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# resnet101-base_tobacco-cnn_tobacco3482_kd_MSE

This model is a fine-tuned version of [bdpc/resnet101-base_tobacco](https://huggingface.co/bdpc/resnet101-base_tobacco) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1315
- Accuracy: 0.365
- Brier Loss: 0.7313
- Nll: 5.5846
- F1 Micro: 0.3650
- F1 Macro: 0.2369
- Ece: 0.2526
- Aurc: 0.4412

## 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: 256
- eval_batch_size: 256
- 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   | 4    | 1.1507          | 0.1      | 0.8998     | 9.9508 | 0.1000   | 0.0462   | 0.1728 | 0.9208 |
| No log        | 2.0   | 8    | 0.9900          | 0.155    | 0.8924     | 9.6289 | 0.155    | 0.0268   | 0.2400 | 0.9571 |
| No log        | 3.0   | 12   | 0.8441          | 0.155    | 0.9273     | 8.9944 | 0.155    | 0.0268   | 0.3276 | 0.9345 |
| No log        | 4.0   | 16   | 1.4048          | 0.155    | 1.3149     | 8.9869 | 0.155    | 0.0268   | 0.6569 | 0.6091 |
| No log        | 5.0   | 20   | 1.0761          | 0.155    | 1.1553     | 8.9185 | 0.155    | 0.0272   | 0.5441 | 0.6112 |
| No log        | 6.0   | 24   | 1.1745          | 0.155    | 1.1386     | 9.2644 | 0.155    | 0.0304   | 0.4982 | 0.6120 |
| No log        | 7.0   | 28   | 0.4686          | 0.225    | 0.8829     | 7.3879 | 0.225    | 0.0724   | 0.3173 | 0.5804 |
| No log        | 8.0   | 32   | 0.3535          | 0.24     | 0.8393     | 7.0880 | 0.24     | 0.0797   | 0.2963 | 0.5518 |
| No log        | 9.0   | 36   | 0.2519          | 0.295    | 0.8157     | 6.6738 | 0.295    | 0.1375   | 0.2944 | 0.4810 |
| No log        | 10.0  | 40   | 0.2957          | 0.265    | 0.8432     | 6.8903 | 0.265    | 0.1030   | 0.3171 | 0.5807 |
| No log        | 11.0  | 44   | 0.5224          | 0.21     | 0.8832     | 8.6128 | 0.2100   | 0.0987   | 0.2948 | 0.6814 |
| No log        | 12.0  | 48   | 0.4088          | 0.18     | 0.8807     | 7.0533 | 0.18     | 0.0309   | 0.2966 | 0.7466 |
| No log        | 13.0  | 52   | 0.5082          | 0.225    | 0.8732     | 8.3126 | 0.225    | 0.0606   | 0.2761 | 0.7285 |
| No log        | 14.0  | 56   | 0.5253          | 0.18     | 0.8905     | 8.3229 | 0.18     | 0.0305   | 0.2973 | 0.7838 |
| No log        | 15.0  | 60   | 0.5612          | 0.225    | 0.8579     | 7.9410 | 0.225    | 0.0642   | 0.2690 | 0.7108 |
| No log        | 16.0  | 64   | 0.2805          | 0.28     | 0.8094     | 6.0275 | 0.28     | 0.1475   | 0.2633 | 0.5701 |
| No log        | 17.0  | 68   | 0.3076          | 0.32     | 0.8151     | 6.1462 | 0.32     | 0.1641   | 0.2852 | 0.6162 |
| No log        | 18.0  | 72   | 0.3824          | 0.29     | 0.8072     | 6.0214 | 0.29     | 0.1681   | 0.2900 | 0.6048 |
| No log        | 19.0  | 76   | 0.5089          | 0.19     | 0.8701     | 8.9391 | 0.19     | 0.0418   | 0.2582 | 0.7152 |
| No log        | 20.0  | 80   | 0.1490          | 0.335    | 0.7347     | 5.7349 | 0.335    | 0.1786   | 0.2500 | 0.4430 |
| No log        | 21.0  | 84   | 0.3448          | 0.255    | 0.8455     | 6.6598 | 0.255    | 0.0998   | 0.3124 | 0.7183 |
| No log        | 22.0  | 88   | 0.6254          | 0.22     | 0.8413     | 6.9926 | 0.22     | 0.0966   | 0.2654 | 0.7197 |
| No log        | 23.0  | 92   | 0.5464          | 0.215    | 0.8909     | 8.4952 | 0.2150   | 0.0570   | 0.2931 | 0.7084 |
| No log        | 24.0  | 96   | 0.4465          | 0.24     | 0.8445     | 7.2319 | 0.24     | 0.1396   | 0.2575 | 0.6667 |
