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# resnet101-base_tobacco-cnn_tobacco3482_og_simkd

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: 0.1263
- Accuracy: 0.295
- Brier Loss: 0.7485
- Nll: 6.2362
- F1 Micro: 0.295
- F1 Macro: 0.1126
- Ece: 0.2177
- Aurc: 0.4648

## 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: 128
- eval_batch_size: 128
- 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   | 7    | 0.1887          | 0.18     | 0.8839     | 8.4886 | 0.18     | 0.0305   | 0.2321 | 0.8333 |
| No log        | 2.0   | 14   | 0.1901          | 0.18     | 0.8775     | 6.8406 | 0.18     | 0.0305   | 0.2593 | 0.8187 |
| No log        | 3.0   | 21   | 0.2085          | 0.28     | 0.9045     | 7.0658 | 0.28     | 0.1005   | 0.3168 | 0.6083 |
| No log        | 4.0   | 28   | 0.2493          | 0.155    | 0.9936     | 8.0153 | 0.155    | 0.0300   | 0.3996 | 0.6329 |
| No log        | 5.0   | 35   | 0.2952          | 0.16     | 1.0253     | 8.3946 | 0.16     | 0.0355   | 0.4181 | 0.6026 |
| No log        | 6.0   | 42   | 0.2344          | 0.195    | 0.9022     | 7.0548 | 0.195    | 0.0485   | 0.3118 | 0.8445 |
| No log        | 7.0   | 49   | 0.1665          | 0.18     | 0.9004     | 6.7765 | 0.18     | 0.0310   | 0.2888 | 0.7617 |
| No log        | 8.0   | 56   | 0.1696          | 0.18     | 0.9279     | 9.0648 | 0.18     | 0.0309   | 0.3021 | 0.7025 |
| No log        | 9.0   | 63   | 0.1715          | 0.18     | 0.9330     | 9.0774 | 0.18     | 0.0305   | 0.2992 | 0.7525 |
| No log        | 10.0  | 70   | 0.1369          | 0.285    | 0.8092     | 6.9372 | 0.285    | 0.1134   | 0.2993 | 0.4899 |
| No log        | 11.0  | 77   | 0.1584          | 0.18     | 0.8953     | 8.9899 | 0.18     | 0.0310   | 0.2666 | 0.7495 |
| No log        | 12.0  | 84   | 0.1690          | 0.18     | 0.8896     | 8.9605 | 0.18     | 0.0310   | 0.2593 | 0.7452 |
| No log        | 13.0  | 91   | 0.1636          | 0.18     | 0.8848     | 8.9907 | 0.18     | 0.0310   | 0.2661 | 0.7474 |
| No log        | 14.0  | 98   | 0.1685          | 0.18     | 0.8815     | 8.9991 | 0.18     | 0.0309   | 0.2676 | 0.7750 |
| No log        | 15.0  | 105  | 0.1678          | 0.18     | 0.8807     | 8.9352 | 0.18     | 0.0305   | 0.2658 | 0.7448 |
| No log        | 16.0  | 112  | 0.1599          | 0.18     | 0.8848     | 9.0210 | 0.18     | 0.0309   | 0.2707 | 0.7742 |
| No log        | 17.0  | 119  | 0.1553          | 0.18     | 0.8559     | 7.2132 | 0.18     | 0.0305   | 0.2569 | 0.7479 |
| No log        | 18.0  | 126  | 0.1620          | 0.18     | 0.8728     | 8.8826 | 0.18     | 0.0308   | 0.2472 | 0.7289 |
| No log        | 19.0  | 133  | 0.1631          | 0.18     | 0.8600     | 8.8681 | 0.18     | 0.0305   | 0.2689 | 0.7046 |
| No log        | 20.0  | 140  | 0.1616          | 0.18     | 0.8702     | 8.8768 | 0.18     | 0.0305   | 0.2532 | 0.7489 |
| No log        | 21.0  | 147  | 0.1521          | 0.18     | 0.8505     | 6.9939 | 0.18     | 0.0310   | 0.2687 | 0.7479 |
| No log        | 22.0  | 154  | 0.1290          | 0.285    | 0.7742     | 6.8763 | 0.285    | 0.1123   | 0.2907 | 0.4899 |
| No log        | 23.0  | 161  | 0.1256          | 0.305    | 0.7453     | 6.2659 | 0.305    | 0.1190   | 0.2133 | 0.4457 |
| No log        | 24.0  | 168  | 0.1257          | 0.305    | 0.7527     | 6.7983 | 0.305    | 0.1192   | 0.2483 | 0.4694 |
