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# resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t5.0_a0.5

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: 0.6454
- Accuracy: 0.685
- Brier Loss: 0.4931
- Nll: 2.5040
- F1 Micro: 0.685
- F1 Macro: 0.6171
- Ece: 0.2996
- Aurc: 0.1499

## 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   | 1.4475          | 0.17     | 0.8966     | 8.4781 | 0.17     | 0.1126   | 0.2169 | 0.8299 |
| No log        | 2.0   | 26   | 1.4360          | 0.165    | 0.8955     | 8.4118 | 0.165    | 0.1097   | 0.2115 | 0.8359 |
| No log        | 3.0   | 39   | 1.3776          | 0.16     | 0.8842     | 6.1685 | 0.16     | 0.0633   | 0.2066 | 0.7780 |
| No log        | 4.0   | 52   | 1.3085          | 0.2      | 0.8701     | 6.0521 | 0.2000   | 0.0728   | 0.2332 | 0.7424 |
| No log        | 5.0   | 65   | 1.2551          | 0.18     | 0.8597     | 6.1887 | 0.18     | 0.0491   | 0.2265 | 0.7890 |
| No log        | 6.0   | 78   | 1.2118          | 0.2      | 0.8489     | 6.1706 | 0.2000   | 0.0631   | 0.2324 | 0.7179 |
| No log        | 7.0   | 91   | 1.1759          | 0.19     | 0.8418     | 6.1310 | 0.19     | 0.0428   | 0.2384 | 0.7431 |
| No log        | 8.0   | 104  | 1.1577          | 0.195    | 0.8339     | 5.7305 | 0.195    | 0.0535   | 0.2399 | 0.7126 |
| No log        | 9.0   | 117  | 0.9905          | 0.34     | 0.7692     | 6.1092 | 0.34     | 0.1567   | 0.2772 | 0.4320 |
| No log        | 10.0  | 130  | 0.9603          | 0.355    | 0.7541     | 5.7998 | 0.3550   | 0.1969   | 0.3021 | 0.4111 |
| No log        | 11.0  | 143  | 1.0839          | 0.255    | 0.8087     | 5.1464 | 0.255    | 0.1242   | 0.2389 | 0.6769 |
| No log        | 12.0  | 156  | 0.9374          | 0.39     | 0.7410     | 4.8415 | 0.39     | 0.2220   | 0.3037 | 0.4194 |
| No log        | 13.0  | 169  | 0.9974          | 0.33     | 0.7720     | 4.9023 | 0.33     | 0.1732   | 0.2863 | 0.6049 |
| No log        | 14.0  | 182  | 0.9393          | 0.435    | 0.7251     | 4.3102 | 0.435    | 0.2455   | 0.3276 | 0.3645 |
| No log        | 15.0  | 195  | 0.9554          | 0.39     | 0.7416     | 4.0361 | 0.39     | 0.2535   | 0.2721 | 0.5075 |
| No log        | 16.0  | 208  | 0.8012          | 0.465    | 0.6445     | 4.0129 | 0.465    | 0.2935   | 0.2551 | 0.2839 |
| No log        | 17.0  | 221  | 0.8033          | 0.53     | 0.6418     | 3.4959 | 0.53     | 0.3816   | 0.3242 | 0.2458 |
| No log        | 18.0  | 234  | 0.7740          | 0.57     | 0.6204     | 3.4062 | 0.57     | 0.4297   | 0.3139 | 0.2245 |
| No log        | 19.0  | 247  | 0.7736          | 0.6      | 0.6124     | 3.3460 | 0.6      | 0.4408   | 0.3017 | 0.1919 |
| No log        | 20.0  | 260  | 0.9105          | 0.555    | 0.6919     | 3.2115 | 0.555    | 0.4524   | 0.3604 | 0.3099 |
| No log        | 21.0  | 273  | 0.7416          | 0.61     | 0.5948     | 3.1349 | 0.61     | 0.5093   | 0.3176 | 0.2233 |
| No log        | 22.0  | 286  | 0.7318          | 0.655    | 0.5815     | 3.1259 | 0.655    | 0.5433   | 0.3478 | 0.1672 |
| No log        | 23.0  | 299  | 0.7799          | 0.59     | 0.6079     | 3.0590 | 0.59     | 0.4963   | 0.3340 | 0.2455 |
| No log        | 24.0  | 312  | 0.7886          | 0.665    | 0.6038     | 2.9965 | 0.665    | 0.5575   | 0.3773 | 0.1623 |
| No log        | 25.0  | 325  | 0.7083          | 0.66     | 0.5602     | 3.0752 | 0.66     | 0.5582   | 0.3283 | 0.1772 |