| No log        | 25.0  | 100  | 0.3967          | 0.215    | 0.8547     | 6.7234 | 0.2150   | 0.0962   | 0.2913 | 0.7053 |
| No log        | 26.0  | 104  | 0.2459          | 0.295    | 0.8041     | 5.1627 | 0.295    | 0.1901   | 0.2525 | 0.6590 |
| No log        | 27.0  | 108  | 0.4125          | 0.19     | 0.8595     | 7.1181 | 0.19     | 0.0551   | 0.2707 | 0.7087 |
| No log        | 28.0  | 112  | 0.1686          | 0.36     | 0.7309     | 5.1322 | 0.36     | 0.2178   | 0.2296 | 0.4432 |
| No log        | 29.0  | 116  | 0.3573          | 0.205    | 0.8664     | 6.6815 | 0.205    | 0.0523   | 0.2753 | 0.7131 |
| No log        | 30.0  | 120  | 0.1634          | 0.32     | 0.7416     | 5.6798 | 0.32     | 0.1862   | 0.2473 | 0.4616 |
| No log        | 31.0  | 124  | 0.1404          | 0.35     | 0.7295     | 5.6538 | 0.35     | 0.2152   | 0.2688 | 0.4389 |
| No log        | 32.0  | 128  | 0.1435          | 0.325    | 0.7415     | 5.5376 | 0.325    | 0.1439   | 0.2567 | 0.4489 |
| No log        | 33.0  | 132  | 0.1428          | 0.33     | 0.7292     | 5.5151 | 0.33     | 0.1791   | 0.2502 | 0.4403 |
| No log        | 34.0  | 136  | 0.1602          | 0.33     | 0.7371     | 5.8829 | 0.33     | 0.1941   | 0.2542 | 0.4481 |
| No log        | 35.0  | 140  | 0.1663          | 0.325    | 0.7398     | 5.6501 | 0.325    | 0.1880   | 0.2443 | 0.4564 |
| No log        | 36.0  | 144  | 0.1637          | 0.35     | 0.7422     | 5.9440 | 0.35     | 0.2053   | 0.2748 | 0.4361 |
| No log        | 37.0  | 148  | 0.1520          | 0.325    | 0.7317     | 5.3284 | 0.325    | 0.1787   | 0.2677 | 0.4531 |
| No log        | 38.0  | 152  | 0.1585          | 0.335    | 0.7385     | 5.9712 | 0.335    | 0.1939   | 0.2648 | 0.4483 |
| No log        | 39.0  | 156  | 0.1491          | 0.335    | 0.7334     | 5.6729 | 0.335    | 0.1912   | 0.2533 | 0.4404 |
| No log        | 40.0  | 160  | 0.1367          | 0.32     | 0.7297     | 5.7350 | 0.32     | 0.1818   | 0.2512 | 0.4498 |
| No log        | 41.0  | 164  | 0.2089          | 0.335    | 0.7583     | 5.2150 | 0.335    | 0.2073   | 0.2822 | 0.4712 |
| No log        | 42.0  | 168  | 0.1612          | 0.335    | 0.7323     | 4.9145 | 0.335    | 0.2058   | 0.2696 | 0.4482 |
| No log        | 43.0  | 172  | 0.1616          | 0.335    | 0.7349     | 5.4305 | 0.335    | 0.1916   | 0.2650 | 0.4493 |
| No log        | 44.0  | 176  | 0.1477          | 0.335    | 0.7335     | 5.3482 | 0.335    | 0.1761   | 0.2478 | 0.4410 |
| No log        | 45.0  | 180  | 0.1426          | 0.34     | 0.7321     | 5.4265 | 0.34     | 0.2018   | 0.2307 | 0.4483 |
| No log        | 46.0  | 184  | 0.1531          | 0.345    | 0.7351     | 5.2269 | 0.345    | 0.2108   | 0.2812 | 0.4572 |
| No log        | 47.0  | 188  | 0.1426          | 0.34     | 0.7299     | 5.1412 | 0.34     | 0.2040   | 0.2418 | 0.4443 |
| No log        | 48.0  | 192  | 0.1321          | 0.335    | 0.7353     | 5.2955 | 0.335    | 0.2017   | 0.2515 | 0.4547 |
| No log        | 49.0  | 196  | 0.1330          | 0.34     | 0.7332     | 5.5391 | 0.34     | 0.2065   | 0.2485 | 0.4524 |
| No log        | 50.0  | 200  | 0.1315          | 0.365    | 0.7313     | 5.5846 | 0.3650   | 0.2369   | 0.2526 | 0.4412 |


### Framework versions

- Transformers 4.36.0.dev0
- Pytorch 2.2.0.dev20231112+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1

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