
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t2.5_a0.7

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: 0.8012
- Accuracy: 0.7
- Brier Loss: 0.4467
- Nll: 2.5682
- F1 Micro: 0.7
- F1 Macro: 0.6313
- Ece: 0.2684
- Aurc: 0.1170

## 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   | 1.8024          | 0.16     | 0.8966     | 8.5001 | 0.16     | 0.1073   | 0.2079 | 0.8334 |
| No log        | 2.0   | 26   | 1.7941          | 0.145    | 0.8957     | 8.3207 | 0.145    | 0.0843   | 0.2022 | 0.8435 |
| No log        | 3.0   | 39   | 1.7486          | 0.2      | 0.8868     | 6.2015 | 0.2000   | 0.1007   | 0.2209 | 0.7900 |
| No log        | 4.0   | 52   | 1.6854          | 0.205    | 0.8738     | 6.0142 | 0.205    | 0.0707   | 0.2453 | 0.7584 |
| No log        | 5.0   | 65   | 1.6162          | 0.2      | 0.8594     | 6.2364 | 0.2000   | 0.0552   | 0.2466 | 0.7717 |
| No log        | 6.0   | 78   | 1.5412          | 0.235    | 0.8416     | 6.0423 | 0.235    | 0.0902   | 0.2589 | 0.7006 |
| No log        | 7.0   | 91   | 1.5011          | 0.295    | 0.8304     | 6.1420 | 0.295    | 0.1272   | 0.2803 | 0.6124 |
| No log        | 8.0   | 104  | 1.4415          | 0.3      | 0.8114     | 6.0440 | 0.3      | 0.1296   | 0.2870 | 0.5641 |
| No log        | 9.0   | 117  | 1.3257          | 0.38     | 0.7625     | 5.6923 | 0.38     | 0.2198   | 0.3136 | 0.3675 |
| No log        | 10.0  | 130  | 1.3748          | 0.33     | 0.7905     | 5.5276 | 0.33     | 0.1870   | 0.2947 | 0.5985 |
| No log        | 11.0  | 143  | 1.3294          | 0.39     | 0.7683     | 4.9632 | 0.39     | 0.2573   | 0.2940 | 0.4639 |
| No log        | 12.0  | 156  | 1.2444          | 0.385    | 0.7297     | 4.8431 | 0.3850   | 0.2330   | 0.2849 | 0.4173 |
| No log        | 13.0  | 169  | 1.2212          | 0.45     | 0.7153     | 4.5819 | 0.45     | 0.3051   | 0.3143 | 0.3379 |
| No log        | 14.0  | 182  | 1.1835          | 0.495    | 0.6888     | 3.6108 | 0.495    | 0.3412   | 0.3316 | 0.2873 |
| No log        | 15.0  | 195  | 1.1203          | 0.47     | 0.6559     | 3.6500 | 0.47     | 0.3348   | 0.2935 | 0.3061 |
| No log        | 16.0  | 208  | 1.1520          | 0.495    | 0.6707     | 3.8106 | 0.495    | 0.3632   | 0.2938 | 0.3604 |
| No log        | 17.0  | 221  | 1.0261          | 0.565    | 0.6021     | 3.3382 | 0.565    | 0.4214   | 0.2840 | 0.2047 |
| No log        | 18.0  | 234  | 1.0080          | 0.61     | 0.5914     | 3.2936 | 0.61     | 0.4748   | 0.3240 | 0.1806 |
| No log        | 19.0  | 247  | 1.0696          | 0.58     | 0.6253     | 3.2354 | 0.58     | 0.4686   | 0.3152 | 0.2626 |
| No log        | 20.0  | 260  | 0.9733          | 0.615    | 0.5722     | 3.1019 | 0.615    | 0.4968   | 0.3259 | 0.2066 |
| No log        | 21.0  | 273  | 0.9266          | 0.625    | 0.5423     | 3.0239 | 0.625    | 0.5202   | 0.2834 | 0.1782 |
| No log        | 22.0  | 286  | 0.9364          | 0.66     | 0.5461     | 2.9031 | 0.66     | 0.5461   | 0.3128 | 0.1601 |
| No log        | 23.0  | 299  | 0.9181          | 0.675    | 0.5307     | 2.8416 | 0.675    | 0.5584   | 0.3106 | 0.1462 |
| No log        | 24.0  | 312  | 0.9739          | 0.665    | 0.5539     | 2.8798 | 0.665    | 0.5634   | 0.3325 | 0.1610 |
| No log        | 25.0  | 325  | 0.8851          | 0.69     | 0.5099     | 2.7336 | 0.69     | 0.6013   | 0.3064 | 0.1437 |
