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resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_kd_CEKD_t1.0_a1.0

Image Classification
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PyTorch
resnet
Generated from Trainer
Inference Endpoints
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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_kd_CEKD_t1.0_a1.0
    results: []

resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_kd_CEKD_t1.0_a1.0

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: 1.7844
  • Accuracy: 0.742
  • Brier Loss: 0.4405
  • Nll: 2.8680
  • F1 Micro: 0.7420
  • F1 Macro: 0.7411
  • Ece: 0.1946
  • Aurc: 0.1002

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 2.7345 0.153 0.9327 8.3371 0.153 0.1246 0.0866 0.7933
2.6983 2.0 500 2.4500 0.4213 0.8816 4.7062 0.4213 0.3924 0.3073 0.4444
2.6983 3.0 750 1.7959 0.5012 0.7003 3.3576 0.5012 0.4758 0.1869 0.3051
1.7341 4.0 1000 1.3637 0.5985 0.5511 2.8818 0.5985 0.5868 0.1005 0.1935
1.7341 5.0 1250 1.1978 0.6498 0.4862 2.7546 0.6498 0.6471 0.0826 0.1500
1.0818 6.0 1500 1.0812 0.6853 0.4364 2.6325 0.6853 0.6845 0.0522 0.1217
1.0818 7.0 1750 1.0276 0.7013 0.4149 2.5542 0.7013 0.7003 0.0397 0.1108
0.7498 8.0 2000 0.9724 0.7133 0.3944 2.4773 0.7133 0.7129 0.0505 0.1040
0.7498 9.0 2250 0.9777 0.7248 0.3924 2.4916 0.7248 0.7242 0.0628 0.0992
0.5034 10.0 2500 1.0027 0.724 0.3976 2.4974 0.724 0.7250 0.0751 0.1032
0.5034 11.0 2750 0.9979 0.729 0.3913 2.5344 0.729 0.7295 0.0805 0.0988
0.3237 12.0 3000 1.0553 0.7192 0.4075 2.6242 0.7192 0.7193 0.0963 0.1072
0.3237 13.0 3250 1.1162 0.7175 0.4139 2.6543 0.7175 0.7185 0.1295 0.1093
0.2023 14.0 3500 1.1259 0.725 0.4140 2.6758 0.7250 0.7246 0.1237 0.1055
0.2023 15.0 3750 1.2728 0.7115 0.4381 2.8308 0.7115 0.7147 0.1464 0.1168
0.1264 16.0 4000 1.2664 0.7222 0.4296 2.8434 0.7223 0.7236 0.1523 0.1107
0.1264 17.0 4250 1.2620 0.724 0.4252 2.7990 0.724 0.7252 0.1563 0.1066
0.0802 18.0 4500 1.3362 0.727 0.4293 2.8642 0.7270 0.7267 0.1653 0.1090
0.0802 19.0 4750 1.3608 0.7302 0.4288 2.7893 0.7302 0.7318 0.1637 0.1059
0.0553 20.0 5000 1.3757 0.7308 0.4303 2.8861 0.7308 0.7300 0.1670 0.1073
0.0553 21.0 5250 1.4947 0.7295 0.4420 2.8306 0.7295 0.7300 0.1770 0.1128
0.0329 22.0 5500 1.5338 0.7265 0.4416 2.8729 0.7265 0.7273 0.1808 0.1097
0.0329 23.0 5750 1.5127 0.7355 0.4362 2.8574 0.7355 0.7366 0.1774 0.1045
0.0258 24.0 6000 1.5189 0.7352 0.4360 2.8435 0.7353 0.7344 0.1784 0.1030
0.0258 25.0 6250 1.5802 0.7362 0.4404 2.8399 0.7362 0.7362 0.1847 0.1013
0.0193 26.0 6500 1.5869 0.737 0.4378 2.8237 0.737 0.7362 0.1846 0.1022
0.0193 27.0 6750 1.6160 0.7365 0.4373 2.7928 0.7365 0.7360 0.1864 0.1049
0.014 28.0 7000 1.6775 0.7372 0.4426 2.9236 0.7372 0.7373 0.1909 0.1039
0.014 29.0 7250 1.6391 0.736 0.4370 2.8717 0.736 0.7358 0.1905 0.0999
0.0132 30.0 7500 1.6804 0.7355 0.4434 2.8397 0.7355 0.7360 0.1903 0.1067
0.0132 31.0 7750 1.6809 0.738 0.4386 2.8853 0.738 0.7387 0.1920 0.1015
0.0121 32.0 8000 1.6953 0.734 0.4443 2.8451 0.734 0.7342 0.1961 0.1013
0.0121 33.0 8250 1.7184 0.7425 0.4344 2.8180 0.7425 0.7423 0.1910 0.1014
0.0098 34.0 8500 1.7151 0.735 0.4445 2.8532 0.735 0.7337 0.1952 0.1000
0.0098 35.0 8750 1.7781 0.7338 0.4484 2.8133 0.7338 0.7351 0.1999 0.1052
0.0086 36.0 9000 1.7540 0.7372 0.4443 2.8388 0.7372 0.7388 0.1954 0.1039
0.0086 37.0 9250 1.7744 0.738 0.4474 2.8600 0.738 0.7390 0.1953 0.1057
0.0079 38.0 9500 1.7446 0.7368 0.4417 2.8485 0.7367 0.7374 0.1972 0.1016
0.0079 39.0 9750 1.7700 0.739 0.4398 2.8826 0.739 0.7395 0.1970 0.1023
0.0076 40.0 10000 1.7896 0.7368 0.4442 2.8449 0.7367 0.7376 0.1988 0.1033
0.0076 41.0 10250 1.7435 0.7402 0.4387 2.8390 0.7402 0.7405 0.1926 0.1031
0.0074 42.0 10500 1.7837 0.7338 0.4470 2.8191 0.7338 0.7339 0.2018 0.1035
0.0074 43.0 10750 1.8015 0.7392 0.4427 2.8093 0.7392 0.7401 0.1981 0.1017
0.0061 44.0 11000 1.8155 0.739 0.4449 2.8333 0.739 0.7406 0.1983 0.1022
0.0061 45.0 11250 1.7958 0.7392 0.4426 2.8161 0.7392 0.7385 0.1963 0.1039
0.0059 46.0 11500 1.8089 0.7422 0.4411 2.8174 0.7422 0.7422 0.1955 0.1011
0.0059 47.0 11750 1.8125 0.743 0.4386 2.8184 0.743 0.7435 0.1939 0.1012
0.0053 48.0 12000 1.8004 0.7372 0.4432 2.8413 0.7372 0.7371 0.1995 0.1023
0.0053 49.0 12250 1.8075 0.7405 0.4392 2.8569 0.7405 0.7397 0.1962 0.1015
0.0055 50.0 12500 1.7844 0.742 0.4405 2.8680 0.7420 0.7411 0.1946 0.1002

Framework versions

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