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# dit-base-finetuned-rvlcdip-tiny_rvl_cdip-NK1000_og_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: 12216.4121
- Accuracy: 0.8337
- Brier Loss: 0.3073
- Nll: 2.1945
- F1 Micro: 0.8337
- F1 Macro: 0.8337
- Ece: 0.1506
- Aurc: 0.0535

## 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   |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 12731.392     | 1.0   | 1000  | 12479.0312      | 0.5323   | 0.5967     | 3.1288 | 0.5323   | 0.4700   | 0.1047 | 0.2100 |
| 12715.18      | 2.0   | 2000  | 12453.9434      | 0.5787   | 0.6261     | 3.4535 | 0.5787   | 0.5518   | 0.1953 | 0.2225 |
| 12672.101     | 3.0   | 3000  | 12456.4629      | 0.6723   | 0.4708     | 2.9701 | 0.6723   | 0.6487   | 0.1102 | 0.1171 |
| 12681.216     | 4.0   | 4000  | 12448.6143      | 0.6815   | 0.4741     | 2.8370 | 0.6815   | 0.6707   | 0.1250 | 0.1245 |
| 12638.181     | 5.0   | 5000  | 12443.0645      | 0.716    | 0.4178     | 2.7837 | 0.7160   | 0.7273   | 0.1095 | 0.0932 |
| 12802.019     | 6.0   | 6000  | 12432.3438      | 0.7468   | 0.3768     | 2.6575 | 0.7468   | 0.7537   | 0.0898 | 0.0800 |
| 12671.194     | 7.0   | 7000  | 12420.4395      | 0.7335   | 0.4163     | 2.8018 | 0.7335   | 0.7331   | 0.1256 | 0.1139 |
| 12705.783     | 8.0   | 8000  | 12410.6143      | 0.7598   | 0.3739     | 2.7562 | 0.7598   | 0.7634   | 0.1157 | 0.0729 |
| 12559.44      | 9.0   | 9000  | 12412.9453      | 0.7612   | 0.3793     | 2.8905 | 0.7612   | 0.7658   | 0.1232 | 0.0731 |
| 12618.725     | 10.0  | 10000 | 12390.2139      | 0.7722   | 0.3613     | 2.6477 | 0.7722   | 0.7753   | 0.1190 | 0.0750 |
| 12731.292     | 11.0  | 11000 | 12387.4863      | 0.7875   | 0.3427     | 2.5376 | 0.7875   | 0.7898   | 0.1154 | 0.0689 |
| 12705.794     | 12.0  | 12000 | 12379.9336      | 0.7805   | 0.3555     | 2.6072 | 0.7805   | 0.7813   | 0.1284 | 0.0681 |
| 12550.782     | 13.0  | 13000 | 12380.7959      | 0.787    | 0.3400     | 2.5381 | 0.787    | 0.7887   | 0.1162 | 0.0662 |
| 12670.568     | 14.0  | 14000 | 12376.7646      | 0.7867   | 0.3423     | 2.6149 | 0.7868   | 0.7925   | 0.1186 | 0.0574 |
| 12580.616     | 15.0  | 15000 | 12352.3135      | 0.7953   | 0.3468     | 2.6324 | 0.7953   | 0.7969   | 0.1382 | 0.0622 |
| 12723.865     | 16.0  | 16000 | 12345.4600      | 0.8015   | 0.3312     | 2.4793 | 0.8015   | 0.8034   | 0.1244 | 0.0601 |
| 12620.305     | 17.0  | 17000 | 12343.1553      | 0.8023   | 0.3424     | 2.6488 | 0.8023   | 0.8031   | 0.1420 | 0.0644 |
| 12668.087     | 18.0  | 18000 | 12336.9277      | 0.8      | 0.3455     | 2.7019 | 0.8000   | 0.8023   | 0.1401 | 0.0592 |
| 12654.687     | 19.0  | 19000 | 12332.4404      | 0.8075   | 0.3321     | 2.5589 | 0.8075   | 0.8094   | 0.1393 | 0.0556 |
| 12578.655     | 20.0  | 20000 | 12321.3037      | 0.8075   | 0.3395     | 2.4255 | 0.8075   | 0.8050   | 0.1484 | 0.0638 |
| 12525.448     | 21.0  | 21000 | 12315.5303      | 0.8067   | 0.3328     | 2.5264 | 0.8067   | 0.8066   | 0.1440 | 0.0548 |
| 12610.837     | 22.0  | 22000 | 12311.0215      | 0.8105   | 0.3291     | 2.4781 | 0.8105   | 0.8112   | 0.1445 | 0.0540 |
| 12494.528     | 23.0  | 23000 | 12303.3623      | 0.8145   | 0.3337     | 2.5535 | 0.8145   | 0.8154   | 0.1510 | 0.0561 |
| 12561.799     | 24.0  | 24000 | 12296.2363      | 0.8153   | 0.3246     | 2.4243 | 0.8153   | 0.8142   | 0.1475 | 0.0513 |
