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---
license: apache-2.0
base_model: microsoft/resnet-50
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_og_simkd
results: []
---
<!-- 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
This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3748
- Accuracy: 0.8023
- Brier Loss: 0.2845
- Nll: 1.8818
- F1 Micro: 0.8023
- F1 Macro: 0.8020
- Ece: 0.0375
- Aurc: 0.0534
## 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 | 0.8880 | 0.1955 | 0.8872 | 5.3865 | 0.1955 | 0.1551 | 0.0582 | 0.7111 |
| 0.9199 | 2.0 | 500 | 0.6464 | 0.407 | 0.7284 | 5.2363 | 0.4070 | 0.3745 | 0.0770 | 0.4284 |
| 0.9199 | 3.0 | 750 | 0.5608 | 0.5945 | 0.5337 | 3.5976 | 0.5945 | 0.5912 | 0.0561 | 0.1950 |
| 0.563 | 4.0 | 1000 | 0.4962 | 0.6905 | 0.4235 | 2.6948 | 0.6905 | 0.6885 | 0.0474 | 0.1170 |
| 0.563 | 5.0 | 1250 | 0.4613 | 0.7177 | 0.3858 | 2.5472 | 0.7178 | 0.7181 | 0.0512 | 0.0964 |
| 0.4567 | 6.0 | 1500 | 0.4372 | 0.742 | 0.3584 | 2.3396 | 0.7420 | 0.7425 | 0.0527 | 0.0824 |
| 0.4567 | 7.0 | 1750 | 0.4271 | 0.7595 | 0.3406 | 2.2123 | 0.7595 | 0.7596 | 0.0459 | 0.0756 |
| 0.4103 | 8.0 | 2000 | 0.4129 | 0.7658 | 0.3308 | 2.1667 | 0.7658 | 0.7666 | 0.0439 | 0.0704 |
| 0.4103 | 9.0 | 2250 | 0.4070 | 0.7678 | 0.3296 | 2.1663 | 0.7678 | 0.7692 | 0.0485 | 0.0699 |
| 0.3836 | 10.0 | 2500 | 0.4017 | 0.7725 | 0.3209 | 2.1207 | 0.7725 | 0.7732 | 0.0426 | 0.0667 |
| 0.3836 | 11.0 | 2750 | 0.3984 | 0.7768 | 0.3153 | 2.0353 | 0.7768 | 0.7771 | 0.0454 | 0.0651 |
| 0.3645 | 12.0 | 3000 | 0.3961 | 0.7752 | 0.3124 | 2.0755 | 0.7752 | 0.7754 | 0.0428 | 0.0642 |
| 0.3645 | 13.0 | 3250 | 0.3961 | 0.786 | 0.3071 | 1.9949 | 0.786 | 0.7861 | 0.0407 | 0.0612 |
| 0.3497 | 14.0 | 3500 | 0.3899 | 0.7823 | 0.3053 | 1.9769 | 0.7823 | 0.7823 | 0.0435 | 0.0606 |
| 0.3497 | 15.0 | 3750 | 0.3873 | 0.7853 | 0.3021 | 1.9881 | 0.7853 | 0.7849 | 0.0479 | 0.0594 |
| 0.3378 | 16.0 | 4000 | 0.3861 | 0.7833 | 0.3026 | 1.9263 | 0.7833 | 0.7834 | 0.0431 | 0.0593 |
| 0.3378 | 17.0 | 4250 | 0.3853 | 0.7913 | 0.2970 | 1.9108 | 0.7913 | 0.7917 | 0.0390 | 0.0571 |
| 0.3271 | 18.0 | 4500 | 0.3840 | 0.7903 | 0.2978 | 1.9643 | 0.7903 | 0.7902 | 0.0377 | 0.0576 |
| 0.3271 | 19.0 | 4750 | 0.3828 | 0.7915 | 0.2967 | 1.9332 | 0.7915 | 0.7914 | 0.0393 | 0.0572 |
| 0.3186 | 20.0 | 5000 | 0.3806 | 0.7913 | 0.2938 | 1.9410 | 0.7913 | 0.7909 | 0.0410 | 0.0563 |
| 0.3186 | 21.0 | 5250 | 0.3815 | 0.7953 | 0.2921 | 1.9285 | 0.7953 | 0.7949 | 0.0387 | 0.0566 |
| 0.3111 | 22.0 | 5500 | 0.3838 | 0.7895 | 0.2949 | 1.9126 | 0.7895 | 0.7894 | 0.0382 | 0.0570 |
| 0.3111 | 23.0 | 5750 | 0.3799 | 0.7955 | 0.2902 | 1.9332 | 0.7955 | 0.7955 | 0.0373 | 0.0558 |
