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# dit-base-finetuned-rvlcdip-small_rvl_cdip-NK1000_og_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: 12194.7598
- Accuracy: 0.8558
- Brier Loss: 0.2688
- Nll: 1.9967
- F1 Micro: 0.8558
- F1 Macro: 0.8552
- Ece: 0.1305
- Aurc: 0.0490

## 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   |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 12767.556     | 1.0   | 1000  | 12472.2930      | 0.5725   | 0.5598     | 2.8765 | 0.5725   | 0.5254   | 0.0980 | 0.1794 |
| 12750.588     | 2.0   | 2000  | 12455.9170      | 0.6683   | 0.4822     | 2.7098 | 0.6683   | 0.6534   | 0.1247 | 0.1325 |
| 12820.858     | 3.0   | 3000  | 12458.1924      | 0.7003   | 0.4459     | 2.6913 | 0.7003   | 0.7019   | 0.1303 | 0.0964 |
| 12762.296     | 4.0   | 4000  | 12445.5703      | 0.7167   | 0.4202     | 2.8379 | 0.7168   | 0.7217   | 0.1078 | 0.0925 |
| 12706.14      | 5.0   | 5000  | 12425.8330      | 0.753    | 0.3820     | 2.7925 | 0.753    | 0.7539   | 0.0997 | 0.0923 |
| 12764.822     | 6.0   | 6000  | 12427.2080      | 0.7635   | 0.3603     | 2.5903 | 0.7635   | 0.7659   | 0.0950 | 0.0823 |
| 12719.869     | 7.0   | 7000  | 12411.4668      | 0.769    | 0.3469     | 2.7566 | 0.769    | 0.7761   | 0.0895 | 0.0681 |
| 12628.481     | 8.0   | 8000  | 12412.3760      | 0.7738   | 0.3535     | 2.7667 | 0.7738   | 0.7832   | 0.1127 | 0.0699 |
| 12624.542     | 9.0   | 9000  | 12396.7773      | 0.7933   | 0.3243     | 2.4484 | 0.7932   | 0.7954   | 0.1002 | 0.0664 |
| 12681.642     | 10.0  | 10000 | 12391.2744      | 0.7943   | 0.3241     | 2.5709 | 0.7943   | 0.7979   | 0.1081 | 0.0592 |
| 12656.593     | 11.0  | 11000 | 12383.5020      | 0.8015   | 0.3190     | 2.4516 | 0.8015   | 0.8065   | 0.1064 | 0.0597 |
| 12638.155     | 12.0  | 12000 | 12372.9707      | 0.7957   | 0.3357     | 2.4891 | 0.7957   | 0.7956   | 0.1225 | 0.0679 |
| 12698.474     | 13.0  | 13000 | 12370.7217      | 0.813    | 0.2988     | 2.1414 | 0.813    | 0.8125   | 0.1030 | 0.0494 |
| 12574.549     | 14.0  | 14000 | 12361.6641      | 0.8045   | 0.3218     | 2.4610 | 0.8045   | 0.8043   | 0.1155 | 0.0560 |
| 12589.537     | 15.0  | 15000 | 12345.1123      | 0.8193   | 0.3046     | 2.2566 | 0.8193   | 0.8184   | 0.1220 | 0.0524 |
| 12592.604     | 16.0  | 16000 | 12354.9756      | 0.817    | 0.3078     | 2.3526 | 0.817    | 0.8207   | 0.1204 | 0.0527 |
| 12660.709     | 17.0  | 17000 | 12334.7686      | 0.8293   | 0.2942     | 2.2857 | 0.8293   | 0.8284   | 0.1201 | 0.0482 |
| 12591.369     | 18.0  | 18000 | 12334.4570      | 0.829    | 0.2948     | 2.1559 | 0.8290   | 0.8287   | 0.1211 | 0.0451 |
| 12598.469     | 19.0  | 19000 | 12320.7510      | 0.826    | 0.2997     | 2.2348 | 0.826    | 0.8251   | 0.1240 | 0.0473 |
| 12497.537     | 20.0  | 20000 | 12307.0811      | 0.8347   | 0.2833     | 2.2433 | 0.8347   | 0.8358   | 0.1200 | 0.0426 |
| 12537.66      | 21.0  | 21000 | 12310.8438      | 0.8323   | 0.2965     | 2.1513 | 0.8323   | 0.8321   | 0.1287 | 0.0490 |
| 12524.668     | 22.0  | 22000 | 12300.1055      | 0.8403   | 0.2776     | 2.1780 | 0.8403   | 0.8407   | 0.1207 | 0.0427 |
| 12433.952     | 23.0  | 23000 | 12288.1221      | 0.8353   | 0.2898     | 2.2189 | 0.8353   | 0.8357   | 0.1346 | 0.0439 |
| 12598.38      | 24.0  | 24000 | 12282.6680      | 0.8442   | 0.2765     | 2.1653 | 0.8443   | 0.8440   | 0.1264 | 0.0438 |
