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# resnet-50

This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6576
- F1 Macro: 0.2323
- F1 Micro: 0.3485
- F1 Weighted: 0.2841
- Precision Macro: 0.2908
- Precision Micro: 0.3485
- Precision Weighted: 0.3373
- Recall Macro: 0.2776
- Recall Micro: 0.3485
- Recall Weighted: 0.3485
- Accuracy: 0.3485

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 Micro | F1 Weighted | Precision Macro | Precision Micro | Precision Weighted | Recall Macro | Recall Micro | Recall Weighted | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|:---------------:|:---------------:|:------------------:|:------------:|:------------:|:---------------:|:--------:|
| 1.9401        | 1.0   | 29   | 1.9376          | 0.0673   | 0.1364   | 0.0898      | 0.0524          | 0.1364          | 0.0693             | 0.1020       | 0.1364       | 0.1364          | 0.1364   |
| 1.9122        | 2.0   | 58   | 1.9165          | 0.0601   | 0.2045   | 0.0852      | 0.0463          | 0.2045          | 0.0648             | 0.1433       | 0.2045       | 0.2045          | 0.2045   |
| 1.9226        | 3.0   | 87   | 1.8974          | 0.0729   | 0.2197   | 0.1023      | 0.0542          | 0.2197          | 0.0754             | 0.1547       | 0.2197       | 0.2197          | 0.2197   |
| 1.8609        | 4.0   | 116  | 1.8879          | 0.0479   | 0.1894   | 0.0686      | 0.0293          | 0.1894          | 0.0419             | 0.1323       | 0.1894       | 0.1894          | 0.1894   |
| 1.8345        | 5.0   | 145  | 1.8808          | 0.0498   | 0.2045   | 0.0713      | 0.0301          | 0.2045          | 0.0431             | 0.1429       | 0.2045       | 0.2045          | 0.2045   |
| 1.8965        | 6.0   | 174  | 1.8803          | 0.0555   | 0.1894   | 0.0787      | 0.0379          | 0.1894          | 0.0534             | 0.1327       | 0.1894       | 0.1894          | 0.1894   |
| 1.8651        | 7.0   | 203  | 1.8732          | 0.0787   | 0.2273   | 0.1100      | 0.0607          | 0.2273          | 0.0840             | 0.1604       | 0.2273       | 0.2273          | 0.2273   |
| 1.8235        | 8.0   | 232  | 1.8693          | 0.0573   | 0.1970   | 0.0813      | 0.0393          | 0.1970          | 0.0554             | 0.1380       | 0.1970       | 0.1970          | 0.1970   |
| 1.7786        | 9.0   | 261  | 1.8613          | 0.1112   | 0.25     | 0.1502      | 0.2131          | 0.25            | 0.2558             | 0.1808       | 0.25         | 0.25            | 0.25     |
| 1.9601        | 10.0  | 290  | 1.8535          | 0.1144   | 0.2576   | 0.1549      | 0.1437          | 0.2576          | 0.1793             | 0.1865       | 0.2576       | 0.2576          | 0.2576   |
| 1.7922        | 11.0  | 319  | 1.8492          | 0.1222   | 0.2727   | 0.1652      | 0.1487          | 0.2727          | 0.1860             | 0.1983       | 0.2727       | 0.2727          | 0.2727   |
| 1.8398        | 12.0  | 348  | 1.8497          | 0.1371   | 0.2727   | 0.1810      | 0.1368          | 0.2727          | 0.1724             | 0.2022       | 0.2727       | 0.2727          | 0.2727   |
| 1.8811        | 13.0  | 377  | 1.8354          | 0.1099   | 0.2424   | 0.1484      | 0.1170          | 0.2424          | 0.1490             | 0.1780       | 0.2424       | 0.2424          | 0.2424   |
| 1.7813        | 14.0  | 406  | 1.8299          | 0.1445   | 0.2955   | 0.1925      | 0.1274          | 0.2955          | 0.1643             | 0.2164       | 0.2955       | 0.2955          | 0.2955   |
