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resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_og_simkd

Image Classification
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PyTorch
resnet
Generated from Trainer
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metadata
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: []

resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_og_simkd

This model is a fine-tuned version of 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