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# resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_hint_rand

This model is a fine-tuned version of [bdpc/resnet101_rvl-cdip](https://huggingface.co/bdpc/resnet101_rvl-cdip) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 19.9624
- Accuracy: 0.765
- Brier Loss: 0.3910
- Nll: 2.2998
- F1 Micro: 0.765
- F1 Macro: 0.7641
- Ece: 0.1669
- Aurc: 0.0775

## 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: 64
- eval_batch_size: 64
- 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   | 250   | 26.8546         | 0.2102   | 0.8652     | 3.4225 | 0.2102   | 0.1897   | 0.0659 | 0.6137 |
| 26.8891       | 2.0   | 500   | 25.9803         | 0.2845   | 0.8381     | 3.4360 | 0.2845   | 0.2457   | 0.0762 | 0.5573 |
| 26.8891       | 3.0   | 750   | 25.8252         | 0.337    | 0.8092     | 3.6483 | 0.337    | 0.3168   | 0.1091 | 0.4988 |
| 25.1656       | 4.0   | 1000  | 24.9957         | 0.4088   | 0.7588     | 3.0528 | 0.4088   | 0.3859   | 0.1283 | 0.4163 |
| 25.1656       | 5.0   | 1250  | 24.2209         | 0.5517   | 0.5964     | 2.6661 | 0.5517   | 0.5510   | 0.0726 | 0.2489 |
| 23.8526       | 6.0   | 1500  | 23.4086         | 0.5915   | 0.5431     | 2.5360 | 0.5915   | 0.5840   | 0.0549 | 0.1994 |
| 23.8526       | 7.0   | 1750  | 23.0800         | 0.625    | 0.5049     | 2.4839 | 0.625    | 0.6268   | 0.0608 | 0.1668 |
| 22.7511       | 8.0   | 2000  | 22.9512         | 0.6573   | 0.4660     | 2.4202 | 0.6573   | 0.6564   | 0.0565 | 0.1407 |
| 22.7511       | 9.0   | 2250  | 22.7991         | 0.6783   | 0.4509     | 2.4137 | 0.6783   | 0.6749   | 0.0634 | 0.1315 |
| 21.9881       | 10.0  | 2500  | 22.8533         | 0.6352   | 0.5056     | 2.6549 | 0.6352   | 0.6231   | 0.1085 | 0.1554 |
| 21.9881       | 11.0  | 2750  | 22.7499         | 0.669    | 0.4673     | 2.5291 | 0.669    | 0.6642   | 0.1053 | 0.1347 |
| 21.391        | 12.0  | 3000  | 22.6520         | 0.6757   | 0.4767     | 2.5038 | 0.6757   | 0.6745   | 0.1204 | 0.1355 |
| 21.391        | 13.0  | 3250  | 22.4767         | 0.6737   | 0.4850     | 2.6030 | 0.6737   | 0.6718   | 0.1385 | 0.1380 |
| 20.9347       | 14.0  | 3500  | 22.3023         | 0.6767   | 0.4832     | 2.5438 | 0.6767   | 0.6770   | 0.1594 | 0.1301 |
| 20.9347       | 15.0  | 3750  | 22.1482         | 0.693    | 0.4666     | 2.5622 | 0.693    | 0.6913   | 0.1581 | 0.1209 |
| 20.5776       | 16.0  | 4000  | 22.1655         | 0.6943   | 0.4849     | 2.5685 | 0.6943   | 0.6994   | 0.1766 | 0.1288 |
| 20.5776       | 17.0  | 4250  | 22.0213         | 0.686    | 0.4922     | 2.6576 | 0.686    | 0.6925   | 0.1749 | 0.1250 |
| 20.2836       | 18.0  | 4500  | 21.5434         | 0.7023   | 0.4560     | 2.5508 | 0.7023   | 0.7018   | 0.1720 | 0.1146 |
| 20.2836       | 19.0  | 4750  | 21.7105         | 0.715    | 0.4501     | 2.5953 | 0.715    | 0.7128   | 0.1738 | 0.1083 |
| 20.0339       | 20.0  | 5000  | 21.6301         | 0.7057   | 0.4645     | 2.6131 | 0.7057   | 0.7033   | 0.1701 | 0.1153 |
| 20.0339       | 21.0  | 5250  | 21.9130         | 0.7007   | 0.4825     | 2.7114 | 0.7007   | 0.6989   | 0.1917 | 0.1231 |
| 19.8026       | 22.0  | 5500  | 21.7975         | 0.713    | 0.4702     | 2.7340 | 0.713    | 0.7117   | 0.1879 | 0.1148 |
| 19.8026       | 23.0  | 5750  | 21.5577         | 0.7173   | 0.4621     | 2.7138 | 0.7173   | 0.7100   | 0.1931 | 0.1072 |