| 0.305 | 24.0 | 6000 | 0.3796 | 0.7947 | 0.2912 | 1.8615 | 0.7947 | 0.7940 | 0.0418 | 0.0561 |
| 0.305 | 25.0 | 6250 | 0.3805 | 0.7947 | 0.2912 | 1.8999 | 0.7947 | 0.7940 | 0.0413 | 0.0558 |
| 0.2993 | 26.0 | 6500 | 0.3842 | 0.7925 | 0.2913 | 1.9451 | 0.7925 | 0.7927 | 0.0339 | 0.0559 |
| 0.2993 | 27.0 | 6750 | 0.3784 | 0.794 | 0.2908 | 1.9151 | 0.7940 | 0.7942 | 0.0389 | 0.0553 |
| 0.2943 | 28.0 | 7000 | 0.3779 | 0.7957 | 0.2895 | 1.8758 | 0.7957 | 0.7957 | 0.0392 | 0.0549 |
| 0.2943 | 29.0 | 7250 | 0.3776 | 0.7955 | 0.2892 | 1.8785 | 0.7955 | 0.7947 | 0.0445 | 0.0549 |
| 0.2905 | 30.0 | 7500 | 0.3775 | 0.7973 | 0.2879 | 1.8786 | 0.7973 | 0.7972 | 0.0379 | 0.0550 |
| 0.2905 | 31.0 | 7750 | 0.3773 | 0.7945 | 0.2903 | 1.9039 | 0.7945 | 0.7942 | 0.0405 | 0.0551 |
| 0.2863 | 32.0 | 8000 | 0.3764 | 0.7963 | 0.2880 | 1.8569 | 0.7963 | 0.7962 | 0.0375 | 0.0549 |
| 0.2863 | 33.0 | 8250 | 0.3775 | 0.7925 | 0.2884 | 1.9070 | 0.7925 | 0.7917 | 0.0411 | 0.0544 |
| 0.2831 | 34.0 | 8500 | 0.3762 | 0.7935 | 0.2873 | 1.8608 | 0.7935 | 0.7933 | 0.0389 | 0.0547 |
| 0.2831 | 35.0 | 8750 | 0.3765 | 0.7973 | 0.2868 | 1.9316 | 0.7973 | 0.7970 | 0.0385 | 0.0540 |
| 0.28 | 36.0 | 9000 | 0.3750 | 0.7967 | 0.2857 | 1.8871 | 0.7967 | 0.7965 | 0.0375 | 0.0540 |
| 0.28 | 37.0 | 9250 | 0.3761 | 0.793 | 0.2874 | 1.8977 | 0.793 | 0.7926 | 0.0405 | 0.0543 |
| 0.2775 | 38.0 | 9500 | 0.3760 | 0.7983 | 0.2861 | 1.8613 | 0.7983 | 0.7987 | 0.0422 | 0.0540 |
| 0.2775 | 39.0 | 9750 | 0.3761 | 0.7955 | 0.2870 | 1.8744 | 0.7955 | 0.7957 | 0.0412 | 0.0545 |
| 0.2755 | 40.0 | 10000 | 0.3753 | 0.8007 | 0.2852 | 1.8640 | 0.8007 | 0.8006 | 0.0345 | 0.0532 |
| 0.2755 | 41.0 | 10250 | 0.3753 | 0.8023 | 0.2857 | 1.8637 | 0.8023 | 0.8025 | 0.0363 | 0.0535 |
| 0.2735 | 42.0 | 10500 | 0.3751 | 0.7995 | 0.2851 | 1.9134 | 0.7995 | 0.7994 | 0.0403 | 0.0531 |
| 0.2735 | 43.0 | 10750 | 0.3753 | 0.8 | 0.2857 | 1.8832 | 0.8000 | 0.7996 | 0.0406 | 0.0538 |
| 0.2717 | 44.0 | 11000 | 0.3746 | 0.7985 | 0.2851 | 1.8545 | 0.7985 | 0.7982 | 0.0432 | 0.0532 |
| 0.2717 | 45.0 | 11250 | 0.3747 | 0.7985 | 0.2847 | 1.8730 | 0.7985 | 0.7984 | 0.0400 | 0.0534 |
| 0.2701 | 46.0 | 11500 | 0.3744 | 0.801 | 0.2843 | 1.8783 | 0.801 | 0.8007 | 0.0411 | 0.0532 |
| 0.2701 | 47.0 | 11750 | 0.3744 | 0.798 | 0.2852 | 1.8843 | 0.798 | 0.7975 | 0.0420 | 0.0535 |
| 0.2694 | 48.0 | 12000 | 0.3753 | 0.7993 | 0.2857 | 1.8875 | 0.7993 | 0.7988 | 0.0405 | 0.0532 |
| 0.2694 | 49.0 | 12250 | 0.3758 | 0.7965 | 0.2868 | 1.8927 | 0.7965 | 0.7964 | 0.0415 | 0.0539 |
| 0.2684 | 50.0 | 12500 | 0.3748 | 0.8023 | 0.2845 | 1.8818 | 0.8023 | 0.8020 | 0.0375 | 0.0534 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.2.0.dev20231002
- Datasets 2.7.1
- Tokenizers 0.13.3
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