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# resnet-50-finetuned-barkley

This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9221
- Precision: 0.8780
- Recall: 0.8618
- F1: 0.8574
- Accuracy: 0.8744
- Top1 Accuracy: 0.8618
- Error Rate: 0.1256

## 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: 0.0002
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy | Top1 Accuracy | Error Rate |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------------:|:----------:|
| 1.6171        | 1.0   | 38   | 1.6195          | 0.0663    | 0.1513 | 0.0664 | 0.1738   | 0.1513        | 0.8262     |
| 1.6149        | 2.0   | 76   | 1.6160          | 0.2953    | 0.1579 | 0.0802 | 0.1785   | 0.1579        | 0.8215     |
| 1.6119        | 3.0   | 114  | 1.6112          | 0.0804    | 0.1579 | 0.0834 | 0.1772   | 0.1579        | 0.8228     |
| 1.6041        | 4.0   | 152  | 1.6015          | 0.4161    | 0.1974 | 0.1461 | 0.2155   | 0.1974        | 0.7845     |
| 1.5945        | 5.0   | 190  | 1.5895          | 0.4089    | 0.2895 | 0.2428 | 0.3092   | 0.2895        | 0.6908     |
| 1.5777        | 6.0   | 228  | 1.5710          | 0.5764    | 0.4408 | 0.3944 | 0.4663   | 0.4408        | 0.5337     |
| 1.561         | 7.0   | 266  | 1.5490          | 0.6013    | 0.4934 | 0.4516 | 0.5173   | 0.5           | 0.4827     |
| 1.536         | 8.0   | 304  | 1.5222          | 0.6377    | 0.5132 | 0.4711 | 0.5450   | 0.5132        | 0.4550     |
| 1.5081        | 9.0   | 342  | 1.4912          | 0.7595    | 0.5987 | 0.5869 | 0.6250   | 0.5987        | 0.3750     |
| 1.4756        | 10.0  | 380  | 1.4566          | 0.7579    | 0.6447 | 0.6293 | 0.6683   | 0.6447        | 0.3317     |
| 1.4387        | 11.0  | 418  | 1.4156          | 0.7914    | 0.6776 | 0.6692 | 0.6985   | 0.6776        | 0.3015     |
| 1.3993        | 12.0  | 456  | 1.3737          | 0.7997    | 0.6842 | 0.6732 | 0.7080   | 0.6842        | 0.2920     |
| 1.358         | 13.0  | 494  | 1.3288          | 0.8290    | 0.7039 | 0.7048 | 0.7232   | 0.7039        | 0.2768     |
| 1.3139        | 14.0  | 532  | 1.2806          | 0.8277    | 0.7434 | 0.7373 | 0.7592   | 0.75          | 0.2408     |
| 1.262         | 15.0  | 570  | 1.2345          | 0.8478    | 0.7697 | 0.7664 | 0.7829   | 0.7697        | 0.2171     |
| 1.2184        | 16.0  | 608  | 1.1887          | 0.8323    | 0.7697 | 0.7654 | 0.7818   | 0.7697        | 0.2182     |
| 1.1803        | 17.0  | 646  | 1.1408          | 0.8423    | 0.7763 | 0.7735 | 0.7931   | 0.7763        | 0.2069     |
| 1.1422        | 18.0  | 684  | 1.0966          | 0.8594    | 0.8158 | 0.8100 | 0.8317   | 0.8158        | 0.1683     |
| 1.1032        | 19.0  | 722  | 1.0587          | 0.8431    | 0.8026 | 0.7969 | 0.8145   | 0.8026        | 0.1855     |
| 1.058         | 20.0  | 760  | 1.0289          | 0.8610    | 0.8355 | 0.8301 | 0.8487   | 0.8355        | 0.1513     |
| 1.0252        | 21.0  | 798  | 0.9918          | 0.8576    | 0.8421 | 0.8370 | 0.8534   | 0.8421        | 0.1466     |
| 1.002         | 22.0  | 836  | 0.9727          | 0.8677    | 0.8487 | 0.8435 | 0.8611   | 0.8487        | 0.1389     |
| 0.9812        | 23.0  | 874  | 0.9465          | 0.8795    | 0.8553 | 0.8497 | 0.8678   | 0.8553        | 0.1322     |
| 0.9636        | 24.0  | 912  | 0.9331          | 0.8820    | 0.8553 | 0.8485 | 0.8699   | 0.8553        | 0.1301     |
| 0.9591        | 25.0  | 950  | 0.9221          | 0.8780    | 0.8618 | 0.8574 | 0.8744   | 0.8618        | 0.1256     |
| 0.948         | 26.0  | 988  | 0.9158          | 0.8780    | 0.8618 | 0.8574 | 0.8744   | 0.8684        | 0.1256     |
| 0.9384        | 27.0  | 1026 | 0.9017          | 0.8685    | 0.8487 | 0.8431 | 0.8601   | 0.8487        | 0.1399     |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.5.0+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1

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