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resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_kd_MSE

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: 0.7429
  • Accuracy: 0.7853
  • Brier Loss: 0.3044
  • Nll: 2.0364
  • F1 Micro: 0.7853
  • F1 Macro: 0.7862
  • Ece: 0.0430
  • Aurc: 0.0599

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: 100

Training results

Training Loss Epoch Step Validation Loss Accuracy Brier Loss Nll F1 Micro F1 Macro Ece Aurc
No log 1.0 250 9.5443 0.0765 0.9365 3.7373 0.0765 0.0522 0.0360 0.9336
9.4095 2.0 500 7.4542 0.0757 0.9312 2.8468 0.0757 0.0316 0.0425 0.8840
9.4095 3.0 750 5.8933 0.0975 0.9356 3.2058 0.0975 0.0408 0.0798 0.8593
5.9994 4.0 1000 4.3665 0.2125 0.8700 5.3759 0.2125 0.1290 0.0743 0.7029
5.9994 5.0 1250 3.0367 0.4415 0.6924 4.9073 0.4415 0.4283 0.0806 0.3570
3.2184 6.0 1500 2.1589 0.579 0.5587 3.7412 0.579 0.5771 0.0572 0.2172
3.2184 7.0 1750 1.5582 0.652 0.4673 3.0701 0.652 0.6456 0.0517 0.1478
1.6737 8.0 2000 1.3502 0.6893 0.4266 2.8575 0.6893 0.6860 0.0544 0.1175
1.6737 9.0 2250 1.1389 0.7188 0.3914 2.5937 0.7188 0.7195 0.0544 0.1006
1.0789 10.0 2500 1.0563 0.7302 0.3742 2.5043 0.7302 0.7305 0.0618 0.0912
1.0789 11.0 2750 1.0035 0.7428 0.3604 2.4375 0.7428 0.7441 0.0587 0.0823
0.7934 12.0 3000 0.9169 0.7548 0.3472 2.2921 0.7548 0.7555 0.0547 0.0762
0.7934 13.0 3250 0.8628 0.7598 0.3386 2.2849 0.7598 0.7600 0.0550 0.0739
0.6268 14.0 3500 0.8773 0.7675 0.3362 2.2170 0.7675 0.7692 0.0490 0.0718
0.6268 15.0 3750 0.8263 0.7682 0.3306 2.1617 0.7682 0.7702 0.0534 0.0704
0.5269 16.0 4000 0.8422 0.7708 0.3289 2.1907 0.7707 0.7717 0.0524 0.0687
0.5269 17.0 4250 0.8100 0.7745 0.3241 2.1664 0.7745 0.7761 0.0509 0.0667
0.4516 18.0 4500 0.8013 0.7778 0.3215 2.1216 0.7778 0.7790 0.0473 0.0669
0.4516 19.0 4750 0.7911 0.7802 0.3183 2.1224 0.7802 0.7812 0.0476 0.0648
0.4039 20.0 5000 0.7900 0.7775 0.3197 2.0969 0.7775 0.7797 0.0473 0.0647
0.4039 21.0 5250 0.7919 0.7792 0.3191 2.1445 0.7792 0.7810 0.0531 0.0652
0.3563 22.0 5500 0.7960 0.7802 0.3166 2.0849 0.7802 0.7818 0.0478 0.0649
0.3563 23.0 5750 0.7615 0.7825 0.3128 2.0834 0.7825 0.7833 0.0478 0.0638
0.3251 24.0 6000 0.7840 0.7792 0.3151 2.0841 0.7792 0.7800 0.0513 0.0648
0.3251 25.0 6250 0.7837 0.7792 0.3159 2.0889 0.7792 0.7808 0.0485 0.0643
0.2949 26.0 6500 0.7827 0.7802 0.3158 2.0416 0.7802 0.7819 0.0548 0.0648
0.2949 27.0 6750 0.7650 0.78 0.3130 2.0411 0.78 0.7807 0.0506 0.0629
0.2669 28.0 7000 0.7787 0.7802 0.3133 2.0843 0.7802 0.7810 0.0454 0.0627
0.2669 29.0 7250 0.7892 0.782 0.3163 2.0953 0.782 0.7826 0.0508 0.0635
0.2512 30.0 7500 0.7775 0.7825 0.3126 2.0904 0.7825 0.7837 0.0451 0.0633
