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# dit-base-finetuned-rvlcdip-small_rvl_cdip-NK1000_simkd

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: 0.0864
- Accuracy: 0.842
- Brier Loss: 0.4773
- Nll: 1.3055
- F1 Micro: 0.842
- F1 Macro: 0.8427
- Ece: 0.4404
- Aurc: 0.0865

## 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: 16
- eval_batch_size: 16
- 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   |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 0.1387        | 1.0   | 1000  | 0.1293          | 0.4412   | 0.8613     | 3.7912 | 0.4412   | 0.3863   | 0.3246 | 0.3321 |
| 0.1154        | 2.0   | 2000  | 0.1132          | 0.668    | 0.7441     | 2.1523 | 0.668    | 0.6478   | 0.4699 | 0.1467 |
| 0.1074        | 3.0   | 3000  | 0.1075          | 0.73     | 0.7150     | 1.9686 | 0.7300   | 0.7323   | 0.5177 | 0.1104 |
| 0.1036        | 4.0   | 4000  | 0.1068          | 0.7258   | 0.6943     | 2.0361 | 0.7258   | 0.7299   | 0.4903 | 0.1318 |
| 0.0996        | 5.0   | 5000  | 0.1047          | 0.742    | 0.6897     | 2.1166 | 0.7420   | 0.7456   | 0.5061 | 0.1124 |
| 0.0967        | 6.0   | 6000  | 0.0992          | 0.78     | 0.6307     | 1.8492 | 0.78     | 0.7875   | 0.5018 | 0.0924 |
| 0.0923        | 7.0   | 7000  | 0.0985          | 0.774    | 0.6055     | 2.0449 | 0.774    | 0.7800   | 0.4698 | 0.1056 |
| 0.0893        | 8.0   | 8000  | 0.0982          | 0.7817   | 0.6012     | 1.9476 | 0.7817   | 0.7814   | 0.4696 | 0.1243 |
| 0.0871        | 9.0   | 9000  | 0.0954          | 0.8043   | 0.5826     | 1.7573 | 0.8043   | 0.8065   | 0.4925 | 0.0811 |
| 0.0857        | 10.0  | 10000 | 0.0969          | 0.784    | 0.5672     | 2.0878 | 0.7840   | 0.7846   | 0.4417 | 0.1076 |
| 0.083         | 11.0  | 11000 | 0.0934          | 0.8067   | 0.5658     | 1.6628 | 0.8067   | 0.8062   | 0.4809 | 0.0788 |
| 0.0819        | 12.0  | 12000 | 0.0930          | 0.8027   | 0.5499     | 1.6718 | 0.8027   | 0.8035   | 0.4592 | 0.0851 |
| 0.081         | 13.0  | 13000 | 0.0937          | 0.7957   | 0.5579     | 1.7282 | 0.7957   | 0.7956   | 0.4544 | 0.0948 |
| 0.079         | 14.0  | 14000 | 0.0939          | 0.794    | 0.5463     | 1.9406 | 0.7940   | 0.7974   | 0.4344 | 0.1142 |
| 0.0779        | 15.0  | 15000 | 0.0912          | 0.81     | 0.5206     | 1.6776 | 0.81     | 0.8141   | 0.4371 | 0.0947 |
| 0.0767        | 16.0  | 16000 | 0.0905          | 0.813    | 0.5165     | 1.6744 | 0.813    | 0.8140   | 0.4383 | 0.0864 |
| 0.0766        | 17.0  | 17000 | 0.0911          | 0.8113   | 0.5239     | 1.7109 | 0.8113   | 0.8104   | 0.4428 | 0.0902 |
| 0.0762        | 18.0  | 18000 | 0.0914          | 0.8093   | 0.5153     | 1.6778 | 0.8093   | 0.8098   | 0.4290 | 0.0998 |
| 0.0759        | 19.0  | 19000 | 0.0904          | 0.8163   | 0.5076     | 1.6946 | 0.8163   | 0.8178   | 0.4333 | 0.0939 |
| 0.075         | 20.0  | 20000 | 0.0897          | 0.8133   | 0.5062     | 1.5892 | 0.8133   | 0.8155   | 0.4300 | 0.0898 |
| 0.0743        | 21.0  | 21000 | 0.0895          | 0.8147   | 0.5058     | 1.5900 | 0.8148   | 0.8149   | 0.4315 | 0.0917 |
| 0.0745        | 22.0  | 22000 | 0.0898          | 0.8157   | 0.5014     | 1.5523 | 0.8157   | 0.8164   | 0.4287 | 0.0848 |
| 0.0737        | 23.0  | 23000 | 0.0901          | 0.8127   | 0.5038     | 1.6625 | 0.8128   | 0.8146   | 0.4219 | 0.0978 |
| 0.0735        | 24.0  | 24000 | 0.0907          | 0.8117   | 0.5082     | 1.6475 | 0.8117   | 0.8133   | 0.4231 | 0.1064 |
