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# resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t5.0_a0.9

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: 0.8809
- Accuracy: 0.7
- Brier Loss: 0.4126
- Nll: 2.4279
- F1 Micro: 0.7
- F1 Macro: 0.6279
- Ece: 0.2569
- Aurc: 0.1111

## 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   | 13   | 2.1185          | 0.165    | 0.8967     | 8.5399 | 0.165    | 0.1130   | 0.2151 | 0.8331 |
| No log        | 2.0   | 26   | 2.1127          | 0.13     | 0.8958     | 8.1152 | 0.13     | 0.0842   | 0.1816 | 0.8392 |
| No log        | 3.0   | 39   | 2.0781          | 0.165    | 0.8888     | 6.8828 | 0.165    | 0.0878   | 0.2150 | 0.8082 |
| No log        | 4.0   | 52   | 2.0197          | 0.22     | 0.8762     | 5.7578 | 0.22     | 0.1155   | 0.2521 | 0.7521 |
| No log        | 5.0   | 65   | 1.9499          | 0.205    | 0.8601     | 6.0641 | 0.205    | 0.0951   | 0.2567 | 0.7355 |
| No log        | 6.0   | 78   | 1.9019          | 0.25     | 0.8483     | 5.8930 | 0.25     | 0.1178   | 0.2728 | 0.6862 |
| No log        | 7.0   | 91   | 1.8252          | 0.28     | 0.8301     | 5.8062 | 0.28     | 0.1660   | 0.2890 | 0.6982 |
| No log        | 8.0   | 104  | 1.8194          | 0.28     | 0.8275     | 5.2642 | 0.28     | 0.1625   | 0.2874 | 0.6935 |
| No log        | 9.0   | 117  | 1.7671          | 0.355    | 0.8109     | 5.1326 | 0.3550   | 0.2211   | 0.3018 | 0.5678 |
| No log        | 10.0  | 130  | 1.6582          | 0.355    | 0.7774     | 5.2226 | 0.3550   | 0.2200   | 0.2991 | 0.5305 |
| No log        | 11.0  | 143  | 1.5849          | 0.395    | 0.7422     | 5.0239 | 0.395    | 0.2436   | 0.2979 | 0.3974 |
| No log        | 12.0  | 156  | 1.4908          | 0.46     | 0.7001     | 4.2790 | 0.46     | 0.3169   | 0.3091 | 0.3003 |
| No log        | 13.0  | 169  | 1.6016          | 0.395    | 0.7496     | 4.2149 | 0.395    | 0.2793   | 0.2929 | 0.4640 |
| No log        | 14.0  | 182  | 1.4714          | 0.475    | 0.6971     | 4.0742 | 0.4750   | 0.3299   | 0.3177 | 0.3613 |
| No log        | 15.0  | 195  | 1.5007          | 0.46     | 0.7119     | 3.8252 | 0.46     | 0.3145   | 0.3111 | 0.3954 |
| No log        | 16.0  | 208  | 1.4352          | 0.515    | 0.6776     | 3.4028 | 0.515    | 0.3948   | 0.3376 | 0.2993 |
| No log        | 17.0  | 221  | 1.2890          | 0.575    | 0.6104     | 3.4453 | 0.575    | 0.4478   | 0.2940 | 0.2119 |
| No log        | 18.0  | 234  | 1.2190          | 0.595    | 0.5719     | 3.2413 | 0.595    | 0.4662   | 0.2608 | 0.1981 |
| No log        | 19.0  | 247  | 1.2287          | 0.59     | 0.5764     | 3.2303 | 0.59     | 0.4857   | 0.2811 | 0.2020 |
| No log        | 20.0  | 260  | 1.1726          | 0.64     | 0.5494     | 2.9544 | 0.64     | 0.5307   | 0.2993 | 0.1708 |
| No log        | 21.0  | 273  | 1.1305          | 0.61     | 0.5384     | 2.9557 | 0.61     | 0.5170   | 0.2771 | 0.1949 |
| No log        | 22.0  | 286  | 1.1256          | 0.645    | 0.5295     | 2.7934 | 0.645    | 0.5381   | 0.3181 | 0.1629 |
| No log        | 23.0  | 299  | 1.1209          | 0.645    | 0.5217     | 2.8697 | 0.645    | 0.5432   | 0.3055 | 0.1687 |
| No log        | 24.0  | 312  | 1.2513          | 0.685    | 0.5917     | 2.7262 | 0.685    | 0.5639   | 0.3779 | 0.1833 |
| No log        | 25.0  | 325  | 1.0321          | 0.695    | 0.4819     | 2.7202 | 0.695    | 0.5896   | 0.2810 | 0.1280 |
