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# resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_kd_NKD_t1.0_g1.5_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: 3.0688
- Accuracy: 0.768
- Brier Loss: 0.3296
- Nll: 1.9490
- F1 Micro: 0.768
- F1 Macro: 0.7688
- Ece: 0.0595
- Aurc: 0.0737

## 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   | 5.5798          | 0.1893   | 0.8757     | 3.8790 | 0.1893   | 0.1519   | 0.0656 | 0.6360 |
| 5.6792        | 2.0   | 500   | 5.5256          | 0.2642   | 0.8415     | 4.3054 | 0.2642   | 0.2235   | 0.0705 | 0.5478 |
| 5.6792        | 3.0   | 750   | 5.3875          | 0.3157   | 0.8213     | 3.4839 | 0.3157   | 0.2920   | 0.0835 | 0.5208 |
| 4.8939        | 4.0   | 1000  | 4.4870          | 0.473    | 0.6754     | 2.6509 | 0.473    | 0.4671   | 0.0728 | 0.3199 |
| 4.8939        | 5.0   | 1250  | 4.4320          | 0.5015   | 0.6345     | 2.7717 | 0.5015   | 0.4779   | 0.0577 | 0.2773 |
| 4.3006        | 6.0   | 1500  | 4.2399          | 0.5435   | 0.5938     | 2.6489 | 0.5435   | 0.5334   | 0.0527 | 0.2353 |
| 4.3006        | 7.0   | 1750  | 3.8189          | 0.6155   | 0.5111     | 2.4587 | 0.6155   | 0.6096   | 0.0725 | 0.1646 |
| 3.6984        | 8.0   | 2000  | 3.5238          | 0.6795   | 0.4476     | 2.2085 | 0.6795   | 0.6810   | 0.0712 | 0.1258 |
| 3.6984        | 9.0   | 2250  | 3.4654          | 0.6877   | 0.4287     | 2.2234 | 0.6877   | 0.6852   | 0.0511 | 0.1198 |
| 3.32          | 10.0  | 2500  | 3.4769          | 0.692    | 0.4253     | 2.2586 | 0.692    | 0.6868   | 0.0456 | 0.1164 |
| 3.32          | 11.0  | 2750  | 3.3473          | 0.7235   | 0.3967     | 2.1360 | 0.7235   | 0.7245   | 0.0518 | 0.1063 |
| 3.0488        | 12.0  | 3000  | 3.3891          | 0.712    | 0.3929     | 2.1461 | 0.712    | 0.7100   | 0.0491 | 0.1043 |
| 3.0488        | 13.0  | 3250  | 3.3123          | 0.7208   | 0.3846     | 2.1236 | 0.7208   | 0.7171   | 0.0444 | 0.0958 |
| 2.8727        | 14.0  | 3500  | 3.3357          | 0.7147   | 0.3877     | 2.0928 | 0.7147   | 0.7170   | 0.0489 | 0.1000 |
| 2.8727        | 15.0  | 3750  | 3.2576          | 0.7318   | 0.3703     | 2.1052 | 0.7318   | 0.7358   | 0.0473 | 0.0901 |
| 2.7552        | 16.0  | 4000  | 3.2528          | 0.738    | 0.3650     | 2.0968 | 0.738    | 0.7396   | 0.0477 | 0.0877 |
| 2.7552        | 17.0  | 4250  | 3.4241          | 0.7093   | 0.4120     | 2.1652 | 0.7093   | 0.7078   | 0.0540 | 0.1114 |
| 2.6718        | 18.0  | 4500  | 3.2877          | 0.7358   | 0.3686     | 2.0587 | 0.7358   | 0.7362   | 0.0517 | 0.0899 |
| 2.6718        | 19.0  | 4750  | 3.2690          | 0.7375   | 0.3650     | 2.0509 | 0.7375   | 0.7409   | 0.0527 | 0.0891 |
| 2.6158        | 20.0  | 5000  | 3.2650          | 0.7398   | 0.3629     | 2.0803 | 0.7398   | 0.7432   | 0.0491 | 0.0889 |
| 2.6158        | 21.0  | 5250  | 3.2507          | 0.7452   | 0.3599     | 2.0773 | 0.7452   | 0.7467   | 0.0577 | 0.0859 |
| 2.5766        | 22.0  | 5500  | 3.1737          | 0.7525   | 0.3481     | 2.0408 | 0.7525   | 0.7539   | 0.0557 | 0.0815 |
| 2.5766        | 23.0  | 5750  | 3.1625          | 0.7585   | 0.3478     | 2.0313 | 0.7585   | 0.7599   | 0.0587 | 0.0813 |
| 2.5388        | 24.0  | 6000  | 3.2357          | 0.746    | 0.3563     | 2.0384 | 0.746    | 0.7445   | 0.0592 | 0.0838 |
