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# resnet101-base_tobacco-cnn_tobacco3482_kd_MSE

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: 0.0899
- Accuracy: 0.395
- Brier Loss: 0.6867
- Nll: 4.7352
- F1 Micro: 0.395
- F1 Macro: 0.2347
- Ece: 0.2366
- Aurc: 0.3626

## 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   | 1.1202          | 0.17     | 0.8964     | 8.4790 | 0.17     | 0.1089   | 0.2136 | 0.8244 |
| No log        | 2.0   | 26   | 1.0772          | 0.165    | 0.8950     | 8.2397 | 0.165    | 0.0929   | 0.2120 | 0.8534 |
| No log        | 3.0   | 39   | 0.9427          | 0.2      | 0.8847     | 7.1036 | 0.2000   | 0.0796   | 0.2384 | 0.7748 |
| No log        | 4.0   | 52   | 0.7947          | 0.21     | 0.8720     | 6.5481 | 0.2100   | 0.0649   | 0.2432 | 0.7270 |
| No log        | 5.0   | 65   | 0.5378          | 0.205    | 0.8432     | 6.3064 | 0.205    | 0.0544   | 0.2367 | 0.6763 |
| No log        | 6.0   | 78   | 0.4557          | 0.18     | 0.8402     | 6.3878 | 0.18     | 0.0308   | 0.2384 | 0.7467 |
| No log        | 7.0   | 91   | 0.4326          | 0.18     | 0.8383     | 6.3386 | 0.18     | 0.0308   | 0.2385 | 0.7234 |
| No log        | 8.0   | 104  | 0.2832          | 0.265    | 0.8085     | 6.3561 | 0.265    | 0.1012   | 0.2570 | 0.6272 |
| No log        | 9.0   | 117  | 0.2672          | 0.255    | 0.8124     | 6.2296 | 0.255    | 0.0981   | 0.2569 | 0.6567 |
| No log        | 10.0  | 130  | 0.2452          | 0.29     | 0.7953     | 6.3199 | 0.29     | 0.1153   | 0.2717 | 0.5884 |
| No log        | 11.0  | 143  | 0.2155          | 0.31     | 0.7764     | 6.3618 | 0.31     | 0.1231   | 0.2728 | 0.4803 |
| No log        | 12.0  | 156  | 0.1315          | 0.31     | 0.7371     | 6.2610 | 0.31     | 0.1231   | 0.2343 | 0.4419 |
| No log        | 13.0  | 169  | 0.1803          | 0.3      | 0.7665     | 6.1189 | 0.3      | 0.1187   | 0.2587 | 0.4579 |
| No log        | 14.0  | 182  | 0.1426          | 0.31     | 0.7386     | 6.1115 | 0.31     | 0.1236   | 0.2502 | 0.4341 |
| No log        | 15.0  | 195  | 0.1431          | 0.31     | 0.7334     | 5.9353 | 0.31     | 0.1274   | 0.2624 | 0.4233 |
| No log        | 16.0  | 208  | 0.1540          | 0.32     | 0.7318     | 5.7102 | 0.32     | 0.1432   | 0.2493 | 0.4322 |
| No log        | 17.0  | 221  | 0.2603          | 0.305    | 0.7784     | 5.6776 | 0.305    | 0.1361   | 0.2751 | 0.5118 |
| No log        | 18.0  | 234  | 0.1000          | 0.35     | 0.7074     | 5.4636 | 0.35     | 0.1574   | 0.2420 | 0.4027 |
| No log        | 19.0  | 247  | 0.1014          | 0.33     | 0.7131     | 5.5297 | 0.33     | 0.1413   | 0.2439 | 0.4245 |
| No log        | 20.0  | 260  | 0.2862          | 0.265    | 0.8013     | 5.5041 | 0.265    | 0.1126   | 0.2762 | 0.6324 |
| No log        | 21.0  | 273  | 0.1224          | 0.34     | 0.7183     | 5.2027 | 0.34     | 0.1544   | 0.2673 | 0.4222 |
| No log        | 22.0  | 286  | 0.1406          | 0.345    | 0.7173     | 5.1426 | 0.345    | 0.1612   | 0.2710 | 0.4019 |
| No log        | 23.0  | 299  | 0.1509          | 0.34     | 0.7270     | 5.0281 | 0.34     | 0.1565   | 0.2641 | 0.4178 |
| No log        | 24.0  | 312  | 0.0994          | 0.37     | 0.6996     | 5.1278 | 0.37     | 0.1771   | 0.2390 | 0.3930 |
| No log        | 25.0  | 325  | 0.1965          | 0.35     | 0.7474     | 5.0356 | 0.35     | 0.1707   | 0.2774 | 0.4503 |