0.2512 31.0 7750 0.7601 0.7817 0.3124 2.0251 0.7817 0.7827 0.0485 0.0627
0.231 32.0 8000 0.7669 0.7833 0.3120 2.0685 0.7833 0.7842 0.0472 0.0629
0.231 33.0 8250 0.7652 0.7847 0.3116 2.0661 0.7847 0.7858 0.0519 0.0625
0.2172 34.0 8500 0.7637 0.7837 0.3107 2.0264 0.7837 0.7852 0.0487 0.0628
0.2172 35.0 8750 0.7691 0.783 0.3120 2.0535 0.7830 0.7844 0.0438 0.0634
0.2032 36.0 9000 0.7647 0.7845 0.3093 2.0480 0.7845 0.7852 0.0471 0.0620
0.2032 37.0 9250 0.7727 0.782 0.3122 2.0610 0.782 0.7830 0.0493 0.0628
0.1925 38.0 9500 0.7563 0.7843 0.3085 2.0267 0.7843 0.7849 0.0459 0.0608
0.1925 39.0 9750 0.7597 0.7835 0.3087 2.0062 0.7835 0.7845 0.0485 0.0614
0.1823 40.0 10000 0.7611 0.7833 0.3107 2.0007 0.7833 0.7853 0.0479 0.0625
0.1823 41.0 10250 0.7608 0.7843 0.3076 2.0335 0.7843 0.7854 0.0486 0.0602
0.17 42.0 10500 0.7535 0.7833 0.3096 2.0121 0.7833 0.7844 0.0505 0.0613
0.17 43.0 10750 0.7524 0.7845 0.3066 2.0425 0.7845 0.7856 0.0476 0.0605
0.1639 44.0 11000 0.7608 0.7808 0.3108 2.0739 0.7808 0.7816 0.0503 0.0618
0.1639 45.0 11250 0.7560 0.786 0.3063 1.9876 0.786 0.7868 0.0496 0.0607
0.1575 46.0 11500 0.7494 0.784 0.3063 2.0311 0.7840 0.7846 0.0416 0.0601
0.1575 47.0 11750 0.7515 0.7857 0.3069 2.0539 0.7857 0.7866 0.0456 0.0609
0.1493 48.0 12000 0.7511 0.7843 0.3086 2.0325 0.7843 0.7852 0.0552 0.0612
0.1493 49.0 12250 0.7495 0.787 0.3067 2.0231 0.787 0.7880 0.0475 0.0605
0.1425 50.0 12500 0.7538 0.7867 0.3052 2.0267 0.7868 0.7870 0.0507 0.0603
0.1425 51.0 12750 0.7529 0.7847 0.3081 2.0592 0.7847 0.7859 0.0467 0.0604
0.1356 52.0 13000 0.7527 0.7808 0.3071 2.0349 0.7808 0.7818 0.0473 0.0607
0.1356 53.0 13250 0.7451 0.7865 0.3049 2.0368 0.7865 0.7879 0.0484 0.0595
0.1325 54.0 13500 0.7481 0.7857 0.3056 2.0223 0.7857 0.7869 0.0468 0.0603
0.1325 55.0 13750 0.7470 0.7835 0.3057 2.0306 0.7835 0.7844 0.0492 0.0601
0.1264 56.0 14000 0.7471 0.7873 0.3053 2.0336 0.7873 0.7880 0.0519 0.0601
0.1264 57.0 14250 0.7429 0.7895 0.3032 2.0149 0.7895 0.7903 0.0468 0.0595
0.1208 58.0 14500 0.7399 0.7885 0.3035 2.0147 0.7885 0.7895 0.0433 0.0596
0.1208 59.0 14750 0.7518 0.786 0.3076 2.0481 0.786 0.7873 0.0403 0.0607
0.119 60.0 15000 0.7483 0.7903 0.3058 2.0138 0.7903 0.7914 0.0471 0.0601
0.119 61.0 15250 0.7463 0.7845 0.3043 2.0617 0.7845 0.7855 0.0458 0.0599
0.1128 62.0 15500 0.7478 0.7875 0.3056 2.0187 0.7875 0.7888 0.0452 0.0604
0.1128 63.0 15750 0.7510 0.784 0.3061 2.0204 0.7840 0.7850 0.0495 0.0605
0.1109 64.0 16000 0.7424 0.786 0.3053 2.0167 0.786 0.7871 0.0449 0.0603
0.1109 65.0 16250 0.7473 0.7885 0.3054 2.0200 0.7885 0.7893 0.0471 0.0600
0.1078 66.0 16500 0.7467 0.7873 0.3054 2.0224 0.7873 0.7883 0.0482 0.0599
