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# resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t1.0_a0.5

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: 0.8577
- Accuracy: 0.53
- Brier Loss: 0.6406
- Nll: 2.1208
- F1 Micro: 0.53
- F1 Macro: 0.4957
- Ece: 0.3004
- Aurc: 0.3168

## 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    | 1.4267          | 0.05     | 0.9008     | 9.6592  | 0.0500   | 0.0177   | 0.1432 | 0.9439 |
| No log        | 2.0   | 8    | 1.4006          | 0.155    | 0.8969     | 7.9140  | 0.155    | 0.0268   | 0.2365 | 0.9603 |
| No log        | 3.0   | 12   | 1.4621          | 0.155    | 0.9457     | 13.3695 | 0.155    | 0.0268   | 0.3013 | 0.9107 |
| No log        | 4.0   | 16   | 2.1836          | 0.155    | 1.3252     | 12.8977 | 0.155    | 0.0268   | 0.6400 | 0.7514 |
| No log        | 5.0   | 20   | 2.4365          | 0.155    | 1.3998     | 8.4435  | 0.155    | 0.0268   | 0.7030 | 0.6102 |
| No log        | 6.0   | 24   | 2.1554          | 0.155    | 1.2534     | 6.9190  | 0.155    | 0.0279   | 0.5987 | 0.6271 |
| No log        | 7.0   | 28   | 1.5617          | 0.175    | 0.9637     | 5.7454  | 0.175    | 0.0462   | 0.3802 | 0.6485 |
| No log        | 8.0   | 32   | 1.3267          | 0.245    | 0.8707     | 5.2368  | 0.245    | 0.0835   | 0.2961 | 0.5438 |
| No log        | 9.0   | 36   | 1.2434          | 0.19     | 0.8886     | 5.0360  | 0.19     | 0.0471   | 0.3198 | 0.7720 |
| No log        | 10.0  | 40   | 1.0721          | 0.305    | 0.8123     | 4.5157  | 0.305    | 0.1762   | 0.2684 | 0.5269 |
| No log        | 11.0  | 44   | 1.1256          | 0.22     | 0.8429     | 3.9215  | 0.22     | 0.1083   | 0.2812 | 0.7346 |
| No log        | 12.0  | 48   | 0.9865          | 0.35     | 0.7676     | 3.4553  | 0.35     | 0.2565   | 0.2884 | 0.4790 |
| No log        | 13.0  | 52   | 1.0206          | 0.355    | 0.7899     | 3.3582  | 0.3550   | 0.2278   | 0.2954 | 0.5883 |
| No log        | 14.0  | 56   | 0.9096          | 0.415    | 0.6994     | 3.2174  | 0.415    | 0.3147   | 0.2563 | 0.3596 |
| No log        | 15.0  | 60   | 0.9187          | 0.415    | 0.7129     | 3.2059  | 0.415    | 0.2742   | 0.2941 | 0.3971 |
| No log        | 16.0  | 64   | 0.8905          | 0.395    | 0.6956     | 2.9931  | 0.395    | 0.2618   | 0.2590 | 0.3826 |
| No log        | 17.0  | 68   | 0.9108          | 0.425    | 0.7073     | 3.1634  | 0.425    | 0.2855   | 0.2995 | 0.3685 |
| No log        | 18.0  | 72   | 0.8769          | 0.465    | 0.6706     | 3.1088  | 0.465    | 0.3652   | 0.2855 | 0.3261 |
| No log        | 19.0  | 76   | 0.8585          | 0.475    | 0.6687     | 2.8710  | 0.4750   | 0.3884   | 0.2916 | 0.3282 |
| No log        | 20.0  | 80   | 0.9822          | 0.405    | 0.7378     | 2.8889  | 0.405    | 0.3570   | 0.2850 | 0.4895 |
| No log        | 21.0  | 84   | 0.9324          | 0.445    | 0.6992     | 2.7975  | 0.445    | 0.3553   | 0.3021 | 0.3762 |
| No log        | 22.0  | 88   | 1.0330          | 0.42     | 0.7350     | 2.7487  | 0.4200   | 0.3506   | 0.2984 | 0.4771 |
| No log        | 23.0  | 92   | 0.8755          | 0.455    | 0.6674     | 2.5903  | 0.455    | 0.3415   | 0.2570 | 0.3352 |
| No log        | 24.0  | 96   | 0.8651          | 0.47     | 0.6443     | 2.8456  | 0.47     | 0.3800   | 0.2451 | 0.2975 |
