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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_kd_CEKD_t1.0_a1.0
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_kd_CEKD_t1.0_a1.0
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: 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
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