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# dit-base-finetuned-rvlcdip-tiny_rvl_cdip-NK1000_simkd

This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0914
- Accuracy: 0.8157
- Brier Loss: 0.5152
- Nll: 1.5529
- F1 Micro: 0.8157
- F1 Macro: 0.8167
- Ece: 0.4370
- Aurc: 0.1010

## 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   |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 0.149         | 1.0   | 1000  | 0.1482          | 0.1598   | 0.9334     | 6.4910 | 0.1598   | 0.1128   | 0.0991 | 0.7250 |
| 0.1278        | 2.0   | 2000  | 0.1248          | 0.5295   | 0.8391     | 3.1275 | 0.5295   | 0.4951   | 0.4011 | 0.2355 |
| 0.1156        | 3.0   | 3000  | 0.1146          | 0.6388   | 0.7532     | 2.4008 | 0.6388   | 0.6238   | 0.4480 | 0.1552 |
| 0.1094        | 4.0   | 4000  | 0.1089          | 0.7085   | 0.7069     | 2.1449 | 0.7085   | 0.7039   | 0.4850 | 0.1225 |
| 0.105         | 5.0   | 5000  | 0.1063          | 0.7252   | 0.6970     | 2.0868 | 0.7252   | 0.7291   | 0.4969 | 0.1087 |
| 0.1011        | 6.0   | 6000  | 0.1038          | 0.7472   | 0.6562     | 2.1759 | 0.7472   | 0.7478   | 0.4844 | 0.1095 |
| 0.097         | 7.0   | 7000  | 0.1043          | 0.7415   | 0.6509     | 2.5078 | 0.7415   | 0.7404   | 0.4668 | 0.1364 |
| 0.0946        | 8.0   | 8000  | 0.1022          | 0.7508   | 0.6416     | 2.1930 | 0.7508   | 0.7550   | 0.4714 | 0.1300 |
| 0.0916        | 9.0   | 9000  | 0.0995          | 0.7642   | 0.6271     | 1.9398 | 0.7642   | 0.7691   | 0.4791 | 0.1086 |
| 0.0901        | 10.0  | 10000 | 0.1013          | 0.747    | 0.6277     | 2.3724 | 0.747    | 0.7538   | 0.4538 | 0.1227 |
| 0.0881        | 11.0  | 11000 | 0.0991          | 0.7752   | 0.6037     | 1.9848 | 0.7752   | 0.7784   | 0.4696 | 0.1054 |
| 0.0868        | 12.0  | 12000 | 0.0983          | 0.7738   | 0.6074     | 2.0011 | 0.7738   | 0.7757   | 0.4741 | 0.0996 |
| 0.0855        | 13.0  | 13000 | 0.0977          | 0.7833   | 0.5864     | 1.9790 | 0.7833   | 0.7868   | 0.4633 | 0.1068 |
| 0.0845        | 14.0  | 14000 | 0.0986          | 0.782    | 0.5928     | 2.0415 | 0.782    | 0.7847   | 0.4645 | 0.1158 |
| 0.083         | 15.0  | 15000 | 0.0974          | 0.78     | 0.5793     | 2.0235 | 0.78     | 0.7857   | 0.4455 | 0.1243 |
| 0.0821        | 16.0  | 16000 | 0.0975          | 0.7823   | 0.5776     | 2.0363 | 0.7823   | 0.7859   | 0.4462 | 0.1238 |
| 0.0811        | 17.0  | 17000 | 0.0962          | 0.7883   | 0.5667     | 2.0085 | 0.7883   | 0.7907   | 0.4474 | 0.1108 |
| 0.0803        | 18.0  | 18000 | 0.0969          | 0.7833   | 0.5720     | 2.0028 | 0.7833   | 0.7840   | 0.4421 | 0.1276 |
| 0.0801        | 19.0  | 19000 | 0.0962          | 0.7823   | 0.5727     | 1.9412 | 0.7823   | 0.7847   | 0.4447 | 0.1182 |
| 0.0794        | 20.0  | 20000 | 0.0961          | 0.7847   | 0.5681     | 1.9442 | 0.7847   | 0.7851   | 0.4449 | 0.1121 |
| 0.0786        | 21.0  | 21000 | 0.0993          | 0.7612   | 0.5748     | 2.2878 | 0.7612   | 0.7627   | 0.4088 | 0.1494 |
| 0.0776        | 22.0  | 22000 | 0.0947          | 0.797    | 0.5491     | 1.8933 | 0.797    | 0.7986   | 0.4379 | 0.1211 |
| 0.0771        | 23.0  | 23000 | 0.0955          | 0.7893   | 0.5564     | 1.8974 | 0.7893   | 0.7918   | 0.4391 | 0.1124 |
| 0.0772        | 24.0  | 24000 | 0.0956          | 0.788    | 0.5524     | 1.9541 | 0.788    | 0.7898   | 0.4309 | 0.1166 |