| No log        | 26.0  | 338  | 0.6882          | 0.63     | 0.5507     | 2.9022 | 0.63     | 0.5404   | 0.2963 | 0.1851 |
| No log        | 27.0  | 351  | 0.6774          | 0.66     | 0.5348     | 2.7876 | 0.66     | 0.5674   | 0.3095 | 0.1662 |
| No log        | 28.0  | 364  | 0.8111          | 0.675    | 0.6067     | 2.7578 | 0.675    | 0.5800   | 0.3905 | 0.1923 |
| No log        | 29.0  | 377  | 0.6803          | 0.645    | 0.5338     | 2.8666 | 0.645    | 0.5486   | 0.3054 | 0.1646 |
| No log        | 30.0  | 390  | 0.6835          | 0.685    | 0.5336     | 2.5944 | 0.685    | 0.5840   | 0.3119 | 0.1595 |
| No log        | 31.0  | 403  | 0.6810          | 0.655    | 0.5309     | 2.7112 | 0.655    | 0.5625   | 0.2879 | 0.1786 |
| No log        | 32.0  | 416  | 0.6848          | 0.685    | 0.5194     | 2.6456 | 0.685    | 0.5893   | 0.3314 | 0.1350 |
| No log        | 33.0  | 429  | 0.6631          | 0.695    | 0.5063     | 2.6286 | 0.695    | 0.5980   | 0.3198 | 0.1314 |
| No log        | 34.0  | 442  | 0.6639          | 0.69     | 0.5126     | 2.3890 | 0.69     | 0.5834   | 0.2990 | 0.1376 |
| No log        | 35.0  | 455  | 0.6736          | 0.675    | 0.5172     | 2.3291 | 0.675    | 0.6014   | 0.3148 | 0.1646 |
| No log        | 36.0  | 468  | 0.6648          | 0.68     | 0.5137     | 2.4549 | 0.68     | 0.6156   | 0.3316 | 0.1492 |
| No log        | 37.0  | 481  | 0.6543          | 0.7      | 0.5006     | 2.4275 | 0.7      | 0.6130   | 0.3041 | 0.1342 |
| No log        | 38.0  | 494  | 0.6514          | 0.675    | 0.5001     | 2.4064 | 0.675    | 0.5984   | 0.2963 | 0.1491 |
| 0.7462        | 39.0  | 507  | 0.6498          | 0.71     | 0.4988     | 2.5772 | 0.7100   | 0.6405   | 0.2980 | 0.1335 |
| 0.7462        | 40.0  | 520  | 0.6496          | 0.705    | 0.4964     | 2.5649 | 0.705    | 0.6386   | 0.3060 | 0.1380 |
| 0.7462        | 41.0  | 533  | 0.6562          | 0.68     | 0.5027     | 2.5816 | 0.68     | 0.6026   | 0.3100 | 0.1467 |
| 0.7462        | 42.0  | 546  | 0.6632          | 0.68     | 0.5089     | 2.4570 | 0.68     | 0.6112   | 0.2989 | 0.1500 |
| 0.7462        | 43.0  | 559  | 0.6437          | 0.7      | 0.4885     | 2.3648 | 0.7      | 0.6331   | 0.2741 | 0.1427 |
| 0.7462        | 44.0  | 572  | 0.6435          | 0.705    | 0.4894     | 2.4253 | 0.705    | 0.6370   | 0.3043 | 0.1390 |
| 0.7462        | 45.0  | 585  | 0.6457          | 0.695    | 0.4929     | 2.3611 | 0.695    | 0.6314   | 0.3021 | 0.1449 |
| 0.7462        | 46.0  | 598  | 0.6437          | 0.695    | 0.4912     | 2.3639 | 0.695    | 0.6370   | 0.2984 | 0.1436 |
| 0.7462        | 47.0  | 611  | 0.6466          | 0.685    | 0.4933     | 2.4859 | 0.685    | 0.6306   | 0.2936 | 0.1474 |
| 0.7462        | 48.0  | 624  | 0.6470          | 0.67     | 0.4950     | 2.3782 | 0.67     | 0.6070   | 0.3139 | 0.1547 |
| 0.7462        | 49.0  | 637  | 0.6477          | 0.675    | 0.4945     | 2.4509 | 0.675    | 0.6092   | 0.2852 | 0.1527 |
| 0.7462        | 50.0  | 650  | 0.6454          | 0.685    | 0.4931     | 2.5040 | 0.685    | 0.6171   | 0.2996 | 0.1499 |


### Framework versions

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

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "microsoft/resnet-50", "model-index": [{"name": "resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t5.0_a0.5", "results": []}]}
transformers pytorch resnet image-classification generated_from_trainer base_model:microsoft/resnet-50 license:apache-2.0 autotrain_compatible endpoints_compatible region:us