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# dit-base-finetuned-rvlcdip-small_rvl_cdip-NK1000_kd_MSE

This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3468
- Accuracy: 0.8335
- Brier Loss: 0.2611
- Nll: 1.2696
- F1 Micro: 0.8335
- F1 Macro: 0.8338
- Ece: 0.0865
- Aurc: 0.0606

## 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: 96
- eval_batch_size: 96
- 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   | 167  | 0.9313          | 0.5955   | 0.5896     | 2.2535 | 0.5955   | 0.5760   | 0.2103 | 0.1770 |
| No log        | 2.0   | 334  | 0.6941          | 0.7027   | 0.4541     | 1.7696 | 0.7027   | 0.6962   | 0.1756 | 0.1075 |
| 0.9587        | 3.0   | 501  | 0.5806          | 0.7435   | 0.3758     | 1.8114 | 0.7435   | 0.7489   | 0.1225 | 0.0802 |
| 0.9587        | 4.0   | 668  | 0.4847          | 0.7808   | 0.3232     | 1.6093 | 0.7808   | 0.7825   | 0.0897 | 0.0600 |
| 0.9587        | 5.0   | 835  | 0.4888          | 0.775    | 0.3291     | 1.7398 | 0.775    | 0.7686   | 0.0629 | 0.0681 |
| 0.3668        | 6.0   | 1002 | 0.4441          | 0.789    | 0.3136     | 1.5502 | 0.7890   | 0.7882   | 0.0720 | 0.0626 |
| 0.3668        | 7.0   | 1169 | 0.4258          | 0.7993   | 0.3009     | 1.6320 | 0.7993   | 0.7994   | 0.0626 | 0.0658 |
| 0.3668        | 8.0   | 1336 | 0.4813          | 0.774    | 0.3395     | 1.8387 | 0.774    | 0.7788   | 0.0716 | 0.0811 |
| 0.1959        | 9.0   | 1503 | 0.4289          | 0.7967   | 0.3111     | 1.7125 | 0.7967   | 0.7985   | 0.0799 | 0.0686 |
| 0.1959        | 10.0  | 1670 | 0.4380          | 0.7897   | 0.3151     | 1.8001 | 0.7897   | 0.7920   | 0.0802 | 0.0730 |
| 0.1959        | 11.0  | 1837 | 0.4414          | 0.796    | 0.3159     | 1.7450 | 0.796    | 0.7956   | 0.0795 | 0.0815 |
| 0.1249        | 12.0  | 2004 | 0.4507          | 0.787    | 0.3243     | 1.7219 | 0.787    | 0.7840   | 0.0841 | 0.0767 |
| 0.1249        | 13.0  | 2171 | 0.4209          | 0.802    | 0.3095     | 1.7111 | 0.802    | 0.8045   | 0.0825 | 0.0729 |
| 0.1249        | 14.0  | 2338 | 0.4095          | 0.8007   | 0.3039     | 1.5961 | 0.8007   | 0.8018   | 0.0742 | 0.0743 |
| 0.088         | 15.0  | 2505 | 0.4043          | 0.8125   | 0.2974     | 1.6100 | 0.8125   | 0.8158   | 0.0801 | 0.0740 |
| 0.088         | 16.0  | 2672 | 0.4056          | 0.8083   | 0.2964     | 1.6402 | 0.8083   | 0.8080   | 0.0833 | 0.0681 |
| 0.088         | 17.0  | 2839 | 0.4052          | 0.8103   | 0.2993     | 1.6074 | 0.8103   | 0.8105   | 0.0848 | 0.0780 |
| 0.0638        | 18.0  | 3006 | 0.4207          | 0.8035   | 0.3066     | 1.6669 | 0.8035   | 0.8075   | 0.0826 | 0.0746 |
| 0.0638        | 19.0  | 3173 | 0.3981          | 0.8125   | 0.2911     | 1.5687 | 0.8125   | 0.8128   | 0.0836 | 0.0762 |
| 0.0638        | 20.0  | 3340 | 0.3828          | 0.8207   | 0.2803     | 1.5513 | 0.8207   | 0.8217   | 0.0800 | 0.0627 |
| 0.0456        | 21.0  | 3507 | 0.3710          | 0.821    | 0.2802     | 1.4355 | 0.821    | 0.8218   | 0.0913 | 0.0662 |
| 0.0456        | 22.0  | 3674 | 0.3672          | 0.8247   | 0.2744     | 1.4922 | 0.8247   | 0.8280   | 0.0774 | 0.0615 |
| 0.0456        | 23.0  | 3841 | 0.3600          | 0.8255   | 0.2727     | 1.4413 | 0.8255   | 0.8256   | 0.0817 | 0.0675 |
| 0.0289        | 24.0  | 4008 | 0.3650          | 0.8235   | 0.2767     | 1.3874 | 0.8235   | 0.8248   | 0.0818 | 0.0698 |