| 12580.176     | 25.0  | 25000 | 12291.8018      | 0.8193   | 0.3262     | 2.3932 | 0.8193   | 0.8174   | 0.1484 | 0.0550 |
| 12455.165     | 26.0  | 26000 | 12276.9355      | 0.826    | 0.3223     | 2.4710 | 0.826    | 0.8251   | 0.1507 | 0.0597 |
| 12528.496     | 27.0  | 27000 | 12280.9180      | 0.8257   | 0.3154     | 2.4010 | 0.8257   | 0.8260   | 0.1462 | 0.0524 |
| 12521.554     | 28.0  | 28000 | 12262.9600      | 0.821    | 0.3274     | 2.4721 | 0.821    | 0.8201   | 0.1560 | 0.0595 |
| 12557.871     | 29.0  | 29000 | 12260.7754      | 0.823    | 0.3217     | 2.3929 | 0.823    | 0.8226   | 0.1552 | 0.0551 |
| 12535.524     | 30.0  | 30000 | 12271.4717      | 0.8263   | 0.3183     | 2.3249 | 0.8263   | 0.8269   | 0.1503 | 0.0502 |
| 12488.263     | 31.0  | 31000 | 12259.3057      | 0.823    | 0.3219     | 2.3830 | 0.823    | 0.8226   | 0.1541 | 0.0528 |
| 12498.048     | 32.0  | 32000 | 12253.2412      | 0.8263   | 0.3174     | 2.2771 | 0.8263   | 0.8243   | 0.1527 | 0.0541 |
| 12465.825     | 33.0  | 33000 | 12257.4863      | 0.8323   | 0.3088     | 2.3466 | 0.8323   | 0.8319   | 0.1454 | 0.0500 |
| 12439.6       | 34.0  | 34000 | 12238.5957      | 0.8323   | 0.3093     | 2.4057 | 0.8323   | 0.8329   | 0.1482 | 0.0552 |
| 12407.423     | 35.0  | 35000 | 12250.7178      | 0.8335   | 0.3072     | 2.2532 | 0.8335   | 0.8336   | 0.1471 | 0.0521 |
| 12534.711     | 36.0  | 36000 | 12231.9902      | 0.8353   | 0.3032     | 2.2711 | 0.8353   | 0.8353   | 0.1464 | 0.0548 |
| 12458.666     | 37.0  | 37000 | 12232.9521      | 0.835    | 0.3041     | 2.2523 | 0.835    | 0.8352   | 0.1467 | 0.0539 |
| 12461.748     | 38.0  | 38000 | 12230.4639      | 0.8317   | 0.3096     | 2.3052 | 0.8317   | 0.8318   | 0.1512 | 0.0539 |
| 12434.679     | 39.0  | 39000 | 12229.0684      | 0.8317   | 0.3081     | 2.2172 | 0.8317   | 0.8317   | 0.1497 | 0.0547 |
| 12468.468     | 40.0  | 40000 | 12226.4775      | 0.8323   | 0.3096     | 2.3112 | 0.8323   | 0.8324   | 0.1509 | 0.0524 |
| 12540.176     | 41.0  | 41000 | 12213.8359      | 0.8357   | 0.3085     | 2.2929 | 0.8357   | 0.8356   | 0.1502 | 0.0541 |
| 12513.896     | 42.0  | 42000 | 12216.2480      | 0.8333   | 0.3096     | 2.1638 | 0.8333   | 0.8329   | 0.1501 | 0.0559 |
| 12406.31      | 43.0  | 43000 | 12213.7012      | 0.8347   | 0.3078     | 2.1971 | 0.8347   | 0.8345   | 0.1504 | 0.0542 |
| 12350.768     | 44.0  | 44000 | 12224.6738      | 0.8323   | 0.3086     | 2.1722 | 0.8323   | 0.8320   | 0.1514 | 0.0546 |
| 12394.478     | 45.0  | 45000 | 12221.9336      | 0.8325   | 0.3100     | 2.2464 | 0.8325   | 0.8323   | 0.1516 | 0.0536 |
| 12399.318     | 46.0  | 46000 | 12207.5957      | 0.8347   | 0.3089     | 2.2193 | 0.8347   | 0.8344   | 0.1517 | 0.0553 |
| 12476.218     | 47.0  | 47000 | 12213.4814      | 0.8353   | 0.3055     | 2.2084 | 0.8353   | 0.8353   | 0.1488 | 0.0532 |
| 12448.278     | 48.0  | 48000 | 12212.2119      | 0.8347   | 0.3058     | 2.1518 | 0.8347   | 0.8345   | 0.1492 | 0.0545 |
| 12486.848     | 49.0  | 49000 | 12210.5742      | 0.8347   | 0.3062     | 2.2778 | 0.8347   | 0.8345   | 0.1498 | 0.0546 |
| 12376.327     | 50.0  | 50000 | 12216.4121      | 0.8337   | 0.3073     | 2.1945 | 0.8337   | 0.8337   | 0.1506 | 0.0535 |


### Framework versions

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

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