
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_kd_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: 0.7141
- Accuracy: 0.776
- Brier Loss: 0.3162
- Nll: 1.9406
- F1 Micro: 0.776
- F1 Macro: 0.7755
- Ece: 0.0376
- Aurc: 0.0655

## 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   | 3.5746          | 0.2293   | 0.8509     | 3.6359 | 0.2293   | 0.1928   | 0.0536 | 0.5849 |
| 3.6362        | 2.0   | 500   | 3.4699          | 0.2848   | 0.8495     | 4.0612 | 0.2848   | 0.2468   | 0.0791 | 0.5541 |
| 3.6362        | 3.0   | 750   | 2.9284          | 0.3957   | 0.7832     | 3.1766 | 0.3957   | 0.3732   | 0.1412 | 0.4345 |
| 2.5616        | 4.0   | 1000  | 2.4586          | 0.4497   | 0.7009     | 3.0303 | 0.4497   | 0.4362   | 0.0977 | 0.3388 |
| 2.5616        | 5.0   | 1250  | 2.5238          | 0.4268   | 0.7479     | 3.0431 | 0.4268   | 0.4163   | 0.2085 | 0.3343 |
| 1.8525        | 6.0   | 1500  | 1.6707          | 0.5915   | 0.5475     | 2.6137 | 0.5915   | 0.5880   | 0.0929 | 0.1837 |
| 1.8525        | 7.0   | 1750  | 1.3529          | 0.6385   | 0.4901     | 2.3981 | 0.6385   | 0.6360   | 0.0936 | 0.1504 |
| 1.2522        | 8.0   | 2000  | 1.2537          | 0.6585   | 0.4640     | 2.2970 | 0.6585   | 0.6591   | 0.0722 | 0.1341 |
| 1.2522        | 9.0   | 2250  | 1.0462          | 0.7053   | 0.4096     | 2.2117 | 0.7053   | 0.7032   | 0.0668 | 0.1048 |
| 0.8788        | 10.0  | 2500  | 1.2045          | 0.6743   | 0.4500     | 2.2939 | 0.6743   | 0.6751   | 0.0818 | 0.1245 |
| 0.8788        | 11.0  | 2750  | 1.0487          | 0.7045   | 0.4107     | 2.1999 | 0.7045   | 0.6995   | 0.0686 | 0.1074 |
| 0.6325        | 12.0  | 3000  | 1.0625          | 0.7105   | 0.4075     | 2.2241 | 0.7105   | 0.7038   | 0.0670 | 0.1062 |
| 0.6325        | 13.0  | 3250  | 0.9567          | 0.7262   | 0.3820     | 2.1370 | 0.7262   | 0.7211   | 0.0585 | 0.0950 |
| 0.4724        | 14.0  | 3500  | 0.8833          | 0.744    | 0.3598     | 2.0806 | 0.744    | 0.7450   | 0.0477 | 0.0820 |
| 0.4724        | 15.0  | 3750  | 0.9074          | 0.744    | 0.3638     | 2.1065 | 0.744    | 0.7447   | 0.0504 | 0.0869 |
| 0.3827        | 16.0  | 4000  | 0.9355          | 0.7382   | 0.3678     | 2.1484 | 0.7382   | 0.7369   | 0.0490 | 0.0877 |
| 0.3827        | 17.0  | 4250  | 0.8586          | 0.7502   | 0.3508     | 2.0839 | 0.7502   | 0.7495   | 0.0500 | 0.0803 |
| 0.3253        | 18.0  | 4500  | 0.8572          | 0.7412   | 0.3579     | 2.0452 | 0.7412   | 0.7419   | 0.0504 | 0.0830 |
| 0.3253        | 19.0  | 4750  | 0.8663          | 0.741    | 0.3581     | 2.0923 | 0.7410   | 0.7405   | 0.0481 | 0.0826 |
| 0.288         | 20.0  | 5000  | 0.8162          | 0.7535   | 0.3431     | 2.0485 | 0.7535   | 0.7547   | 0.0444 | 0.0776 |
| 0.288         | 21.0  | 5250  | 0.9363          | 0.735    | 0.3739     | 2.1240 | 0.735    | 0.7332   | 0.0482 | 0.0895 |
| 0.2614        | 22.0  | 5500  | 0.8013          | 0.762    | 0.3391     | 2.0235 | 0.762    | 0.7598   | 0.0457 | 0.0743 |
| 0.2614        | 23.0  | 5750  | 0.7936          | 0.756    | 0.3391     | 2.0319 | 0.756    | 0.7585   | 0.0450 | 0.0748 |
| 0.2434        | 24.0  | 6000  | 0.8142          | 0.7515   | 0.3476     | 1.9971 | 0.7515   | 0.7483   | 0.0455 | 0.0769 |
| 0.2434        | 25.0  | 6250  | 0.8032          | 0.757    | 0.3441     | 2.0560 | 0.757    | 0.7569   | 0.0482 | 0.0766 |
