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# resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_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: 0.4602
- Accuracy: 0.769
- Brier Loss: 0.3252
- Nll: 2.1002
- F1 Micro: 0.769
- F1 Macro: 0.7667
- Ece: 0.0388
- Aurc: 0.0678

## 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   | 1.0910          | 0.059    | 0.9372     | 6.6175 | 0.059    | 0.0236   | 0.0366 | 0.9408 |
| 1.0976        | 2.0   | 500   | 1.0013          | 0.0838   | 0.9335     | 4.2665 | 0.0838   | 0.0443   | 0.0391 | 0.9208 |
| 1.0976        | 3.0   | 750   | 0.9171          | 0.1335   | 0.9308     | 2.8791 | 0.1335   | 0.0985   | 0.0770 | 0.8928 |
| 0.9312        | 4.0   | 1000  | 0.8701          | 0.1822   | 0.9243     | 2.7464 | 0.1822   | 0.1497   | 0.1142 | 0.8582 |
| 0.9312        | 5.0   | 1250  | 0.8306          | 0.274    | 0.8635     | 5.8805 | 0.274    | 0.2059   | 0.1347 | 0.6733 |
| 0.8353        | 6.0   | 1500  | 0.7791          | 0.396    | 0.7897     | 5.0905 | 0.396    | 0.3620   | 0.1762 | 0.4569 |
| 0.8353        | 7.0   | 1750  | 0.7452          | 0.47     | 0.7200     | 4.3882 | 0.47     | 0.4357   | 0.1822 | 0.3485 |
| 0.7569        | 8.0   | 2000  | 0.7148          | 0.5635   | 0.6470     | 3.6418 | 0.5635   | 0.5444   | 0.2022 | 0.2564 |
| 0.7569        | 9.0   | 2250  | 0.6847          | 0.6092   | 0.5626     | 3.0490 | 0.6092   | 0.5904   | 0.1508 | 0.1932 |
| 0.6953        | 10.0  | 2500  | 0.6552          | 0.648    | 0.5117     | 2.7913 | 0.648    | 0.6309   | 0.1312 | 0.1622 |
| 0.6953        | 11.0  | 2750  | 0.6369          | 0.662    | 0.4778     | 2.6400 | 0.662    | 0.6468   | 0.0959 | 0.1471 |
| 0.6357        | 12.0  | 3000  | 0.6074          | 0.6863   | 0.4436     | 2.4974 | 0.6863   | 0.6724   | 0.0734 | 0.1274 |
| 0.6357        | 13.0  | 3250  | 0.5915          | 0.6975   | 0.4226     | 2.4214 | 0.6975   | 0.6843   | 0.0607 | 0.1173 |
| 0.5943        | 14.0  | 3500  | 0.5811          | 0.7055   | 0.4080     | 2.3606 | 0.7055   | 0.6923   | 0.0487 | 0.1093 |
| 0.5943        | 15.0  | 3750  | 0.5694          | 0.7177   | 0.3947     | 2.2689 | 0.7178   | 0.7087   | 0.0553 | 0.1016 |
| 0.5665        | 16.0  | 4000  | 0.5555          | 0.7225   | 0.3866     | 2.2797 | 0.7225   | 0.7130   | 0.0394 | 0.0981 |
| 0.5665        | 17.0  | 4250  | 0.5502          | 0.725    | 0.3821     | 2.2616 | 0.7250   | 0.7166   | 0.0441 | 0.0957 |
| 0.5446        | 18.0  | 4500  | 0.5425          | 0.7345   | 0.3704     | 2.1992 | 0.7345   | 0.7277   | 0.0401 | 0.0893 |
| 0.5446        | 19.0  | 4750  | 0.5325          | 0.731    | 0.3670     | 2.1856 | 0.731    | 0.7257   | 0.0401 | 0.0872 |
| 0.5268        | 20.0  | 5000  | 0.5272          | 0.738    | 0.3661     | 2.2345 | 0.738    | 0.7335   | 0.0467 | 0.0865 |
| 0.5268        | 21.0  | 5250  | 0.5199          | 0.745    | 0.3582     | 2.1676 | 0.745    | 0.7407   | 0.0388 | 0.0827 |
| 0.5107        | 22.0  | 5500  | 0.5146          | 0.748    | 0.3530     | 2.1726 | 0.748    | 0.7446   | 0.0417 | 0.0802 |
| 0.5107        | 23.0  | 5750  | 0.5101          | 0.7482   | 0.3516     | 2.1670 | 0.7482   | 0.7445   | 0.0398 | 0.0799 |
| 0.4973        | 24.0  | 6000  | 0.5076          | 0.7455   | 0.3533     | 2.1814 | 0.7455   | 0.7431   | 0.0396 | 0.0807 |