| No log        | 25.0  | 175  | 0.1256          | 0.295    | 0.7483     | 6.7540 | 0.295    | 0.1106   | 0.2233 | 0.4632 |
| No log        | 26.0  | 182  | 0.1277          | 0.3      | 0.7590     | 6.6632 | 0.3      | 0.1214   | 0.2641 | 0.4563 |
| No log        | 27.0  | 189  | 0.1644          | 0.18     | 0.8539     | 8.7216 | 0.18     | 0.0306   | 0.2483 | 0.7170 |
| No log        | 28.0  | 196  | 0.1268          | 0.305    | 0.7494     | 6.5633 | 0.305    | 0.1146   | 0.2379 | 0.4509 |
| No log        | 29.0  | 203  | 0.1246          | 0.305    | 0.7376     | 6.2718 | 0.305    | 0.1158   | 0.2319 | 0.4326 |
| No log        | 30.0  | 210  | 0.1249          | 0.3      | 0.7428     | 6.5246 | 0.3      | 0.1138   | 0.2463 | 0.4449 |
| No log        | 31.0  | 217  | 0.1284          | 0.295    | 0.7474     | 6.4668 | 0.295    | 0.1116   | 0.2566 | 0.4550 |
| No log        | 32.0  | 224  | 0.1715          | 0.18     | 0.8599     | 8.5902 | 0.18     | 0.0310   | 0.2413 | 0.7447 |
| No log        | 33.0  | 231  | 0.1566          | 0.18     | 0.8495     | 7.4352 | 0.18     | 0.0308   | 0.2624 | 0.7110 |
| No log        | 34.0  | 238  | 0.1370          | 0.275    | 0.7990     | 6.5052 | 0.275    | 0.1096   | 0.2760 | 0.5186 |
| No log        | 35.0  | 245  | 0.1289          | 0.3      | 0.7569     | 6.4685 | 0.3      | 0.1212   | 0.2643 | 0.4524 |
| No log        | 36.0  | 252  | 0.1557          | 0.18     | 0.8493     | 6.8218 | 0.18     | 0.0305   | 0.2574 | 0.7401 |
| No log        | 37.0  | 259  | 0.1629          | 0.18     | 0.8558     | 8.5068 | 0.18     | 0.0310   | 0.2522 | 0.7466 |
| No log        | 38.0  | 266  | 0.1386          | 0.275    | 0.8117     | 6.4244 | 0.275    | 0.1053   | 0.2455 | 0.5912 |
| No log        | 39.0  | 273  | 0.1601          | 0.18     | 0.8508     | 8.3697 | 0.18     | 0.0305   | 0.2445 | 0.7048 |
| No log        | 40.0  | 280  | 0.1510          | 0.185    | 0.8428     | 6.8710 | 0.185    | 0.0369   | 0.2517 | 0.7155 |
| No log        | 41.0  | 287  | 0.1315          | 0.29     | 0.7675     | 6.1897 | 0.29     | 0.1167   | 0.2708 | 0.4594 |
| No log        | 42.0  | 294  | 0.1235          | 0.3      | 0.7405     | 6.1762 | 0.3      | 0.1158   | 0.2338 | 0.4496 |
| No log        | 43.0  | 301  | 0.1250          | 0.295    | 0.7456     | 6.3789 | 0.295    | 0.1174   | 0.2524 | 0.4548 |
| No log        | 44.0  | 308  | 0.1249          | 0.285    | 0.7440     | 6.3862 | 0.285    | 0.1097   | 0.2405 | 0.4680 |
| No log        | 45.0  | 315  | 0.1245          | 0.29     | 0.7428     | 6.4641 | 0.29     | 0.1117   | 0.2403 | 0.4623 |
| No log        | 46.0  | 322  | 0.1245          | 0.295    | 0.7440     | 6.5208 | 0.295    | 0.1149   | 0.2385 | 0.4610 |
| No log        | 47.0  | 329  | 0.1250          | 0.29     | 0.7464     | 6.2221 | 0.29     | 0.1117   | 0.2332 | 0.4674 |
| No log        | 48.0  | 336  | 0.1263          | 0.295    | 0.7458     | 6.3085 | 0.295    | 0.1126   | 0.2375 | 0.4670 |
| No log        | 49.0  | 343  | 0.1252          | 0.29     | 0.7469     | 6.0647 | 0.29     | 0.1117   | 0.2410 | 0.4679 |
| No log        | 50.0  | 350  | 0.1263          | 0.295    | 0.7485     | 6.2362 | 0.295    | 0.1126   | 0.2177 | 0.4648 |


### 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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