| 0.0289        | 25.0  | 4175 | 0.3608          | 0.827    | 0.2706     | 1.3223 | 0.827    | 0.8279   | 0.0861 | 0.0597 |
| 0.0289        | 26.0  | 4342 | 0.3572          | 0.829    | 0.2687     | 1.3947 | 0.8290   | 0.8300   | 0.0878 | 0.0650 |
| 0.0176        | 27.0  | 4509 | 0.3516          | 0.8315   | 0.2655     | 1.3000 | 0.8315   | 0.8319   | 0.0866 | 0.0597 |
| 0.0176        | 28.0  | 4676 | 0.3455          | 0.8337   | 0.2626     | 1.3070 | 0.8337   | 0.8351   | 0.0870 | 0.0602 |
| 0.0176        | 29.0  | 4843 | 0.3489          | 0.8337   | 0.2656     | 1.3027 | 0.8337   | 0.8347   | 0.0859 | 0.0587 |
| 0.011         | 30.0  | 5010 | 0.3472          | 0.8327   | 0.2639     | 1.2879 | 0.8327   | 0.8336   | 0.0878 | 0.0599 |
| 0.011         | 31.0  | 5177 | 0.3468          | 0.8335   | 0.2642     | 1.2955 | 0.8335   | 0.8341   | 0.0859 | 0.0650 |
| 0.011         | 32.0  | 5344 | 0.3467          | 0.8333   | 0.2635     | 1.2911 | 0.8333   | 0.8341   | 0.0849 | 0.0588 |
| 0.0076        | 33.0  | 5511 | 0.3430          | 0.834    | 0.2601     | 1.2738 | 0.834    | 0.8346   | 0.0831 | 0.0609 |
| 0.0076        | 34.0  | 5678 | 0.3442          | 0.8345   | 0.2626     | 1.2921 | 0.8345   | 0.8353   | 0.0864 | 0.0629 |
| 0.0076        | 35.0  | 5845 | 0.3431          | 0.8355   | 0.2596     | 1.2790 | 0.8355   | 0.8362   | 0.0860 | 0.0589 |
| 0.0055        | 36.0  | 6012 | 0.3496          | 0.8297   | 0.2646     | 1.2985 | 0.8297   | 0.8305   | 0.0897 | 0.0642 |
| 0.0055        | 37.0  | 6179 | 0.3445          | 0.8343   | 0.2605     | 1.2509 | 0.8343   | 0.8348   | 0.0862 | 0.0594 |
| 0.0055        | 38.0  | 6346 | 0.3473          | 0.831    | 0.2628     | 1.2919 | 0.831    | 0.8314   | 0.0881 | 0.0616 |
| 0.0041        | 39.0  | 6513 | 0.3445          | 0.8325   | 0.2625     | 1.2894 | 0.8325   | 0.8330   | 0.0880 | 0.0619 |
| 0.0041        | 40.0  | 6680 | 0.3462          | 0.8317   | 0.2614     | 1.2840 | 0.8317   | 0.8323   | 0.0844 | 0.0599 |
| 0.0041        | 41.0  | 6847 | 0.3437          | 0.833    | 0.2602     | 1.2694 | 0.833    | 0.8336   | 0.0871 | 0.0598 |
| 0.003         | 42.0  | 7014 | 0.3456          | 0.8347   | 0.2605     | 1.2867 | 0.8347   | 0.8352   | 0.0844 | 0.0615 |
| 0.003         | 43.0  | 7181 | 0.3454          | 0.8347   | 0.2607     | 1.2844 | 0.8347   | 0.8354   | 0.0868 | 0.0598 |
| 0.003         | 44.0  | 7348 | 0.3451          | 0.8337   | 0.2599     | 1.2719 | 0.8337   | 0.8342   | 0.0832 | 0.0595 |
| 0.0022        | 45.0  | 7515 | 0.3460          | 0.8343   | 0.2607     | 1.2750 | 0.8343   | 0.8346   | 0.0844 | 0.0591 |
| 0.0022        | 46.0  | 7682 | 0.3461          | 0.8325   | 0.2607     | 1.2774 | 0.8325   | 0.8328   | 0.0861 | 0.0604 |
| 0.0022        | 47.0  | 7849 | 0.3465          | 0.8335   | 0.2610     | 1.2776 | 0.8335   | 0.8338   | 0.0834 | 0.0600 |
| 0.0018        | 48.0  | 8016 | 0.3468          | 0.8327   | 0.2609     | 1.2758 | 0.8327   | 0.8331   | 0.0865 | 0.0605 |
| 0.0018        | 49.0  | 8183 | 0.3466          | 0.8333   | 0.2610     | 1.2724 | 0.8333   | 0.8336   | 0.0858 | 0.0609 |
| 0.0018        | 50.0  | 8350 | 0.3468          | 0.8335   | 0.2611     | 1.2696 | 0.8335   | 0.8338   | 0.0865 | 0.0606 |


### Framework versions

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

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