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# resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_kd_MSE_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: 0.9849
- Accuracy: 0.7652
- Brier Loss: 0.3272
- Nll: 2.2207
- F1 Micro: 0.7652
- F1 Macro: 0.7659
- Ece: 0.0400
- Aurc: 0.0704

## 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   | 5.3199          | 0.2082   | 0.8704     | 3.2359 | 0.2082   | 0.1578   | 0.0869 | 0.5991 |
| 6.235         | 2.0   | 500   | 4.4722          | 0.336    | 0.7969     | 6.1930 | 0.336    | 0.3239   | 0.0602 | 0.4723 |
| 6.235         | 3.0   | 750   | 4.4441          | 0.3252   | 0.8299     | 7.3074 | 0.3252   | 0.2997   | 0.1510 | 0.4987 |
| 3.7524        | 4.0   | 1000  | 5.8119          | 0.2512   | 0.9906     | 9.0519 | 0.2512   | 0.2274   | 0.3051 | 0.6464 |
| 3.7524        | 5.0   | 1250  | 3.3062          | 0.4848   | 0.6850     | 5.3438 | 0.4848   | 0.4758   | 0.1312 | 0.3066 |
| 2.6846        | 6.0   | 1500  | 2.5489          | 0.5737   | 0.5723     | 4.2350 | 0.5737   | 0.5633   | 0.0986 | 0.2059 |
| 2.6846        | 7.0   | 1750  | 1.9242          | 0.646    | 0.4801     | 3.5335 | 0.646    | 0.6475   | 0.0736 | 0.1432 |
| 1.7535        | 8.0   | 2000  | 1.5612          | 0.681    | 0.4321     | 3.0209 | 0.681    | 0.6849   | 0.0656 | 0.1183 |
| 1.7535        | 9.0   | 2250  | 1.6465          | 0.6943   | 0.4279     | 3.0835 | 0.6943   | 0.6880   | 0.0740 | 0.1125 |
| 1.2416        | 10.0  | 2500  | 1.4493          | 0.6963   | 0.4166     | 2.7813 | 0.6963   | 0.7028   | 0.0650 | 0.1096 |
| 1.2416        | 11.0  | 2750  | 1.3074          | 0.724    | 0.3870     | 2.6976 | 0.724    | 0.7209   | 0.0586 | 0.0978 |
| 0.9163        | 12.0  | 3000  | 1.4593          | 0.7175   | 0.4025     | 2.7043 | 0.7175   | 0.7181   | 0.0565 | 0.1089 |
| 0.9163        | 13.0  | 3250  | 1.3412          | 0.7105   | 0.4017     | 2.6889 | 0.7105   | 0.7090   | 0.0701 | 0.1062 |
| 0.7085        | 14.0  | 3500  | 1.1755          | 0.7342   | 0.3648     | 2.4382 | 0.7342   | 0.7368   | 0.0612 | 0.0857 |
| 0.7085        | 15.0  | 3750  | 1.2057          | 0.7298   | 0.3733     | 2.4317 | 0.7298   | 0.7346   | 0.0591 | 0.0898 |
| 0.563         | 16.0  | 4000  | 1.2093          | 0.7288   | 0.3730     | 2.4142 | 0.7288   | 0.7329   | 0.0485 | 0.0914 |
| 0.563         | 17.0  | 4250  | 1.2197          | 0.7392   | 0.3636     | 2.5076 | 0.7392   | 0.7381   | 0.0503 | 0.0852 |
| 0.4589        | 18.0  | 4500  | 1.3051          | 0.74     | 0.3646     | 2.5211 | 0.74     | 0.7361   | 0.0455 | 0.0860 |
| 0.4589        | 19.0  | 4750  | 1.3057          | 0.7415   | 0.3636     | 2.4452 | 0.7415   | 0.7389   | 0.0459 | 0.0858 |
| 0.3787        | 20.0  | 5000  | 1.2422          | 0.7435   | 0.3577     | 2.5107 | 0.7435   | 0.7417   | 0.0474 | 0.0835 |
| 0.3787        | 21.0  | 5250  | 1.3069          | 0.7408   | 0.3645     | 2.5522 | 0.7408   | 0.7384   | 0.0450 | 0.0875 |
| 0.3203        | 22.0  | 5500  | 1.0779          | 0.7555   | 0.3449     | 2.3048 | 0.7555   | 0.7556   | 0.0514 | 0.0786 |
| 0.3203        | 23.0  | 5750  | 1.1117          | 0.7522   | 0.3481     | 2.3305 | 0.7522   | 0.7559   | 0.0501 | 0.0790 |
| 0.2752        | 24.0  | 6000  | 1.1987          | 0.7545   | 0.3504     | 2.4084 | 0.7545   | 0.7521   | 0.0468 | 0.0803 |
