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resnet101_rvl-cdip-_rvl_cdip-NK1000__CEKD_t2.5_a0.5

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-_rvl_cdip-NK1000__CEKD_t2.5_a0.5
    results: []

resnet101_rvl-cdip-_rvl_cdip-NK1000__CEKD_t2.5_a0.5

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: 0.6065
  • Accuracy: 0.7915
  • Brier Loss: 0.3054
  • Nll: 1.9957
  • F1 Micro: 0.7915
  • F1 Macro: 0.7910
  • Ece: 0.0453
  • Aurc: 0.0607

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 4.1565 0.1378 0.9318 7.9039 0.1378 0.1073 0.0673 0.8326
4.1485 2.0 500 3.6932 0.3235 0.8832 5.1525 0.3235 0.2725 0.2044 0.5507
4.1485 3.0 750 2.3374 0.4725 0.6611 3.3127 0.4725 0.4311 0.0839 0.2921
2.392 4.0 1000 1.6516 0.588 0.5470 2.8681 0.588 0.5789 0.0620 0.1929
2.392 5.0 1250 1.3260 0.6488 0.4782 2.6378 0.6488 0.6444 0.0486 0.1458
1.1422 6.0 1500 1.0390 0.702 0.4156 2.4086 0.702 0.7029 0.0576 0.1097
1.1422 7.0 1750 0.8420 0.7288 0.3738 2.2222 0.7288 0.7300 0.0553 0.0888
0.708 8.0 2000 0.7753 0.7398 0.3586 2.1518 0.7398 0.7396 0.0587 0.0826
0.708 9.0 2250 0.7797 0.7462 0.3580 2.1095 0.7462 0.7457 0.0581 0.0820
0.5195 10.0 2500 0.7101 0.7602 0.3404 2.0711 0.7602 0.7612 0.0473 0.0733
0.5195 11.0 2750 0.6971 0.7645 0.3338 2.0649 0.7645 0.7653 0.0541 0.0715
0.4176 12.0 3000 0.6936 0.7712 0.3302 2.0265 0.7712 0.7708 0.0515 0.0702
0.4176 13.0 3250 0.6991 0.7662 0.3346 2.0582 0.7663 0.7657 0.0581 0.0723
0.3573 14.0 3500 0.6672 0.7722 0.3246 2.0053 0.7722 0.7723 0.0551 0.0683
0.3573 15.0 3750 0.6735 0.777 0.3244 2.0387 0.777 0.7782 0.0488 0.0671
0.3193 16.0 4000 0.6567 0.776 0.3216 2.0256 0.776 0.7773 0.0499 0.0678
0.3193 17.0 4250 0.6498 0.78 0.3184 1.9865 0.78 0.7802 0.0477 0.0662
0.2893 18.0 4500 0.6763 0.7755 0.3264 2.0844 0.7755 0.7755 0.0531 0.0697
0.2893 19.0 4750 0.6519 0.7815 0.3183 2.0458 0.7815 0.7817 0.0513 0.0658
0.271 20.0 5000 0.6432 0.7823 0.3147 2.0291 0.7823 0.7827 0.0440 0.0645
0.271 21.0 5250 0.6456 0.781 0.3156 2.0493 0.7810 0.7813 0.0487 0.0652
0.2516 22.0 5500 0.6336 0.7823 0.3144 1.9829 0.7823 0.7822 0.0522 0.0642
0.2516 23.0 5750 0.6333 0.7837 0.3128 2.0196 0.7837 0.7836 0.0492 0.0641
0.2397 24.0 6000 0.6337 0.7817 0.3147 2.0180 0.7817 0.7815 0.0494 0.0644
0.2397 25.0 6250 0.6347 0.7857 0.3145 2.0187 0.7857 0.7856 0.0510 0.0641
0.23 26.0 6500 0.6311 0.7815 0.3129 2.0132 0.7815 0.7819 0.0495 0.0637
0.23 27.0 6750 0.6329 0.7853 0.3125 2.0708 0.7853 0.7852 0.0502 0.0635
0.2191 28.0 7000 0.6222 0.786 0.3109 2.0022 0.786 0.7856 0.0483 0.0638
0.2191 29.0 7250 0.6195 0.7863 0.3096 2.0028 0.7863 0.7859 0.0550 0.0620
0.2155 30.0 7500 0.6196 0.7883 0.3090 1.9972 0.7883 0.7883 0.0486 0.0624
0.2155 31.0 7750 0.6167 0.787 0.3080 2.0173 0.787 0.7871 0.0443 0.0623
0.2074 32.0 8000 0.6143 0.7897 0.3073 2.0223 0.7897 0.7893 0.0443 0.0614
0.2074 33.0 8250 0.6123 0.787 0.3078 1.9869 0.787 0.7866 0.0458 0.0619
0.2028 34.0 8500 0.6137 0.7873 0.3070 1.9883 0.7873 0.7868 0.0457 0.0623
0.2028 35.0 8750 0.6152 0.786 0.3085 2.0108 0.786 0.7863 0.0497 0.0626
0.1982 36.0 9000 0.6133 0.7863 0.3077 2.0205 0.7863 0.7862 0.0515 0.0615
0.1982 37.0 9250 0.6145 0.7877 0.3081 1.9930 0.7877 0.7879 0.0444 0.0621
0.1948 38.0 9500 0.6116 0.7857 0.3078 2.0072 0.7857 0.7854 0.0508 0.0619
0.1948 39.0 9750 0.6090 0.788 0.3059 1.9954 0.788 0.7882 0.0430 0.0614
0.1933 40.0 10000 0.6143 0.7897 0.3072 1.9943 0.7897 0.7899 0.0462 0.0618
0.1933 41.0 10250 0.6061 0.7887 0.3041 1.9900 0.7887 0.7889 0.0439 0.0606
0.1882 42.0 10500 0.6070 0.7865 0.3058 1.9907 0.7865 0.7868 0.0438 0.0607
0.1882 43.0 10750 0.6083 0.788 0.3054 2.0095 0.788 0.7877 0.0489 0.0608
0.1871 44.0 11000 0.6083 0.787 0.3054 1.9828 0.787 0.7872 0.0469 0.0607
0.1871 45.0 11250 0.6092 0.7893 0.3057 2.0140 0.7893 0.7891 0.0483 0.0608
0.1862 46.0 11500 0.6057 0.7893 0.3053 2.0064 0.7893 0.7890 0.0450 0.0609
0.1862 47.0 11750 0.6042 0.79 0.3044 1.9691 0.79 0.7899 0.0435 0.0607
0.1845 48.0 12000 0.6068 0.79 0.3053 2.0052 0.79 0.7899 0.0438 0.0608
0.1845 49.0 12250 0.6081 0.7893 0.3062 2.0117 0.7893 0.7890 0.0485 0.0612
0.1836 50.0 12500 0.6065 0.7915 0.3054 1.9957 0.7915 0.7910 0.0453 0.0607

Framework versions

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