250  | 20.8337         | 0.755    | 0.4052     | 2.6660 | 0.755    | 0.7555   | 0.1743 | 0.0868 |
| 19.3704       | 34.0  | 8500  | 20.7635         | 0.7575   | 0.4022     | 2.6885 | 0.7575   | 0.7583   | 0.1764 | 0.0868 |
| 19.3704       | 35.0  | 8750  | 20.7705         | 0.7608   | 0.4001     | 2.6415 | 0.7608   | 0.7601   | 0.1735 | 0.0856 |
| 19.2791       | 36.0  | 9000  | 20.7221         | 0.7632   | 0.3984     | 2.7139 | 0.7632   | 0.7640   | 0.1706 | 0.0857 |
| 19.2791       | 37.0  | 9250  | 20.6873         | 0.7622   | 0.3986     | 2.6743 | 0.7622   | 0.7625   | 0.1715 | 0.0838 |
| 19.2036       | 38.0  | 9500  | 20.6757         | 0.7618   | 0.3990     | 2.6225 | 0.7618   | 0.7620   | 0.1735 | 0.0852 |
| 19.2036       | 39.0  | 9750  | 20.6421         | 0.7588   | 0.4018     | 2.6342 | 0.7588   | 0.7579   | 0.1761 | 0.0870 |
| 19.1398       | 40.0  | 10000 | 20.6432         | 0.761    | 0.4057     | 2.6595 | 0.761    | 0.7610   | 0.1760 | 0.0868 |
| 19.1398       | 41.0  | 10250 | 20.5778         | 0.7672   | 0.3981     | 2.6180 | 0.7672   | 0.7674   | 0.1680 | 0.0850 |
| 19.0835       | 42.0  | 10500 | 20.5628         | 0.764    | 0.3981     | 2.6309 | 0.764    | 0.7625   | 0.1726 | 0.0851 |
| 19.0835       | 43.0  | 10750 | 20.5530         | 0.7632   | 0.3995     | 2.6470 | 0.7632   | 0.7628   | 0.1733 | 0.0868 |
| 19.0398       | 44.0  | 11000 | 20.5625         | 0.761    | 0.4029     | 2.6650 | 0.761    | 0.7608   | 0.1764 | 0.0864 |
| 19.0398       | 45.0  | 11250 | 20.5637         | 0.7628   | 0.4010     | 2.6709 | 0.7628   | 0.7623   | 0.1760 | 0.0850 |
| 19.0073       | 46.0  | 11500 | 20.5378         | 0.7628   | 0.3998     | 2.6522 | 0.7628   | 0.7631   | 0.1749 | 0.0859 |
| 19.0073       | 47.0  | 11750 | 20.5199         | 0.7615   | 0.4010     | 2.6406 | 0.7615   | 0.7619   | 0.1748 | 0.0867 |
| 18.9818       | 48.0  | 12000 | 20.5378         | 0.761    | 0.4031     | 2.6434 | 0.761    | 0.7616   | 0.1767 | 0.0856 |
| 18.9818       | 49.0  | 12250 | 20.4962         | 0.7652   | 0.3962     | 2.6250 | 0.7652   | 0.7653   | 0.1720 | 0.0853 |
| 18.9734       | 50.0  | 12500 | 20.4893         | 0.7622   | 0.3995     | 2.6673 | 0.7622   | 0.7619   | 0.1742 | 0.0853 |


### Framework versions

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

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