| No log        | 25.0  | 100  | 0.9567          | 0.445    | 0.7150     | 2.7083  | 0.445    | 0.3727   | 0.2667 | 0.4676 |
| No log        | 26.0  | 104  | 1.0224          | 0.42     | 0.7376     | 2.4408  | 0.4200   | 0.3367   | 0.2968 | 0.5019 |
| No log        | 27.0  | 108  | 0.8365          | 0.525    | 0.6407     | 2.6426  | 0.525    | 0.4496   | 0.2960 | 0.2657 |
| No log        | 28.0  | 112  | 0.9798          | 0.425    | 0.7287     | 2.6379  | 0.425    | 0.3489   | 0.2640 | 0.4668 |
| No log        | 29.0  | 116  | 0.9226          | 0.44     | 0.6965     | 2.5748  | 0.44     | 0.3669   | 0.2561 | 0.4054 |
| No log        | 30.0  | 120  | 0.8303          | 0.49     | 0.6398     | 2.4839  | 0.49     | 0.3924   | 0.2981 | 0.2936 |
| No log        | 31.0  | 124  | 0.8426          | 0.52     | 0.6478     | 2.5282  | 0.52     | 0.4322   | 0.3109 | 0.3084 |
| No log        | 32.0  | 128  | 0.9111          | 0.45     | 0.6970     | 2.3870  | 0.45     | 0.3947   | 0.2837 | 0.4448 |
| No log        | 33.0  | 132  | 0.8723          | 0.51     | 0.6524     | 2.6124  | 0.51     | 0.4170   | 0.2536 | 0.3365 |
| No log        | 34.0  | 136  | 0.8936          | 0.47     | 0.6671     | 2.8892  | 0.47     | 0.3814   | 0.2436 | 0.3357 |
| No log        | 35.0  | 140  | 1.2870          | 0.42     | 0.7660     | 4.4020  | 0.4200   | 0.3468   | 0.2860 | 0.4606 |
| No log        | 36.0  | 144  | 0.9991          | 0.455    | 0.7289     | 2.6973  | 0.455    | 0.4132   | 0.3272 | 0.4684 |
| No log        | 37.0  | 148  | 1.6352          | 0.365    | 0.8356     | 4.7695  | 0.3650   | 0.3020   | 0.3312 | 0.6069 |
| No log        | 38.0  | 152  | 1.3014          | 0.39     | 0.8213     | 2.9436  | 0.39     | 0.3382   | 0.3262 | 0.5476 |
| No log        | 39.0  | 156  | 1.0294          | 0.415    | 0.7361     | 2.7188  | 0.415    | 0.3446   | 0.2454 | 0.4632 |
| No log        | 40.0  | 160  | 0.8825          | 0.52     | 0.6538     | 2.3887  | 0.52     | 0.4608   | 0.2721 | 0.3186 |
| No log        | 41.0  | 164  | 0.8572          | 0.54     | 0.6288     | 2.4201  | 0.54     | 0.4822   | 0.2963 | 0.2899 |
| No log        | 42.0  | 168  | 0.8393          | 0.535    | 0.6291     | 2.3587  | 0.535    | 0.4726   | 0.2824 | 0.2937 |
| No log        | 43.0  | 172  | 0.8369          | 0.515    | 0.6303     | 2.4060  | 0.515    | 0.4583   | 0.2689 | 0.2903 |
| No log        | 44.0  | 176  | 0.8458          | 0.49     | 0.6346     | 2.3323  | 0.49     | 0.4428   | 0.2526 | 0.2951 |
| No log        | 45.0  | 180  | 0.8446          | 0.49     | 0.6367     | 2.2207  | 0.49     | 0.4289   | 0.2655 | 0.3041 |
| No log        | 46.0  | 184  | 0.8324          | 0.54     | 0.6289     | 2.3685  | 0.54     | 0.4779   | 0.2571 | 0.2873 |
| No log        | 47.0  | 188  | 0.8658          | 0.515    | 0.6486     | 2.3922  | 0.515    | 0.4584   | 0.2623 | 0.3100 |
| No log        | 48.0  | 192  | 0.8516          | 0.525    | 0.6410     | 2.4448  | 0.525    | 0.4700   | 0.3006 | 0.3044 |
| No log        | 49.0  | 196  | 0.8520          | 0.55     | 0.6350     | 2.2049  | 0.55     | 0.4947   | 0.3030 | 0.2980 |
| No log        | 50.0  | 200  | 0.8577          | 0.53     | 0.6406     | 2.1208  | 0.53     | 0.4957   | 0.3004 | 0.3168 |


### 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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