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# resnet-50-finetuned-omars3

This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7345
- Accuracy: 0.7436

## 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: 0.0005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3928        | 1.0   | 11   | 1.3871          | 0.2308   |
| 1.3864        | 2.0   | 22   | 1.3820          | 0.3077   |
| 1.3694        | 3.0   | 33   | 1.3510          | 0.5385   |
| 1.3513        | 4.0   | 44   | 1.2942          | 0.4872   |
| 1.3067        | 5.0   | 55   | 1.1984          | 0.6154   |
| 1.2184        | 6.0   | 66   | 0.9974          | 0.6923   |
| 1.0967        | 7.0   | 77   | 0.7869          | 0.6667   |
| 0.9731        | 8.0   | 88   | 0.7923          | 0.7436   |
| 0.9506        | 9.0   | 99   | 0.7161          | 0.6667   |
| 0.7783        | 10.0  | 110  | 0.6736          | 0.6923   |
| 0.7072        | 11.0  | 121  | 0.6693          | 0.7436   |
| 0.6669        | 12.0  | 132  | 0.7203          | 0.6923   |
| 0.6579        | 13.0  | 143  | 0.6195          | 0.7949   |
| 0.6695        | 14.0  | 154  | 0.6395          | 0.7692   |
| 0.678         | 15.0  | 165  | 0.6870          | 0.7692   |
| 0.5919        | 16.0  | 176  | 0.6681          | 0.7692   |
| 0.5459        | 17.0  | 187  | 0.6895          | 0.7692   |
| 0.5635        | 18.0  | 198  | 0.6617          | 0.7692   |
| 0.5378        | 19.0  | 209  | 0.6401          | 0.7949   |
| 0.5105        | 20.0  | 220  | 0.7108          | 0.7692   |
| 0.4656        | 21.0  | 231  | 0.7267          | 0.7692   |
| 0.5338        | 22.0  | 242  | 0.7531          | 0.7436   |
| 0.4846        | 23.0  | 253  | 0.7103          | 0.7179   |
| 0.4212        | 24.0  | 264  | 0.7809          | 0.7436   |
| 0.4677        | 25.0  | 275  | 0.7825          | 0.7692   |
| 0.4496        | 26.0  | 286  | 0.8240          | 0.6923   |
| 0.3784        | 27.0  | 297  | 0.7563          | 0.7179   |
| 0.4949        | 28.0  | 308  | 0.6823          | 0.7692   |
| 0.4612        | 29.0  | 319  | 0.7542          | 0.6667   |
| 0.4491        | 30.0  | 330  | 0.7345          | 0.7436   |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.0
- Tokenizers 0.13.3

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