| No log        | 26.0  | 338  | 0.1104          | 0.37     | 0.7085     | 5.0275 | 0.37     | 0.1984   | 0.2663 | 0.3927 |
| No log        | 27.0  | 351  | 0.1674          | 0.34     | 0.7299     | 4.9200 | 0.34     | 0.1739   | 0.2787 | 0.4257 |
| No log        | 28.0  | 364  | 0.2424          | 0.335    | 0.7626     | 5.0286 | 0.335    | 0.1693   | 0.2905 | 0.5297 |
| No log        | 29.0  | 377  | 0.1261          | 0.345    | 0.7185     | 5.0591 | 0.345    | 0.1730   | 0.2892 | 0.4142 |
| No log        | 30.0  | 390  | 0.1574          | 0.365    | 0.7213     | 4.8809 | 0.3650   | 0.1951   | 0.2983 | 0.4062 |
| No log        | 31.0  | 403  | 0.1227          | 0.365    | 0.7098     | 4.8152 | 0.3650   | 0.1996   | 0.2802 | 0.3992 |
| No log        | 32.0  | 416  | 0.1114          | 0.355    | 0.7010     | 4.8224 | 0.3550   | 0.1915   | 0.2657 | 0.3958 |
| No log        | 33.0  | 429  | 0.1027          | 0.39     | 0.6934     | 4.7755 | 0.39     | 0.2245   | 0.2653 | 0.3695 |
| No log        | 34.0  | 442  | 0.0959          | 0.385    | 0.6875     | 4.8715 | 0.3850   | 0.2299   | 0.2591 | 0.3699 |
| No log        | 35.0  | 455  | 0.0905          | 0.395    | 0.6897     | 4.8649 | 0.395    | 0.2367   | 0.2519 | 0.3627 |
| No log        | 36.0  | 468  | 0.0879          | 0.365    | 0.6911     | 4.8472 | 0.3650   | 0.2132   | 0.2437 | 0.3910 |
| No log        | 37.0  | 481  | 0.0867          | 0.39     | 0.6881     | 4.7379 | 0.39     | 0.2335   | 0.2576 | 0.3680 |
| No log        | 38.0  | 494  | 0.0934          | 0.4      | 0.6916     | 4.6797 | 0.4000   | 0.2490   | 0.2578 | 0.3628 |
| 0.2032        | 39.0  | 507  | 0.0928          | 0.38     | 0.6901     | 4.6734 | 0.38     | 0.2268   | 0.2432 | 0.3783 |
| 0.2032        | 40.0  | 520  | 0.0995          | 0.39     | 0.6875     | 4.8180 | 0.39     | 0.2323   | 0.2647 | 0.3730 |
| 0.2032        | 41.0  | 533  | 0.0944          | 0.37     | 0.6892     | 4.8193 | 0.37     | 0.2174   | 0.2536 | 0.3862 |
| 0.2032        | 42.0  | 546  | 0.0904          | 0.415    | 0.6885     | 4.5644 | 0.415    | 0.2556   | 0.2729 | 0.3573 |
| 0.2032        | 43.0  | 559  | 0.0951          | 0.39     | 0.6899     | 4.6549 | 0.39     | 0.2417   | 0.2525 | 0.3692 |
| 0.2032        | 44.0  | 572  | 0.0884          | 0.4      | 0.6860     | 4.6572 | 0.4000   | 0.2402   | 0.2587 | 0.3557 |
| 0.2032        | 45.0  | 585  | 0.0867          | 0.38     | 0.6874     | 4.6558 | 0.38     | 0.2278   | 0.2526 | 0.3738 |
| 0.2032        | 46.0  | 598  | 0.0861          | 0.405    | 0.6844     | 4.5777 | 0.405    | 0.2537   | 0.2548 | 0.3628 |
| 0.2032        | 47.0  | 611  | 0.0874          | 0.385    | 0.6853     | 4.4946 | 0.3850   | 0.2380   | 0.2570 | 0.3743 |
| 0.2032        | 48.0  | 624  | 0.0880          | 0.405    | 0.6857     | 4.5605 | 0.405    | 0.2500   | 0.2489 | 0.3555 |
| 0.2032        | 49.0  | 637  | 0.0884          | 0.4      | 0.6853     | 4.6057 | 0.4000   | 0.2481   | 0.2401 | 0.3616 |
| 0.2032        | 50.0  | 650  | 0.0899          | 0.395    | 0.6867     | 4.7352 | 0.395    | 0.2347   | 0.2366 | 0.3626 |


### 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_MSE", "results": []}]}
transformers pytorch resnet image-classification generated_from_trainer base_model:microsoft/resnet-50 license:apache-2.0 autotrain_compatible endpoints_compatible region:us