
<!-- 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_og_simkd_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: 0.4025
- Accuracy: 0.7765
- Brier Loss: 0.3168
- Nll: 2.1020
- F1 Micro: 0.7765
- F1 Macro: 0.7763
- Ece: 0.0442
- Aurc: 0.0661

## 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   | 0.7862          | 0.115    | 0.9306     | 3.3236 | 0.115    | 0.0472   | 0.0925 | 0.7696 |
| 0.872         | 2.0   | 500   | 0.7072          | 0.187    | 0.9137     | 4.4054 | 0.187    | 0.1401   | 0.0987 | 0.7052 |
| 0.872         | 3.0   | 750   | 0.6647          | 0.2918   | 0.8192     | 7.0505 | 0.2918   | 0.2588   | 0.0612 | 0.5348 |
| 0.6505        | 4.0   | 1000  | 0.6113          | 0.4565   | 0.6865     | 5.3222 | 0.4565   | 0.4485   | 0.0605 | 0.3382 |
| 0.6505        | 5.0   | 1250  | 0.5561          | 0.5797   | 0.5560     | 4.2347 | 0.5797   | 0.5876   | 0.0597 | 0.2014 |
| 0.5417        | 6.0   | 1500  | 0.4978          | 0.657    | 0.4720     | 3.3410 | 0.657    | 0.6557   | 0.0649 | 0.1412 |
| 0.5417        | 7.0   | 1750  | 0.4849          | 0.6703   | 0.4677     | 3.2428 | 0.6703   | 0.6748   | 0.0876 | 0.1380 |
| 0.4664        | 8.0   | 2000  | 0.4561          | 0.709    | 0.4076     | 2.7423 | 0.709    | 0.7105   | 0.0585 | 0.1065 |
| 0.4664        | 9.0   | 2250  | 0.4467          | 0.7225   | 0.3868     | 2.6295 | 0.7225   | 0.7199   | 0.0566 | 0.0974 |
| 0.4241        | 10.0  | 2500  | 0.4380          | 0.7242   | 0.3822     | 2.6630 | 0.7242   | 0.7254   | 0.0618 | 0.0928 |
| 0.4241        | 11.0  | 2750  | 0.4317          | 0.7478   | 0.3579     | 2.4077 | 0.7478   | 0.7491   | 0.0541 | 0.0833 |
| 0.3956        | 12.0  | 3000  | 0.4238          | 0.7512   | 0.3543     | 2.4087 | 0.7513   | 0.7513   | 0.0589 | 0.0813 |
| 0.3956        | 13.0  | 3250  | 0.4403          | 0.734    | 0.3663     | 2.5318 | 0.734    | 0.7327   | 0.0609 | 0.0873 |
| 0.3718        | 14.0  | 3500  | 0.4285          | 0.7462   | 0.3559     | 2.3344 | 0.7462   | 0.7457   | 0.0505 | 0.0808 |
| 0.3718        | 15.0  | 3750  | 0.4228          | 0.7518   | 0.3460     | 2.4046 | 0.7518   | 0.7519   | 0.0466 | 0.0772 |
| 0.3526        | 16.0  | 4000  | 0.4276          | 0.7565   | 0.3472     | 2.3786 | 0.7565   | 0.7568   | 0.0446 | 0.0792 |
| 0.3526        | 17.0  | 4250  | 0.4158          | 0.7588   | 0.3393     | 2.2729 | 0.7588   | 0.7573   | 0.0549 | 0.0743 |
| 0.3366        | 18.0  | 4500  | 0.4108          | 0.7702   | 0.3278     | 2.1763 | 0.7702   | 0.7710   | 0.0503 | 0.0694 |
| 0.3366        | 19.0  | 4750  | 0.4132          | 0.762    | 0.3333     | 2.2475 | 0.762    | 0.7625   | 0.0435 | 0.0720 |
| 0.3236        | 20.0  | 5000  | 0.4083          | 0.7608   | 0.3318     | 2.1875 | 0.7608   | 0.7625   | 0.0391 | 0.0718 |
| 0.3236        | 21.0  | 5250  | 0.4291          | 0.7525   | 0.3419     | 2.3114 | 0.7525   | 0.7530   | 0.0481 | 0.0762 |
| 0.3124        | 22.0  | 5500  | 0.4160          | 0.7648   | 0.3315     | 2.2708 | 0.7648   | 0.7649   | 0.0455 | 0.0714 |
| 0.3124        | 23.0  | 5750  | 0.4144          | 0.759    | 0.3337     | 2.2574 | 0.7590   | 0.7593   | 0.0408 | 0.0747 |
| 0.3029        | 24.0  | 6000  | 0.4066          | 0.7665   | 0.3267     | 2.1274 | 0.7665   | 0.7671   | 0.0424 | 0.0706 |
| 0.3029        | 25.0  | 6250  | 0.4159          | 0.7612   | 0.3359     | 2.3038 | 0.7612   | 0.7620   | 0.0450 | 0.0733 |