| 12474.447     | 25.0  | 25000 | 12277.6797      | 0.8363   | 0.2925     | 2.1209 | 0.8363   | 0.8350   | 0.1366 | 0.0451 |
| 12522.706     | 26.0  | 26000 | 12276.4502      | 0.8465   | 0.2764     | 2.0779 | 0.8465   | 0.8469   | 0.1291 | 0.0432 |
| 12502.289     | 27.0  | 27000 | 12268.1758      | 0.8445   | 0.2811     | 2.0839 | 0.8445   | 0.8442   | 0.1318 | 0.0465 |
| 12465.994     | 28.0  | 28000 | 12252.7266      | 0.8433   | 0.2882     | 2.1410 | 0.8433   | 0.8431   | 0.1380 | 0.0479 |
| 12467.13      | 29.0  | 29000 | 12260.4912      | 0.8442   | 0.2838     | 2.1129 | 0.8443   | 0.8430   | 0.1348 | 0.0487 |
| 12540.006     | 30.0  | 30000 | 12249.1670      | 0.846    | 0.2811     | 2.1134 | 0.8460   | 0.8458   | 0.1349 | 0.0486 |
| 12594.326     | 31.0  | 31000 | 12245.6699      | 0.8452   | 0.2850     | 2.0734 | 0.8452   | 0.8443   | 0.1363 | 0.0480 |
| 12486.203     | 32.0  | 32000 | 12240.5479      | 0.8468   | 0.2813     | 2.0757 | 0.8468   | 0.8463   | 0.1353 | 0.0484 |
| 12468.631     | 33.0  | 33000 | 12231.9600      | 0.852    | 0.2715     | 2.0178 | 0.852    | 0.8523   | 0.1309 | 0.0450 |
| 12423.715     | 34.0  | 34000 | 12215.6680      | 0.8472   | 0.2843     | 2.0927 | 0.8472   | 0.8470   | 0.1389 | 0.0491 |
| 12454.715     | 35.0  | 35000 | 12223.0361      | 0.8492   | 0.2772     | 2.0161 | 0.8492   | 0.8485   | 0.1340 | 0.0476 |
| 12466.932     | 36.0  | 36000 | 12221.3887      | 0.8495   | 0.2776     | 2.0135 | 0.8495   | 0.8488   | 0.1343 | 0.0467 |
| 12483.745     | 37.0  | 37000 | 12210.9414      | 0.8508   | 0.2748     | 2.0374 | 0.8508   | 0.8506   | 0.1350 | 0.0493 |
| 12453.102     | 38.0  | 38000 | 12224.9482      | 0.852    | 0.2737     | 1.9699 | 0.852    | 0.8517   | 0.1308 | 0.0460 |
| 12511.225     | 39.0  | 39000 | 12213.9756      | 0.8522   | 0.2763     | 1.9619 | 0.8522   | 0.8518   | 0.1342 | 0.0484 |
| 12561.782     | 40.0  | 40000 | 12213.9297      | 0.852    | 0.2736     | 2.0481 | 0.852    | 0.8516   | 0.1326 | 0.0477 |
| 12524.982     | 41.0  | 41000 | 12208.1758      | 0.8518   | 0.2745     | 1.9751 | 0.8518   | 0.8509   | 0.1346 | 0.0490 |
| 12465.351     | 42.0  | 42000 | 12215.3604      | 0.8532   | 0.2730     | 2.0037 | 0.8532   | 0.8521   | 0.1314 | 0.0474 |
| 12419.902     | 43.0  | 43000 | 12211.3701      | 0.8565   | 0.2680     | 2.0140 | 0.8565   | 0.8561   | 0.1297 | 0.0462 |
| 12493.264     | 44.0  | 44000 | 12196.7217      | 0.8532   | 0.2717     | 1.9866 | 0.8532   | 0.8524   | 0.1336 | 0.0487 |
| 12487.514     | 45.0  | 45000 | 12199.4902      | 0.8532   | 0.2700     | 1.9711 | 0.8532   | 0.8523   | 0.1309 | 0.0478 |
| 12321.575     | 46.0  | 46000 | 12189.0117      | 0.8535   | 0.2727     | 2.0220 | 0.8535   | 0.8528   | 0.1335 | 0.0492 |
| 12423.494     | 47.0  | 47000 | 12198.2002      | 0.8542   | 0.2711     | 1.9648 | 0.8542   | 0.8536   | 0.1331 | 0.0478 |
| 12535.605     | 48.0  | 48000 | 12192.7061      | 0.8565   | 0.2678     | 2.0098 | 0.8565   | 0.8560   | 0.1292 | 0.0489 |
| 12319.588     | 49.0  | 49000 | 12185.3916      | 0.856    | 0.2691     | 2.0285 | 0.856    | 0.8554   | 0.1311 | 0.0506 |
| 12470.527     | 50.0  | 50000 | 12194.7598      | 0.8558   | 0.2688     | 1.9967 | 0.8558   | 0.8552   | 0.1305 | 0.0490 |


### Framework versions

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

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