| No log        | 26.0  | 338  | 0.8755          | 0.71     | 0.4979     | 2.7400 | 0.7100   | 0.6032   | 0.3162 | 0.1211 |
| No log        | 27.0  | 351  | 0.8653          | 0.675    | 0.4964     | 2.8339 | 0.675    | 0.5705   | 0.2977 | 0.1386 |
| No log        | 28.0  | 364  | 0.8838          | 0.675    | 0.5055     | 2.7456 | 0.675    | 0.5816   | 0.2969 | 0.1524 |
| No log        | 29.0  | 377  | 0.8805          | 0.68     | 0.5025     | 2.6942 | 0.68     | 0.5855   | 0.3099 | 0.1380 |
| No log        | 30.0  | 390  | 0.8585          | 0.665    | 0.4891     | 2.7511 | 0.665    | 0.5737   | 0.2627 | 0.1370 |
| No log        | 31.0  | 403  | 0.8410          | 0.675    | 0.4736     | 2.6431 | 0.675    | 0.5985   | 0.2670 | 0.1335 |
| No log        | 32.0  | 416  | 0.8378          | 0.71     | 0.4724     | 2.7320 | 0.7100   | 0.6236   | 0.2885 | 0.1153 |
| No log        | 33.0  | 429  | 0.8421          | 0.705    | 0.4718     | 2.6331 | 0.705    | 0.6326   | 0.2644 | 0.1147 |
| No log        | 34.0  | 442  | 0.8350          | 0.685    | 0.4697     | 2.8035 | 0.685    | 0.6062   | 0.2831 | 0.1291 |
| No log        | 35.0  | 455  | 0.8377          | 0.7      | 0.4708     | 2.4611 | 0.7      | 0.6376   | 0.3173 | 0.1195 |
| No log        | 36.0  | 468  | 0.8126          | 0.69     | 0.4562     | 2.3909 | 0.69     | 0.6154   | 0.2433 | 0.1177 |
| No log        | 37.0  | 481  | 0.8299          | 0.685    | 0.4673     | 2.5695 | 0.685    | 0.6080   | 0.2802 | 0.1261 |
| No log        | 38.0  | 494  | 0.8197          | 0.685    | 0.4597     | 2.6388 | 0.685    | 0.6187   | 0.2690 | 0.1229 |
| 0.9314        | 39.0  | 507  | 0.8137          | 0.695    | 0.4547     | 2.7263 | 0.695    | 0.6332   | 0.2581 | 0.1207 |
| 0.9314        | 40.0  | 520  | 0.8168          | 0.69     | 0.4583     | 2.6230 | 0.69     | 0.6267   | 0.2696 | 0.1161 |
| 0.9314        | 41.0  | 533  | 0.8090          | 0.7      | 0.4529     | 2.6449 | 0.7      | 0.6236   | 0.2445 | 0.1187 |
| 0.9314        | 42.0  | 546  | 0.8168          | 0.68     | 0.4586     | 2.5516 | 0.68     | 0.6162   | 0.2722 | 0.1275 |
| 0.9314        | 43.0  | 559  | 0.8100          | 0.7      | 0.4523     | 2.5565 | 0.7      | 0.6347   | 0.2869 | 0.1192 |
| 0.9314        | 44.0  | 572  | 0.8078          | 0.7      | 0.4514     | 2.5734 | 0.7      | 0.6344   | 0.2583 | 0.1172 |
| 0.9314        | 45.0  | 585  | 0.8022          | 0.715    | 0.4472     | 2.4971 | 0.715    | 0.6534   | 0.2890 | 0.1165 |
| 0.9314        | 46.0  | 598  | 0.8049          | 0.695    | 0.4484     | 2.4891 | 0.695    | 0.6423   | 0.2722 | 0.1189 |
| 0.9314        | 47.0  | 611  | 0.8025          | 0.705    | 0.4481     | 2.4929 | 0.705    | 0.6393   | 0.2650 | 0.1124 |
| 0.9314        | 48.0  | 624  | 0.7973          | 0.7      | 0.4439     | 2.5000 | 0.7      | 0.6292   | 0.2718 | 0.1142 |
| 0.9314        | 49.0  | 637  | 0.8011          | 0.7      | 0.4464     | 2.5713 | 0.7      | 0.6303   | 0.2400 | 0.1183 |
| 0.9314        | 50.0  | 650  | 0.8012          | 0.7      | 0.4467     | 2.5682 | 0.7      | 0.6313   | 0.2684 | 0.1170 |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.2.0.dev20231002
- Datasets 2.7.1
- Tokenizers 0.13.3

adve email form letter memo news note report resume scientific