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# resnet-50-finetuned-omars5

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: 0.5844
- Accuracy: 0.8845

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3431        | 0.99  | 92   | 1.2810          | 0.5836   |
| 1.0465        | 2.0   | 185  | 0.8740          | 0.8176   |
| 0.8755        | 2.99  | 277  | 0.6467          | 0.7994   |
| 0.7459        | 4.0   | 370  | 0.5379          | 0.8480   |
| 0.7983        | 4.99  | 462  | 0.4385          | 0.8207   |
| 0.7692        | 6.0   | 555  | 0.5795          | 0.7842   |
| 0.5158        | 6.99  | 647  | 0.4936          | 0.8207   |
| 0.625         | 8.0   | 740  | 0.5316          | 0.8298   |
| 0.511         | 8.99  | 832  | 0.5202          | 0.8845   |
| 0.5025        | 10.0  | 925  | 0.5260          | 0.8784   |
| 0.508         | 10.99 | 1017 | 0.5307          | 0.8632   |
| 0.4652        | 12.0  | 1110 | 0.6060          | 0.8480   |
| 0.4432        | 12.99 | 1202 | 0.5051          | 0.8845   |
| 0.3373        | 14.0  | 1295 | 0.8695          | 0.8845   |
| 0.3968        | 14.99 | 1387 | 0.6805          | 0.8571   |
| 0.4268        | 16.0  | 1480 | 0.6541          | 0.8815   |
| 0.3029        | 16.99 | 1572 | 0.5710          | 0.8906   |
| 0.3801        | 18.0  | 1665 | 0.6499          | 0.8571   |
| 0.3545        | 18.99 | 1757 | 0.6727          | 0.8419   |
| 0.3526        | 20.0  | 1850 | 0.6542          | 0.8571   |
| 0.3458        | 20.99 | 1942 | 0.6625          | 0.8997   |
| 0.3078        | 22.0  | 2035 | 0.6551          | 0.8784   |
| 0.3677        | 22.99 | 2127 | 0.5953          | 0.8815   |
| 0.3386        | 24.0  | 2220 | 0.6549          | 0.8693   |
| 0.213         | 24.99 | 2312 | 0.5846          | 0.8997   |
| 0.3778        | 26.0  | 2405 | 0.6746          | 0.8602   |
| 0.3079        | 26.99 | 2497 | 0.6594          | 0.8997   |
| 0.2943        | 28.0  | 2590 | 0.6246          | 0.8815   |
| 0.2782        | 28.99 | 2682 | 0.6550          | 0.8906   |
| 0.2931        | 29.84 | 2760 | 0.5844          | 0.8845   |


### Framework versions

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

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