| 0.0732        | 25.0  | 25000 | 0.0901          | 0.8103   | 0.5041     | 1.6830 | 0.8103   | 0.8105   | 0.4187 | 0.1017 |
| 0.0727        | 26.0  | 26000 | 0.0899          | 0.8135   | 0.5015     | 1.6499 | 0.8135   | 0.8170   | 0.4197 | 0.1020 |
| 0.0722        | 27.0  | 27000 | 0.0880          | 0.8265   | 0.4931     | 1.4651 | 0.8265   | 0.8273   | 0.4330 | 0.0975 |
| 0.0718        | 28.0  | 28000 | 0.0876          | 0.8263   | 0.4917     | 1.4213 | 0.8263   | 0.8275   | 0.4354 | 0.0858 |
| 0.0725        | 29.0  | 29000 | 0.0891          | 0.8247   | 0.4930     | 1.5581 | 0.8247   | 0.8254   | 0.4288 | 0.0946 |
| 0.0717        | 30.0  | 30000 | 0.0879          | 0.8327   | 0.4913     | 1.4417 | 0.8327   | 0.8326   | 0.4403 | 0.0888 |
| 0.0715        | 31.0  | 31000 | 0.0872          | 0.8375   | 0.4866     | 1.3775 | 0.8375   | 0.8389   | 0.4435 | 0.0872 |
| 0.0715        | 32.0  | 32000 | 0.0884          | 0.8297   | 0.4915     | 1.5136 | 0.8297   | 0.8305   | 0.4331 | 0.0946 |
| 0.0717        | 33.0  | 33000 | 0.0877          | 0.8347   | 0.4851     | 1.4096 | 0.8347   | 0.8347   | 0.4375 | 0.0845 |
| 0.0716        | 34.0  | 34000 | 0.0880          | 0.8323   | 0.4866     | 1.4547 | 0.8323   | 0.8333   | 0.4323 | 0.0926 |
| 0.0713        | 35.0  | 35000 | 0.0873          | 0.8343   | 0.4833     | 1.3884 | 0.8343   | 0.8351   | 0.4375 | 0.0810 |
| 0.0713        | 36.0  | 36000 | 0.0873          | 0.8365   | 0.4843     | 1.4168 | 0.8365   | 0.8372   | 0.4381 | 0.0913 |
| 0.071         | 37.0  | 37000 | 0.0871          | 0.8393   | 0.4831     | 1.3524 | 0.8393   | 0.8399   | 0.4412 | 0.0882 |
| 0.0709        | 38.0  | 38000 | 0.0877          | 0.834    | 0.4862     | 1.4457 | 0.834    | 0.8353   | 0.4371 | 0.0929 |
| 0.071         | 39.0  | 39000 | 0.0870          | 0.836    | 0.4811     | 1.3954 | 0.836    | 0.8367   | 0.4360 | 0.0886 |
| 0.0708        | 40.0  | 40000 | 0.0867          | 0.8387   | 0.4800     | 1.3687 | 0.8387   | 0.8403   | 0.4390 | 0.0867 |
| 0.0706        | 41.0  | 41000 | 0.0866          | 0.8395   | 0.4802     | 1.3464 | 0.8395   | 0.8399   | 0.4412 | 0.0860 |
| 0.0708        | 42.0  | 42000 | 0.0868          | 0.8363   | 0.4796     | 1.3828 | 0.8363   | 0.8371   | 0.4345 | 0.0886 |
| 0.0709        | 43.0  | 43000 | 0.0866          | 0.838    | 0.4790     | 1.3503 | 0.838    | 0.8390   | 0.4382 | 0.0860 |
| 0.0702        | 44.0  | 44000 | 0.0866          | 0.8415   | 0.4787     | 1.3679 | 0.8415   | 0.8425   | 0.4403 | 0.0899 |
| 0.0703        | 45.0  | 45000 | 0.0866          | 0.8373   | 0.4788     | 1.3192 | 0.8373   | 0.8379   | 0.4374 | 0.0863 |
| 0.0702        | 46.0  | 46000 | 0.0865          | 0.841    | 0.4776     | 1.3357 | 0.841    | 0.8417   | 0.4398 | 0.0871 |
| 0.0703        | 47.0  | 47000 | 0.0864          | 0.8417   | 0.4772     | 1.3302 | 0.8417   | 0.8424   | 0.4406 | 0.0859 |
| 0.0705        | 48.0  | 48000 | 0.0865          | 0.841    | 0.4776     | 1.3096 | 0.841    | 0.8417   | 0.4398 | 0.0877 |
| 0.0703        | 49.0  | 49000 | 0.0864          | 0.8413   | 0.4775     | 1.3022 | 0.8413   | 0.8419   | 0.4400 | 0.0865 |
| 0.0703        | 50.0  | 50000 | 0.0864          | 0.842    | 0.4773     | 1.3055 | 0.842    | 0.8427   | 0.4404 | 0.0865 |


### Framework versions

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

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