| No log        | 26.0  | 338  | 1.0405          | 0.645    | 0.4957     | 2.6116 | 0.645    | 0.5661   | 0.2515 | 0.1700 |
| No log        | 27.0  | 351  | 1.0580          | 0.695    | 0.4933     | 2.7436 | 0.695    | 0.5996   | 0.2967 | 0.1339 |
| No log        | 28.0  | 364  | 0.9740          | 0.65     | 0.4575     | 2.5682 | 0.65     | 0.5731   | 0.2513 | 0.1384 |
| No log        | 29.0  | 377  | 0.9934          | 0.695    | 0.4651     | 2.5753 | 0.695    | 0.6108   | 0.2775 | 0.1171 |
| No log        | 30.0  | 390  | 0.9900          | 0.645    | 0.4695     | 2.6280 | 0.645    | 0.5668   | 0.2459 | 0.1558 |
| No log        | 31.0  | 403  | 0.9671          | 0.695    | 0.4504     | 2.8174 | 0.695    | 0.6094   | 0.2505 | 0.1188 |
| No log        | 32.0  | 416  | 0.9327          | 0.715    | 0.4324     | 2.5285 | 0.715    | 0.6415   | 0.2565 | 0.1086 |
| No log        | 33.0  | 429  | 0.9628          | 0.71     | 0.4464     | 2.5876 | 0.7100   | 0.6435   | 0.2709 | 0.1152 |
| No log        | 34.0  | 442  | 0.9316          | 0.715    | 0.4353     | 2.7111 | 0.715    | 0.6334   | 0.2361 | 0.1078 |
| No log        | 35.0  | 455  | 0.9275          | 0.7      | 0.4364     | 2.5226 | 0.7      | 0.6251   | 0.2586 | 0.1207 |
| No log        | 36.0  | 468  | 0.9301          | 0.7      | 0.4346     | 2.6464 | 0.7      | 0.6232   | 0.2482 | 0.1142 |
| No log        | 37.0  | 481  | 0.9013          | 0.695    | 0.4194     | 2.5575 | 0.695    | 0.6197   | 0.2554 | 0.1098 |
| No log        | 38.0  | 494  | 0.9008          | 0.695    | 0.4196     | 2.6270 | 0.695    | 0.6156   | 0.2246 | 0.1063 |
| 1.0903        | 39.0  | 507  | 0.9185          | 0.71     | 0.4311     | 2.6290 | 0.7100   | 0.6362   | 0.2626 | 0.1165 |
| 1.0903        | 40.0  | 520  | 0.9053          | 0.685    | 0.4254     | 2.5057 | 0.685    | 0.6239   | 0.2210 | 0.1171 |
| 1.0903        | 41.0  | 533  | 0.8955          | 0.7      | 0.4189     | 2.4823 | 0.7      | 0.6291   | 0.1995 | 0.1103 |
| 1.0903        | 42.0  | 546  | 0.9012          | 0.69     | 0.4223     | 2.5377 | 0.69     | 0.6195   | 0.2486 | 0.1119 |
| 1.0903        | 43.0  | 559  | 0.8894          | 0.71     | 0.4138     | 2.6167 | 0.7100   | 0.6382   | 0.2459 | 0.1022 |
| 1.0903        | 44.0  | 572  | 0.8846          | 0.695    | 0.4132     | 2.5130 | 0.695    | 0.6265   | 0.2198 | 0.1093 |
| 1.0903        | 45.0  | 585  | 0.8946          | 0.69     | 0.4190     | 2.6357 | 0.69     | 0.6230   | 0.2375 | 0.1145 |
| 1.0903        | 46.0  | 598  | 0.8931          | 0.705    | 0.4168     | 2.6306 | 0.705    | 0.6342   | 0.2555 | 0.1102 |
| 1.0903        | 47.0  | 611  | 0.8842          | 0.71     | 0.4160     | 2.3021 | 0.7100   | 0.6347   | 0.2096 | 0.1120 |
| 1.0903        | 48.0  | 624  | 0.8805          | 0.695    | 0.4140     | 2.3447 | 0.695    | 0.6237   | 0.2181 | 0.1128 |
| 1.0903        | 49.0  | 637  | 0.8816          | 0.7      | 0.4142     | 2.4358 | 0.7      | 0.6295   | 0.2550 | 0.1112 |
| 1.0903        | 50.0  | 650  | 0.8809          | 0.7      | 0.4126     | 2.4279 | 0.7      | 0.6279   | 0.2569 | 0.1111 |


### 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-base_tobacco-cnn_tobacco3482_kd_CEKD_t5.0_a0.9", "results": []}]}
transformers pytorch resnet image-classification generated_from_trainer base_model:microsoft/resnet-50 license:apache-2.0 autotrain_compatible endpoints_compatible region:us