0.1078 67.0 16750 0.7445 0.7893 0.3039 2.0082 0.7893 0.7895 0.0456 0.0593
0.1051 68.0 17000 0.7490 0.7873 0.3063 2.0152 0.7873 0.7883 0.0505 0.0602
0.1051 69.0 17250 0.7490 0.785 0.3061 2.0103 0.785 0.7861 0.0465 0.0602
0.1009 70.0 17500 0.7445 0.7875 0.3049 2.0308 0.7875 0.7884 0.0483 0.0598
0.1009 71.0 17750 0.7490 0.7863 0.3068 2.0260 0.7863 0.7875 0.0495 0.0604
0.0984 72.0 18000 0.7465 0.7893 0.3059 2.0161 0.7893 0.7906 0.0427 0.0601
0.0984 73.0 18250 0.7451 0.7873 0.3058 2.0204 0.7873 0.7882 0.0511 0.0605
0.0966 74.0 18500 0.7445 0.7875 0.3042 2.0227 0.7875 0.7886 0.0495 0.0599
0.0966 75.0 18750 0.7443 0.7863 0.3040 2.0138 0.7863 0.7872 0.0442 0.0598
0.0947 76.0 19000 0.7448 0.7865 0.3054 2.0234 0.7865 0.7873 0.0457 0.0598
0.0947 77.0 19250 0.7448 0.7865 0.3041 2.0110 0.7865 0.7875 0.0508 0.0596
0.0931 78.0 19500 0.7460 0.7883 0.3040 2.0125 0.7883 0.7895 0.0467 0.0595
0.0931 79.0 19750 0.7456 0.7883 0.3038 2.0302 0.7883 0.7894 0.0455 0.0596
0.0899 80.0 20000 0.7469 0.788 0.3040 2.0188 0.788 0.7892 0.0487 0.0597
0.0899 81.0 20250 0.7421 0.788 0.3041 2.0359 0.788 0.7888 0.0427 0.0595
0.0882 82.0 20500 0.7444 0.7865 0.3051 2.0219 0.7865 0.7875 0.0479 0.0600
0.0882 83.0 20750 0.7439 0.788 0.3039 2.0197 0.788 0.7894 0.0439 0.0597
0.0871 84.0 21000 0.7421 0.7865 0.3040 1.9910 0.7865 0.7876 0.0445 0.0598
0.0871 85.0 21250 0.7429 0.7887 0.3043 2.0253 0.7887 0.7898 0.0426 0.0597
0.0869 86.0 21500 0.7442 0.7873 0.3041 2.0156 0.7873 0.7885 0.0488 0.0596
0.0869 87.0 21750 0.7439 0.7857 0.3051 2.0099 0.7857 0.7867 0.0465 0.0599
0.084 88.0 22000 0.7434 0.786 0.3040 1.9926 0.786 0.7869 0.0469 0.0598
0.084 89.0 22250 0.7431 0.7873 0.3048 2.0028 0.7873 0.7880 0.0442 0.0599
0.0821 90.0 22500 0.7447 0.7867 0.3040 2.0349 0.7868 0.7876 0.0477 0.0596
0.0821 91.0 22750 0.7443 0.7877 0.3051 2.0356 0.7877 0.7887 0.0486 0.0601
0.0813 92.0 23000 0.7500 0.7873 0.3053 2.0465 0.7873 0.7880 0.0484 0.0601
0.0813 93.0 23250 0.7449 0.788 0.3037 1.9966 0.788 0.7890 0.0441 0.0594
0.0811 94.0 23500 0.7466 0.7897 0.3048 2.0297 0.7897 0.7907 0.0429 0.0600
0.0811 95.0 23750 0.7482 0.7875 0.3058 2.0319 0.7875 0.7885 0.0464 0.0601
0.0808 96.0 24000 0.7473 0.7863 0.3055 2.0219 0.7863 0.7874 0.0477 0.0603
0.0808 97.0 24250 0.7451 0.7855 0.3044 2.0356 0.7855 0.7865 0.0481 0.0594
0.08 98.0 24500 0.7442 0.7857 0.3042 2.0213 0.7857 0.7868 0.0475 0.0595
0.08 99.0 24750 0.7462 0.7863 0.3053 2.0354 0.7863 0.7874 0.0425 0.0599
0.079 100.0 25000 0.7429 0.7853 0.3044 2.0364 0.7853 0.7862 0.0430 0.0599

Framework versions

  • Transformers 4.33.3
  • Pytorch 2.2.0.dev20231002
  • Datasets 2.7.1
  • Tokenizers 0.13.3
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