| 0.2752        | 25.0  | 6250  | 1.0761          | 0.7528   | 0.3434     | 2.3344 | 0.7528   | 0.7548   | 0.0489 | 0.0772 |
| 0.2413        | 26.0  | 6500  | 1.0694          | 0.7585   | 0.3385     | 2.3028 | 0.7585   | 0.7612   | 0.0521 | 0.0750 |
| 0.2413        | 27.0  | 6750  | 1.0564          | 0.758    | 0.3401     | 2.2827 | 0.7580   | 0.7601   | 0.0450 | 0.0760 |
| 0.2108        | 28.0  | 7000  | 1.0964          | 0.756    | 0.3424     | 2.2600 | 0.756    | 0.7583   | 0.0442 | 0.0775 |
| 0.2108        | 29.0  | 7250  | 1.0759          | 0.7515   | 0.3376     | 2.2334 | 0.7515   | 0.7555   | 0.0477 | 0.0753 |
| 0.1891        | 30.0  | 7500  | 1.0712          | 0.758    | 0.3399     | 2.3159 | 0.7580   | 0.7614   | 0.0432 | 0.0765 |
| 0.1891        | 31.0  | 7750  | 1.0485          | 0.7552   | 0.3349     | 2.2442 | 0.7552   | 0.7536   | 0.0461 | 0.0738 |
| 0.1697        | 32.0  | 8000  | 1.0812          | 0.7622   | 0.3362     | 2.3025 | 0.7622   | 0.7609   | 0.0468 | 0.0741 |
| 0.1697        | 33.0  | 8250  | 1.0069          | 0.765    | 0.3294     | 2.2406 | 0.765    | 0.7649   | 0.0441 | 0.0712 |
| 0.1549        | 34.0  | 8500  | 1.0517          | 0.7622   | 0.3339     | 2.2801 | 0.7622   | 0.7618   | 0.0394 | 0.0731 |
| 0.1549        | 35.0  | 8750  | 1.0146          | 0.761    | 0.3322     | 2.2421 | 0.761    | 0.7611   | 0.0434 | 0.0728 |
| 0.1403        | 36.0  | 9000  | 1.0122          | 0.7648   | 0.3308     | 2.2229 | 0.7648   | 0.7662   | 0.0460 | 0.0715 |
| 0.1403        | 37.0  | 9250  | 1.0170          | 0.7615   | 0.3308     | 2.2247 | 0.7615   | 0.7632   | 0.0381 | 0.0721 |
| 0.1277        | 38.0  | 9500  | 1.0135          | 0.763    | 0.3310     | 2.2289 | 0.763    | 0.7633   | 0.0412 | 0.0717 |
| 0.1277        | 39.0  | 9750  | 1.0230          | 0.7595   | 0.3323     | 2.2202 | 0.7595   | 0.7609   | 0.0450 | 0.0727 |
| 0.1163        | 40.0  | 10000 | 1.0133          | 0.7622   | 0.3319     | 2.2613 | 0.7622   | 0.7637   | 0.0429 | 0.0723 |
| 0.1163        | 41.0  | 10250 | 1.0142          | 0.7638   | 0.3319     | 2.2730 | 0.7638   | 0.7629   | 0.0430 | 0.0724 |
| 0.1063        | 42.0  | 10500 | 0.9957          | 0.7618   | 0.3297     | 2.2467 | 0.7618   | 0.7627   | 0.0425 | 0.0717 |
| 0.1063        | 43.0  | 10750 | 1.0029          | 0.7658   | 0.3280     | 2.2498 | 0.7658   | 0.7672   | 0.0464 | 0.0705 |
| 0.0974        | 44.0  | 11000 | 0.9954          | 0.766    | 0.3287     | 2.2435 | 0.766    | 0.7669   | 0.0443 | 0.0709 |
| 0.0974        | 45.0  | 11250 | 0.9887          | 0.768    | 0.3284     | 2.2407 | 0.768    | 0.7687   | 0.0340 | 0.0707 |
| 0.0914        | 46.0  | 11500 | 0.9890          | 0.7668   | 0.3276     | 2.2161 | 0.7668   | 0.7669   | 0.0433 | 0.0704 |
| 0.0914        | 47.0  | 11750 | 0.9854          | 0.766    | 0.3270     | 2.2357 | 0.766    | 0.7668   | 0.0360 | 0.0703 |
| 0.0854        | 48.0  | 12000 | 0.9868          | 0.7675   | 0.3273     | 2.2340 | 0.7675   | 0.7681   | 0.0420 | 0.0705 |
| 0.0854        | 49.0  | 12250 | 0.9843          | 0.765    | 0.3267     | 2.2427 | 0.765    | 0.7654   | 0.0405 | 0.0702 |
| 0.0824        | 50.0  | 12500 | 0.9849          | 0.7652   | 0.3272     | 2.2207 | 0.7652   | 0.7659   | 0.0400 | 0.0704 |


### Framework versions

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

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