| 0.2252        | 26.0  | 6500  | 0.7746          | 0.7622   | 0.3305     | 2.0183 | 0.7622   | 0.7648   | 0.0412 | 0.0717 |
| 0.2252        | 27.0  | 6750  | 0.7663          | 0.7655   | 0.3342     | 1.9947 | 0.7655   | 0.7674   | 0.0371 | 0.0724 |
| 0.213         | 28.0  | 7000  | 0.8283          | 0.756    | 0.3460     | 2.0260 | 0.756    | 0.7558   | 0.0432 | 0.0770 |
| 0.213         | 29.0  | 7250  | 0.7564          | 0.7638   | 0.3301     | 2.0031 | 0.7638   | 0.7653   | 0.0402 | 0.0716 |
| 0.2007        | 30.0  | 7500  | 0.7836          | 0.761    | 0.3376     | 2.0263 | 0.761    | 0.7599   | 0.0380 | 0.0741 |
| 0.2007        | 31.0  | 7750  | 0.7539          | 0.7668   | 0.3297     | 1.9693 | 0.7668   | 0.7647   | 0.0346 | 0.0699 |
| 0.1911        | 32.0  | 8000  | 0.7534          | 0.7665   | 0.3294     | 1.9842 | 0.7665   | 0.7645   | 0.0449 | 0.0702 |
| 0.1911        | 33.0  | 8250  | 0.7608          | 0.769    | 0.3276     | 2.0015 | 0.769    | 0.7687   | 0.0381 | 0.0701 |
| 0.183         | 34.0  | 8500  | 0.7511          | 0.7675   | 0.3273     | 1.9691 | 0.7675   | 0.7666   | 0.0394 | 0.0687 |
| 0.183         | 35.0  | 8750  | 0.7582          | 0.7615   | 0.3282     | 1.9961 | 0.7615   | 0.7607   | 0.0468 | 0.0717 |
| 0.1749        | 36.0  | 9000  | 0.7272          | 0.7702   | 0.3228     | 1.9832 | 0.7702   | 0.7706   | 0.0385 | 0.0679 |
| 0.1749        | 37.0  | 9250  | 0.7446          | 0.7685   | 0.3258     | 2.0006 | 0.7685   | 0.7698   | 0.0391 | 0.0694 |
| 0.1672        | 38.0  | 9500  | 0.7252          | 0.773    | 0.3200     | 1.9722 | 0.7730   | 0.7719   | 0.0423 | 0.0673 |
| 0.1672        | 39.0  | 9750  | 0.7260          | 0.7738   | 0.3184     | 1.9599 | 0.7738   | 0.7741   | 0.0357 | 0.0660 |
| 0.1608        | 40.0  | 10000 | 0.7317          | 0.7715   | 0.3191     | 1.9651 | 0.7715   | 0.7717   | 0.0340 | 0.0668 |
| 0.1608        | 41.0  | 10250 | 0.7268          | 0.7742   | 0.3206     | 1.9666 | 0.7742   | 0.7728   | 0.0429 | 0.0671 |
| 0.1549        | 42.0  | 10500 | 0.7296          | 0.7745   | 0.3198     | 1.9628 | 0.7745   | 0.7738   | 0.0341 | 0.0666 |
| 0.1549        | 43.0  | 10750 | 0.7223          | 0.773    | 0.3173     | 1.9566 | 0.7730   | 0.7732   | 0.0414 | 0.0661 |
| 0.15          | 44.0  | 11000 | 0.7184          | 0.775    | 0.3175     | 1.9566 | 0.775    | 0.7746   | 0.0385 | 0.0661 |
| 0.15          | 45.0  | 11250 | 0.7205          | 0.7755   | 0.3181     | 1.9439 | 0.7755   | 0.7749   | 0.0377 | 0.0661 |
| 0.1458        | 46.0  | 11500 | 0.7174          | 0.777    | 0.3173     | 1.9560 | 0.777    | 0.7768   | 0.0424 | 0.0660 |
| 0.1458        | 47.0  | 11750 | 0.7152          | 0.7755   | 0.3164     | 1.9527 | 0.7755   | 0.7750   | 0.0404 | 0.0656 |
| 0.1429        | 48.0  | 12000 | 0.7173          | 0.777    | 0.3161     | 1.9350 | 0.777    | 0.7774   | 0.0415 | 0.0656 |
| 0.1429        | 49.0  | 12250 | 0.7142          | 0.7755   | 0.3159     | 1.9515 | 0.7755   | 0.7748   | 0.0404 | 0.0656 |
| 0.141         | 50.0  | 12500 | 0.7141          | 0.776    | 0.3162     | 1.9406 | 0.776    | 0.7755   | 0.0376 | 0.0655 |


### Framework versions

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

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_kd_rand", "results": []}]}
transformers pytorch resnet image-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us