| 1.8719        | 15.0  | 435  | 1.8213          | 0.1608   | 0.2955   | 0.2083      | 0.1462          | 0.2955          | 0.1838             | 0.2213       | 0.2955       | 0.2955          | 0.2955   |
| 1.7755        | 16.0  | 464  | 1.8057          | 0.1735   | 0.3182   | 0.2247      | 0.1522          | 0.3182          | 0.1921             | 0.2392       | 0.3182       | 0.3182          | 0.3182   |
| 1.7729        | 17.0  | 493  | 1.7964          | 0.1625   | 0.3106   | 0.2129      | 0.1450          | 0.3106          | 0.1843             | 0.2313       | 0.3106       | 0.3106          | 0.3106   |
| 1.687         | 18.0  | 522  | 1.7865          | 0.1719   | 0.3182   | 0.2237      | 0.1576          | 0.3182          | 0.1987             | 0.2381       | 0.3182       | 0.3182          | 0.3182   |
| 1.7207        | 19.0  | 551  | 1.7771          | 0.1823   | 0.3485   | 0.2394      | 0.1572          | 0.3485          | 0.2012             | 0.2592       | 0.3485       | 0.3485          | 0.3485   |
| 1.7066        | 20.0  | 580  | 1.7672          | 0.1857   | 0.3485   | 0.2424      | 0.1578          | 0.3485          | 0.2015             | 0.2607       | 0.3485       | 0.3485          | 0.3485   |
| 1.7726        | 21.0  | 609  | 1.7596          | 0.2147   | 0.3636   | 0.2710      | 0.2530          | 0.3636          | 0.2931             | 0.2766       | 0.3636       | 0.3636          | 0.3636   |
| 1.7349        | 22.0  | 638  | 1.7517          | 0.2081   | 0.3485   | 0.2627      | 0.2145          | 0.3485          | 0.2554             | 0.2660       | 0.3485       | 0.3485          | 0.3485   |
| 1.7956        | 23.0  | 667  | 1.7437          | 0.2018   | 0.3561   | 0.2590      | 0.1970          | 0.3561          | 0.2402             | 0.2687       | 0.3561       | 0.3561          | 0.3561   |
| 1.4672        | 24.0  | 696  | 1.7264          | 0.2033   | 0.3636   | 0.2611      | 0.2975          | 0.3636          | 0.3356             | 0.2740       | 0.3636       | 0.3636          | 0.3636   |
| 1.6008        | 25.0  | 725  | 1.7233          | 0.2323   | 0.3788   | 0.2905      | 0.2533          | 0.3788          | 0.2963             | 0.2898       | 0.3788       | 0.3788          | 0.3788   |
| 1.6899        | 26.0  | 754  | 1.7199          | 0.2261   | 0.3788   | 0.2852      | 0.2426          | 0.3788          | 0.2863             | 0.2887       | 0.3788       | 0.3788          | 0.3788   |
| 1.7073        | 27.0  | 783  | 1.7113          | 0.2171   | 0.3712   | 0.2752      | 0.2305          | 0.3712          | 0.2729             | 0.2819       | 0.3712       | 0.3712          | 0.3712   |
| 1.6558        | 28.0  | 812  | 1.6996          | 0.2311   | 0.3864   | 0.2923      | 0.2212          | 0.3864          | 0.2677             | 0.2955       | 0.3864       | 0.3864          | 0.3864   |
| 1.4732        | 29.0  | 841  | 1.7078          | 0.2320   | 0.3788   | 0.2901      | 0.2301          | 0.3788          | 0.2742             | 0.2909       | 0.3788       | 0.3788          | 0.3788   |
| 1.6134        | 30.0  | 870  | 1.7132          | 0.2248   | 0.3788   | 0.2845      | 0.2245          | 0.3788          | 0.2692             | 0.2887       | 0.3788       | 0.3788          | 0.3788   |


### Framework versions

- Transformers 4.48.2
- Pytorch 2.6.0+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0

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