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# resnet101-base_tobacco-cnn_tobacco3482_kd_NKD_t1.0_g1.5

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: 3.2266
- Accuracy: 0.385
- Brier Loss: 0.7374
- Nll: 4.0859
- F1 Micro: 0.3850
- F1 Macro: 0.2652
- Ece: 0.2858
- Aurc: 0.4261

## 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    | 3.9610          | 0.05     | 0.9004     | 9.1922 | 0.0500   | 0.0100   | 0.1457 | 0.9423 |
| No log        | 2.0   | 8    | 3.8752          | 0.155    | 0.8934     | 8.5838 | 0.155    | 0.0268   | 0.2356 | 0.9630 |
| No log        | 3.0   | 12   | 3.8665          | 0.155    | 0.9261     | 8.8645 | 0.155    | 0.0268   | 0.3113 | 0.7263 |
| No log        | 4.0   | 16   | 5.0552          | 0.155    | 1.3190     | 8.8736 | 0.155    | 0.0268   | 0.6667 | 0.6226 |
| No log        | 5.0   | 20   | 4.9755          | 0.155    | 1.2873     | 8.9603 | 0.155    | 0.0270   | 0.6315 | 0.6033 |
| No log        | 6.0   | 24   | 4.6069          | 0.155    | 1.1443     | 7.0637 | 0.155    | 0.0301   | 0.5057 | 0.6065 |
| No log        | 7.0   | 28   | 3.7058          | 0.22     | 0.9193     | 6.6528 | 0.22     | 0.0685   | 0.3454 | 0.5737 |
| No log        | 8.0   | 32   | 3.3000          | 0.25     | 0.8140     | 6.8642 | 0.25     | 0.1011   | 0.2638 | 0.5377 |
| No log        | 9.0   | 36   | 3.3805          | 0.195    | 0.8768     | 6.5108 | 0.195    | 0.0779   | 0.2955 | 0.7532 |
| No log        | 10.0  | 40   | 3.4626          | 0.2      | 0.8985     | 6.3933 | 0.2000   | 0.0745   | 0.3154 | 0.7384 |
| No log        | 11.0  | 44   | 3.2088          | 0.32     | 0.7621     | 6.0433 | 0.32     | 0.1695   | 0.2375 | 0.4457 |
| No log        | 12.0  | 48   | 3.4543          | 0.22     | 0.8720     | 6.1413 | 0.22     | 0.1065   | 0.3144 | 0.7026 |
| No log        | 13.0  | 52   | 3.5300          | 0.225    | 0.8684     | 7.0938 | 0.225    | 0.1182   | 0.2747 | 0.7110 |
| No log        | 14.0  | 56   | 3.5981          | 0.215    | 0.8821     | 7.5146 | 0.2150   | 0.0978   | 0.3047 | 0.7351 |
| No log        | 15.0  | 60   | 3.5641          | 0.23     | 0.8895     | 7.7554 | 0.23     | 0.0944   | 0.2985 | 0.7568 |
| No log        | 16.0  | 64   | 3.5853          | 0.235    | 0.8698     | 6.6949 | 0.235    | 0.1292   | 0.2634 | 0.6518 |
| No log        | 17.0  | 68   | 3.5539          | 0.255    | 0.8597     | 7.5062 | 0.255    | 0.1331   | 0.2821 | 0.6332 |
| No log        | 18.0  | 72   | 3.5725          | 0.265    | 0.8569     | 7.4117 | 0.265    | 0.1396   | 0.2708 | 0.5940 |
| No log        | 19.0  | 76   | 3.5207          | 0.27     | 0.8415     | 6.5482 | 0.27     | 0.1542   | 0.2592 | 0.5619 |
| No log        | 20.0  | 80   | 3.5360          | 0.26     | 0.8573     | 7.4207 | 0.26     | 0.1358   | 0.2942 | 0.5949 |
| No log        | 21.0  | 84   | 3.2807          | 0.345    | 0.7933     | 4.8232 | 0.345    | 0.2077   | 0.2903 | 0.5385 |
| No log        | 22.0  | 88   | 3.1633          | 0.39     | 0.7217     | 4.3843 | 0.39     | 0.2417   | 0.2547 | 0.3857 |
| No log        | 23.0  | 92   | 3.2159          | 0.39     | 0.7463     | 4.4691 | 0.39     | 0.2481   | 0.2923 | 0.3756 |
| No log        | 24.0  | 96   | 3.1650          | 0.375    | 0.7248     | 4.4043 | 0.375    | 0.2276   | 0.2433 | 0.3809 |