| 0.0768        | 25.0  | 25000 | 0.0970          | 0.7748   | 0.5568     | 2.0627 | 0.7748   | 0.7776   | 0.4152 | 0.1264 |
| 0.0765        | 26.0  | 26000 | 0.0939          | 0.7975   | 0.5448     | 1.7874 | 0.7975   | 0.7996   | 0.4397 | 0.1086 |
| 0.0759        | 27.0  | 27000 | 0.0944          | 0.797    | 0.5425     | 1.8354 | 0.797    | 0.7982   | 0.4328 | 0.1185 |
| 0.0755        | 28.0  | 28000 | 0.0938          | 0.7993   | 0.5399     | 1.6911 | 0.7993   | 0.7993   | 0.4391 | 0.1025 |
| 0.0754        | 29.0  | 29000 | 0.0945          | 0.797    | 0.5387     | 1.8083 | 0.797    | 0.7980   | 0.4323 | 0.1117 |
| 0.075         | 30.0  | 30000 | 0.0941          | 0.8005   | 0.5353     | 1.7803 | 0.8005   | 0.8020   | 0.4318 | 0.1128 |
| 0.0745        | 31.0  | 31000 | 0.0928          | 0.805    | 0.5282     | 1.6621 | 0.805    | 0.8070   | 0.4338 | 0.1107 |
| 0.0747        | 32.0  | 32000 | 0.0935          | 0.806    | 0.5316     | 1.6745 | 0.806    | 0.8066   | 0.4368 | 0.1111 |
| 0.0743        | 33.0  | 33000 | 0.0928          | 0.8095   | 0.5288     | 1.7115 | 0.8095   | 0.8096   | 0.4401 | 0.1045 |
| 0.074         | 34.0  | 34000 | 0.0927          | 0.8063   | 0.5286     | 1.6801 | 0.8062   | 0.8064   | 0.4378 | 0.1001 |
| 0.0734        | 35.0  | 35000 | 0.0925          | 0.8083   | 0.5260     | 1.6524 | 0.8083   | 0.8102   | 0.4364 | 0.1066 |
| 0.0734        | 36.0  | 36000 | 0.0924          | 0.8087   | 0.5252     | 1.6727 | 0.8087   | 0.8106   | 0.4352 | 0.1077 |
| 0.0733        | 37.0  | 37000 | 0.0920          | 0.8133   | 0.5215     | 1.6062 | 0.8133   | 0.8147   | 0.4399 | 0.1000 |
| 0.0733        | 38.0  | 38000 | 0.0924          | 0.8083   | 0.5243     | 1.6319 | 0.8083   | 0.8100   | 0.4343 | 0.1063 |
| 0.0732        | 39.0  | 39000 | 0.0921          | 0.8105   | 0.5222     | 1.5823 | 0.8105   | 0.8106   | 0.4363 | 0.1034 |
| 0.073         | 40.0  | 40000 | 0.0917          | 0.8157   | 0.5203     | 1.5771 | 0.8157   | 0.8163   | 0.4414 | 0.1014 |
| 0.0728        | 41.0  | 41000 | 0.0916          | 0.8153   | 0.5192     | 1.5726 | 0.8153   | 0.8163   | 0.4395 | 0.1033 |
| 0.0729        | 42.0  | 42000 | 0.0916          | 0.8133   | 0.5188     | 1.5495 | 0.8133   | 0.8145   | 0.4392 | 0.1026 |
| 0.0726        | 43.0  | 43000 | 0.0917          | 0.816    | 0.5185     | 1.5969 | 0.816    | 0.8169   | 0.4395 | 0.1054 |
| 0.0728        | 44.0  | 44000 | 0.0914          | 0.8163   | 0.5164     | 1.5257 | 0.8163   | 0.8167   | 0.4388 | 0.1023 |
| 0.0725        | 45.0  | 45000 | 0.0914          | 0.8153   | 0.5165     | 1.5699 | 0.8153   | 0.8161   | 0.4386 | 0.1012 |
| 0.0723        | 46.0  | 46000 | 0.0915          | 0.816    | 0.5160     | 1.5653 | 0.816    | 0.8171   | 0.4386 | 0.1008 |
| 0.0723        | 47.0  | 47000 | 0.0914          | 0.8155   | 0.5159     | 1.5478 | 0.8155   | 0.8165   | 0.4380 | 0.0997 |
| 0.0721        | 48.0  | 48000 | 0.0914          | 0.816    | 0.5156     | 1.5579 | 0.816    | 0.8169   | 0.4379 | 0.1006 |
| 0.0725        | 49.0  | 49000 | 0.0914          | 0.8155   | 0.5153     | 1.5636 | 0.8155   | 0.8165   | 0.4369 | 0.1009 |
| 0.0721        | 50.0  | 50000 | 0.0914          | 0.8157   | 0.5152     | 1.5529 | 0.8157   | 0.8167   | 0.4370 | 0.1010 |


### Framework versions

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

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