| 0.295         | 26.0  | 6500  | 0.4049          | 0.7722   | 0.3241     | 2.1874 | 0.7722   | 0.7739   | 0.0425 | 0.0691 |
| 0.295         | 27.0  | 6750  | 0.4047          | 0.7735   | 0.3236     | 2.1528 | 0.7735   | 0.7733   | 0.0474 | 0.0693 |
| 0.2873        | 28.0  | 7000  | 0.4052          | 0.773    | 0.3239     | 2.1392 | 0.7730   | 0.7740   | 0.0439 | 0.0689 |
| 0.2873        | 29.0  | 7250  | 0.4126          | 0.7675   | 0.3301     | 2.2627 | 0.7675   | 0.7661   | 0.0355 | 0.0716 |
| 0.2811        | 30.0  | 7500  | 0.4064          | 0.7708   | 0.3265     | 2.1093 | 0.7707   | 0.7734   | 0.0433 | 0.0708 |
| 0.2811        | 31.0  | 7750  | 0.4030          | 0.7742   | 0.3214     | 2.1570 | 0.7742   | 0.7748   | 0.0426 | 0.0683 |
| 0.2747        | 32.0  | 8000  | 0.4041          | 0.7762   | 0.3172     | 2.0937 | 0.7762   | 0.7766   | 0.0430 | 0.0661 |
| 0.2747        | 33.0  | 8250  | 0.4031          | 0.7795   | 0.3176     | 2.1283 | 0.7795   | 0.7788   | 0.0433 | 0.0661 |
| 0.2698        | 34.0  | 8500  | 0.4024          | 0.7758   | 0.3209     | 2.1024 | 0.7758   | 0.7762   | 0.0442 | 0.0679 |
| 0.2698        | 35.0  | 8750  | 0.4024          | 0.7725   | 0.3205     | 2.1210 | 0.7725   | 0.7736   | 0.0424 | 0.0673 |
| 0.2652        | 36.0  | 9000  | 0.4064          | 0.769    | 0.3251     | 2.1974 | 0.769    | 0.7680   | 0.0479 | 0.0698 |
| 0.2652        | 37.0  | 9250  | 0.4016          | 0.779    | 0.3191     | 2.0764 | 0.779    | 0.7796   | 0.0431 | 0.0678 |
| 0.2612        | 38.0  | 9500  | 0.4028          | 0.7735   | 0.3185     | 2.1391 | 0.7735   | 0.7733   | 0.0411 | 0.0668 |
| 0.2612        | 39.0  | 9750  | 0.4031          | 0.7782   | 0.3164     | 2.0988 | 0.7782   | 0.7787   | 0.0349 | 0.0657 |
| 0.2572        | 40.0  | 10000 | 0.4039          | 0.778    | 0.3178     | 2.1040 | 0.778    | 0.7782   | 0.0399 | 0.0664 |
| 0.2572        | 41.0  | 10250 | 0.4025          | 0.7745   | 0.3184     | 2.1274 | 0.7745   | 0.7747   | 0.0389 | 0.0668 |
| 0.2546        | 42.0  | 10500 | 0.4040          | 0.7752   | 0.3184     | 2.1421 | 0.7752   | 0.7754   | 0.0386 | 0.0660 |
| 0.2546        | 43.0  | 10750 | 0.4022          | 0.7775   | 0.3171     | 2.0920 | 0.7775   | 0.7778   | 0.0450 | 0.0663 |
| 0.2516        | 44.0  | 11000 | 0.4023          | 0.776    | 0.3164     | 2.1088 | 0.776    | 0.7771   | 0.0418 | 0.0658 |
| 0.2516        | 45.0  | 11250 | 0.4037          | 0.773    | 0.3187     | 2.0976 | 0.7730   | 0.7738   | 0.0448 | 0.0669 |
| 0.2493        | 46.0  | 11500 | 0.4027          | 0.7758   | 0.3174     | 2.1363 | 0.7758   | 0.7761   | 0.0420 | 0.0660 |
| 0.2493        | 47.0  | 11750 | 0.4028          | 0.7742   | 0.3186     | 2.1218 | 0.7742   | 0.7744   | 0.0408 | 0.0666 |
| 0.2473        | 48.0  | 12000 | 0.4028          | 0.7762   | 0.3162     | 2.1082 | 0.7762   | 0.7764   | 0.0472 | 0.0658 |
| 0.2473        | 49.0  | 12250 | 0.4028          | 0.7755   | 0.3172     | 2.0818 | 0.7755   | 0.7759   | 0.0437 | 0.0662 |
| 0.2462        | 50.0  | 12500 | 0.4025          | 0.7765   | 0.3168     | 2.1020 | 0.7765   | 0.7763   | 0.0442 | 0.0661 |


### Framework versions

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

letter form email handwritten advertisement scientific_report scientific_publication specification file_folder news_article budget invoice presentation questionnaire resume memo