| 0.4973        | 25.0  | 6250  | 0.4971          | 0.7512   | 0.3476     | 2.1618 | 0.7513   | 0.7469   | 0.0414 | 0.0780 |
| 0.484         | 26.0  | 6500  | 0.4934          | 0.753    | 0.3464     | 2.1725 | 0.753    | 0.7497   | 0.0473 | 0.0780 |
| 0.484         | 27.0  | 6750  | 0.4916          | 0.756    | 0.3415     | 2.1408 | 0.756    | 0.7527   | 0.0480 | 0.0753 |
| 0.4709        | 28.0  | 7000  | 0.4886          | 0.7582   | 0.3405     | 2.1415 | 0.7582   | 0.7547   | 0.0410 | 0.0746 |
| 0.4709        | 29.0  | 7250  | 0.4844          | 0.7582   | 0.3377     | 2.1252 | 0.7582   | 0.7556   | 0.0483 | 0.0742 |
| 0.4617        | 30.0  | 7500  | 0.4831          | 0.757    | 0.3372     | 2.1383 | 0.757    | 0.7540   | 0.0425 | 0.0731 |
| 0.4617        | 31.0  | 7750  | 0.4781          | 0.759    | 0.3344     | 2.1035 | 0.7590   | 0.7572   | 0.0404 | 0.0718 |
| 0.4529        | 32.0  | 8000  | 0.4794          | 0.7562   | 0.3375     | 2.1457 | 0.7562   | 0.7545   | 0.0385 | 0.0731 |
| 0.4529        | 33.0  | 8250  | 0.4777          | 0.7625   | 0.3336     | 2.0834 | 0.7625   | 0.7607   | 0.0433 | 0.0717 |
| 0.4462        | 34.0  | 8500  | 0.4730          | 0.7598   | 0.3328     | 2.1058 | 0.7598   | 0.7566   | 0.0496 | 0.0716 |
| 0.4462        | 35.0  | 8750  | 0.4730          | 0.761    | 0.3324     | 2.0874 | 0.761    | 0.7600   | 0.0461 | 0.0712 |
| 0.4404        | 36.0  | 9000  | 0.4692          | 0.7635   | 0.3309     | 2.0914 | 0.7635   | 0.7616   | 0.0481 | 0.0703 |
| 0.4404        | 37.0  | 9250  | 0.4691          | 0.7618   | 0.3298     | 2.0866 | 0.7618   | 0.7598   | 0.0457 | 0.0703 |
| 0.4351        | 38.0  | 9500  | 0.4666          | 0.762    | 0.3294     | 2.0963 | 0.762    | 0.7593   | 0.0428 | 0.0700 |
| 0.4351        | 39.0  | 9750  | 0.4639          | 0.7668   | 0.3265     | 2.1028 | 0.7668   | 0.7652   | 0.0453 | 0.0688 |
| 0.4309        | 40.0  | 10000 | 0.4627          | 0.7675   | 0.3287     | 2.0981 | 0.7675   | 0.7658   | 0.0449 | 0.0694 |
| 0.4309        | 41.0  | 10250 | 0.4634          | 0.765    | 0.3264     | 2.1151 | 0.765    | 0.7631   | 0.0441 | 0.0684 |
| 0.4269        | 42.0  | 10500 | 0.4626          | 0.7658   | 0.3260     | 2.0977 | 0.7658   | 0.7644   | 0.0414 | 0.0684 |
| 0.4269        | 43.0  | 10750 | 0.4609          | 0.7672   | 0.3259     | 2.0944 | 0.7672   | 0.7656   | 0.0420 | 0.0681 |
| 0.4248        | 44.0  | 11000 | 0.4616          | 0.7662   | 0.3253     | 2.0942 | 0.7663   | 0.7652   | 0.0458 | 0.0678 |
| 0.4248        | 45.0  | 11250 | 0.4605          | 0.7658   | 0.3258     | 2.1447 | 0.7658   | 0.7629   | 0.0408 | 0.0678 |
| 0.4233        | 46.0  | 11500 | 0.4604          | 0.7662   | 0.3266     | 2.1007 | 0.7663   | 0.7640   | 0.0493 | 0.0686 |
| 0.4233        | 47.0  | 11750 | 0.4601          | 0.7652   | 0.3252     | 2.0893 | 0.7652   | 0.7633   | 0.0463 | 0.0684 |
| 0.4221        | 48.0  | 12000 | 0.4600          | 0.7645   | 0.3255     | 2.0695 | 0.7645   | 0.7629   | 0.0472 | 0.0683 |
| 0.4221        | 49.0  | 12250 | 0.4605          | 0.7662   | 0.3257     | 2.0778 | 0.7663   | 0.7640   | 0.0425 | 0.0682 |
| 0.4211        | 50.0  | 12500 | 0.4602          | 0.769    | 0.3252     | 2.1002 | 0.769    | 0.7667   | 0.0388 | 0.0678 |


### Framework versions

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

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