| 2.5388        | 25.0  | 6250  | 3.1653          | 0.761    | 0.3441     | 2.0389 | 0.761    | 0.7605   | 0.0423 | 0.0783 |
| 2.5129        | 26.0  | 6500  | 3.1662          | 0.7532   | 0.3468     | 2.0033 | 0.7532   | 0.7556   | 0.0594 | 0.0805 |
| 2.5129        | 27.0  | 6750  | 3.1224          | 0.7632   | 0.3384     | 1.9745 | 0.7632   | 0.7624   | 0.0537 | 0.0773 |
| 2.4881        | 28.0  | 7000  | 3.2460          | 0.7458   | 0.3618     | 2.0745 | 0.7458   | 0.7479   | 0.0521 | 0.0868 |
| 2.4881        | 29.0  | 7250  | 3.1299          | 0.7605   | 0.3414     | 1.9781 | 0.7605   | 0.7626   | 0.0600 | 0.0774 |
| 2.469         | 30.0  | 7500  | 3.1695          | 0.7555   | 0.3481     | 2.0246 | 0.7555   | 0.7563   | 0.0534 | 0.0811 |
| 2.469         | 31.0  | 7750  | 3.1766          | 0.7612   | 0.3474     | 1.9997 | 0.7612   | 0.7633   | 0.0541 | 0.0803 |
| 2.4524        | 32.0  | 8000  | 3.0937          | 0.7638   | 0.3351     | 1.9420 | 0.7638   | 0.7649   | 0.0592 | 0.0754 |
| 2.4524        | 33.0  | 8250  | 3.1293          | 0.7625   | 0.3409     | 1.9671 | 0.7625   | 0.7633   | 0.0580 | 0.0781 |
| 2.4382        | 34.0  | 8500  | 3.1129          | 0.7668   | 0.3370     | 2.0003 | 0.7668   | 0.7672   | 0.0623 | 0.0746 |
| 2.4382        | 35.0  | 8750  | 3.0795          | 0.767    | 0.3324     | 1.9772 | 0.767    | 0.7677   | 0.0536 | 0.0759 |
| 2.4259        | 36.0  | 9000  | 3.0927          | 0.7675   | 0.3332     | 1.9557 | 0.7675   | 0.7690   | 0.0588 | 0.0744 |
| 2.4259        | 37.0  | 9250  | 3.0856          | 0.7702   | 0.3327     | 1.9465 | 0.7702   | 0.7715   | 0.0554 | 0.0756 |
| 2.4107        | 38.0  | 9500  | 3.0915          | 0.7678   | 0.3319     | 1.9699 | 0.7678   | 0.7681   | 0.0556 | 0.0749 |
| 2.4107        | 39.0  | 9750  | 3.0885          | 0.763    | 0.3338     | 1.9478 | 0.763    | 0.7643   | 0.0575 | 0.0750 |
| 2.4002        | 40.0  | 10000 | 3.0921          | 0.771    | 0.3315     | 1.9563 | 0.771    | 0.7729   | 0.0557 | 0.0744 |
| 2.4002        | 41.0  | 10250 | 3.0727          | 0.767    | 0.3310     | 1.9530 | 0.767    | 0.7682   | 0.0567 | 0.0748 |
| 2.3905        | 42.0  | 10500 | 3.0793          | 0.7645   | 0.3320     | 1.9484 | 0.7645   | 0.7657   | 0.0598 | 0.0755 |
| 2.3905        | 43.0  | 10750 | 3.0771          | 0.7672   | 0.3308     | 1.9548 | 0.7672   | 0.7679   | 0.0566 | 0.0737 |
| 2.3798        | 44.0  | 11000 | 3.0794          | 0.7685   | 0.3309     | 1.9620 | 0.7685   | 0.7693   | 0.0631 | 0.0736 |
| 2.3798        | 45.0  | 11250 | 3.0736          | 0.7665   | 0.3320     | 1.9408 | 0.7665   | 0.7677   | 0.0589 | 0.0749 |
| 2.3731        | 46.0  | 11500 | 3.0746          | 0.7682   | 0.3312     | 1.9635 | 0.7682   | 0.7693   | 0.0576 | 0.0743 |
| 2.3731        | 47.0  | 11750 | 3.0711          | 0.768    | 0.3306     | 1.9576 | 0.768    | 0.7689   | 0.0572 | 0.0739 |
| 2.3671        | 48.0  | 12000 | 3.0785          | 0.7682   | 0.3317     | 1.9516 | 0.7682   | 0.7697   | 0.0574 | 0.0744 |
| 2.3671        | 49.0  | 12250 | 3.0678          | 0.7692   | 0.3298     | 1.9388 | 0.7692   | 0.7700   | 0.0606 | 0.0738 |
| 2.3628        | 50.0  | 12500 | 3.0688          | 0.768    | 0.3296     | 1.9490 | 0.768    | 0.7688   | 0.0595 | 0.0737 |


### Framework versions

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

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