| No log        | 25.0  | 100  | 3.2000          | 0.375    | 0.7470     | 4.7004 | 0.375    | 0.2473   | 0.2671 | 0.4264 |
| No log        | 26.0  | 104  | 3.4356          | 0.27     | 0.8326     | 6.6479 | 0.27     | 0.1466   | 0.2636 | 0.5640 |
| No log        | 27.0  | 108  | 3.5761          | 0.285    | 0.8347     | 6.5689 | 0.285    | 0.1796   | 0.2537 | 0.6182 |
| No log        | 28.0  | 112  | 3.5778          | 0.26     | 0.8546     | 7.0753 | 0.26     | 0.1380   | 0.2629 | 0.5870 |
| No log        | 29.0  | 116  | 3.1280          | 0.39     | 0.7075     | 4.5179 | 0.39     | 0.2450   | 0.2479 | 0.3759 |
| No log        | 30.0  | 120  | 3.1559          | 0.37     | 0.7268     | 4.3444 | 0.37     | 0.2413   | 0.2588 | 0.3941 |
| No log        | 31.0  | 124  | 3.1493          | 0.39     | 0.7133     | 4.6188 | 0.39     | 0.2305   | 0.2338 | 0.3686 |
| No log        | 32.0  | 128  | 3.1287          | 0.39     | 0.7015     | 4.0848 | 0.39     | 0.2379   | 0.2271 | 0.3655 |
| No log        | 33.0  | 132  | 3.1409          | 0.395    | 0.7048     | 4.0026 | 0.395    | 0.2290   | 0.2210 | 0.3606 |
| No log        | 34.0  | 136  | 3.1691          | 0.375    | 0.7210     | 4.4086 | 0.375    | 0.2215   | 0.2495 | 0.3800 |
| No log        | 35.0  | 140  | 3.1529          | 0.4      | 0.7117     | 4.1376 | 0.4000   | 0.2487   | 0.2200 | 0.3605 |
| No log        | 36.0  | 144  | 3.1088          | 0.4      | 0.6989     | 4.0773 | 0.4000   | 0.2645   | 0.2478 | 0.3641 |
| No log        | 37.0  | 148  | 3.2158          | 0.4      | 0.7230     | 4.1145 | 0.4000   | 0.2603   | 0.2517 | 0.3761 |
| No log        | 38.0  | 152  | 3.1351          | 0.39     | 0.7064     | 4.3952 | 0.39     | 0.2398   | 0.2475 | 0.3606 |
| No log        | 39.0  | 156  | 3.1239          | 0.395    | 0.7001     | 4.0496 | 0.395    | 0.2569   | 0.2364 | 0.3583 |
| No log        | 40.0  | 160  | 3.1855          | 0.385    | 0.7169     | 4.0634 | 0.3850   | 0.2274   | 0.2467 | 0.3687 |
| No log        | 41.0  | 164  | 3.1938          | 0.37     | 0.7098     | 3.9505 | 0.37     | 0.2146   | 0.2207 | 0.3781 |
| No log        | 42.0  | 168  | 3.3495          | 0.395    | 0.7438     | 4.0247 | 0.395    | 0.2428   | 0.2901 | 0.3973 |
| No log        | 43.0  | 172  | 3.2352          | 0.395    | 0.7115     | 3.9875 | 0.395    | 0.2431   | 0.2651 | 0.3790 |
| No log        | 44.0  | 176  | 3.2838          | 0.39     | 0.7223     | 3.8867 | 0.39     | 0.2246   | 0.2590 | 0.3824 |
| No log        | 45.0  | 180  | 3.3175          | 0.395    | 0.7304     | 4.2165 | 0.395    | 0.2286   | 0.2549 | 0.3811 |
| No log        | 46.0  | 184  | 3.1183          | 0.395    | 0.6916     | 3.9786 | 0.395    | 0.2338   | 0.2345 | 0.3581 |
| No log        | 47.0  | 188  | 3.1608          | 0.395    | 0.7049     | 3.7245 | 0.395    | 0.2580   | 0.2429 | 0.3668 |
| No log        | 48.0  | 192  | 3.2144          | 0.38     | 0.7316     | 3.9593 | 0.38     | 0.2512   | 0.2517 | 0.4202 |
| No log        | 49.0  | 196  | 3.2781          | 0.365    | 0.7561     | 3.9721 | 0.3650   | 0.2440   | 0.2429 | 0.4654 |
| No log        | 50.0  | 200  | 3.2266          | 0.385    | 0.7374     | 4.0859 | 0.3850   | 0.2652   | 0.2858 | 0.4261 |


### 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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