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# 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

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "microsoft/resnet-50", "model-index": [{"name": "resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_kd_CEKD_t1.0_a1.0", "results": []}]}
transformers pytorch resnet image-classification generated_from_trainer base_model:microsoft/resnet-50 license:apache-2.0 autotrain_compatible endpoints_compatible region:us