| 19.6001       | 24.0  | 6000  | 21.2486         | 0.722    | 0.4491     | 2.5651 | 0.722    | 0.7214   | 0.1853 | 0.1045 |
| 19.6001       | 25.0  | 6250  | 21.0363         | 0.7348   | 0.4344     | 2.4688 | 0.7348   | 0.7364   | 0.1780 | 0.0974 |
| 19.4158       | 26.0  | 6500  | 21.3527         | 0.728    | 0.4495     | 2.7492 | 0.728    | 0.7219   | 0.1864 | 0.1005 |
| 19.4158       | 27.0  | 6750  | 20.8258         | 0.7355   | 0.4339     | 2.4375 | 0.7355   | 0.7352   | 0.1838 | 0.0943 |
| 19.2585       | 28.0  | 7000  | 21.0491         | 0.729    | 0.4465     | 2.6324 | 0.729    | 0.7245   | 0.1953 | 0.1010 |
| 19.2585       | 29.0  | 7250  | 20.7774         | 0.7425   | 0.4283     | 2.4694 | 0.7425   | 0.7410   | 0.1799 | 0.0908 |
| 19.1051       | 30.0  | 7500  | 20.6908         | 0.741    | 0.4311     | 2.4924 | 0.7410   | 0.7405   | 0.1890 | 0.0888 |
| 19.1051       | 31.0  | 7750  | 20.8242         | 0.743    | 0.4264     | 2.5098 | 0.743    | 0.7407   | 0.1826 | 0.0903 |
| 18.9722       | 32.0  | 8000  | 20.6257         | 0.7435   | 0.4288     | 2.4740 | 0.7435   | 0.7432   | 0.1865 | 0.0872 |
| 18.9722       | 33.0  | 8250  | 20.6265         | 0.745    | 0.4289     | 2.4552 | 0.745    | 0.7435   | 0.1862 | 0.0929 |
| 18.854        | 34.0  | 8500  | 20.4251         | 0.7505   | 0.4124     | 2.4631 | 0.7505   | 0.7513   | 0.1790 | 0.0845 |
| 18.854        | 35.0  | 8750  | 20.4164         | 0.741    | 0.4278     | 2.3888 | 0.7410   | 0.7402   | 0.1859 | 0.0889 |
| 18.7477       | 36.0  | 9000  | 20.3432         | 0.751    | 0.4184     | 2.4020 | 0.751    | 0.7485   | 0.1800 | 0.0850 |
| 18.7477       | 37.0  | 9250  | 20.4310         | 0.7555   | 0.4154     | 2.4639 | 0.7555   | 0.7528   | 0.1759 | 0.0842 |
| 18.6548       | 38.0  | 9500  | 20.1987         | 0.7542   | 0.4111     | 2.2921 | 0.7542   | 0.7542   | 0.1792 | 0.0815 |
| 18.6548       | 39.0  | 9750  | 20.2326         | 0.7562   | 0.4017     | 2.3536 | 0.7562   | 0.7537   | 0.1767 | 0.0829 |
| 18.5776       | 40.0  | 10000 | 20.1571         | 0.7575   | 0.3985     | 2.3405 | 0.7575   | 0.7568   | 0.1703 | 0.0811 |
| 18.5776       | 41.0  | 10250 | 20.1580         | 0.7625   | 0.3962     | 2.3855 | 0.7625   | 0.7621   | 0.1713 | 0.0814 |
| 18.5133       | 42.0  | 10500 | 20.0952         | 0.7572   | 0.4038     | 2.3600 | 0.7572   | 0.7563   | 0.1768 | 0.0794 |
| 18.5133       | 43.0  | 10750 | 20.1483         | 0.7575   | 0.4008     | 2.3713 | 0.7575   | 0.7564   | 0.1755 | 0.0820 |
| 18.4613       | 44.0  | 11000 | 20.0749         | 0.762    | 0.3992     | 2.3372 | 0.762    | 0.7618   | 0.1720 | 0.0795 |
| 18.4613       | 45.0  | 11250 | 20.0664         | 0.7578   | 0.4035     | 2.3570 | 0.7577   | 0.7566   | 0.1769 | 0.0795 |
| 18.4218       | 46.0  | 11500 | 19.9611         | 0.7622   | 0.3946     | 2.3399 | 0.7622   | 0.7617   | 0.1674 | 0.0784 |
| 18.4218       | 47.0  | 11750 | 19.9678         | 0.7632   | 0.3907     | 2.3011 | 0.7632   | 0.7635   | 0.1692 | 0.0772 |
| 18.3945       | 48.0  | 12000 | 19.9950         | 0.763    | 0.3910     | 2.2773 | 0.763    | 0.7616   | 0.1695 | 0.0775 |
| 18.3945       | 49.0  | 12250 | 20.0013         | 0.7625   | 0.3911     | 2.2875 | 0.7625   | 0.7618   | 0.1705 | 0.0777 |
| 18.3792       | 50.0  | 12500 | 19.9624         | 0.765    | 0.3910     | 2.2998 | 0.765    | 0.7641   | 0.1669 | 0.0775 |


### Framework versions

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

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