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# resnet-50-finetuned-omars1

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: 1.2536
- Accuracy: 0.6667

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3877        | 1.0   | 11   | 1.3919          | 0.2564   |
| 1.383         | 2.0   | 22   | 1.3813          | 0.3077   |
| 1.366         | 3.0   | 33   | 1.3663          | 0.3077   |
| 1.348         | 4.0   | 44   | 1.3393          | 0.4103   |
| 1.3034        | 5.0   | 55   | 1.2699          | 0.5641   |
| 1.2227        | 6.0   | 66   | 1.1615          | 0.6154   |
| 1.0912        | 7.0   | 77   | 1.1262          | 0.6154   |
| 0.9553        | 8.0   | 88   | 1.1313          | 0.5897   |
| 0.8801        | 9.0   | 99   | 1.1711          | 0.6667   |
| 0.8017        | 10.0  | 110  | 1.0136          | 0.6667   |
| 0.7451        | 11.0  | 121  | 0.9310          | 0.6923   |
| 0.6817        | 12.0  | 132  | 0.8635          | 0.6667   |
| 0.6579        | 13.0  | 143  | 1.1545          | 0.6667   |
| 0.6357        | 14.0  | 154  | 0.9239          | 0.6154   |
| 0.6006        | 15.0  | 165  | 1.0271          | 0.6667   |
| 0.5551        | 16.0  | 176  | 1.1781          | 0.5897   |
| 0.5619        | 17.0  | 187  | 1.1831          | 0.6923   |
| 0.5359        | 18.0  | 198  | 0.9667          | 0.6667   |
| 0.5247        | 19.0  | 209  | 1.1237          | 0.6667   |
| 0.5134        | 20.0  | 220  | 1.1176          | 0.6410   |
| 0.4469        | 21.0  | 231  | 0.9955          | 0.7179   |
| 0.4908        | 22.0  | 242  | 1.1411          | 0.7179   |
| 0.4112        | 23.0  | 253  | 1.2766          | 0.6410   |
| 0.4225        | 24.0  | 264  | 1.1135          | 0.6923   |
| 0.4786        | 25.0  | 275  | 1.2243          | 0.7179   |
| 0.3908        | 26.0  | 286  | 1.1587          | 0.7179   |
| 0.4706        | 27.0  | 297  | 1.2236          | 0.6923   |
| 0.502         | 28.0  | 308  | 1.1733          | 0.7179   |
| 0.4514        | 29.0  | 319  | 1.0931          | 0.7436   |
| 0.4386        | 30.0  | 330  | 1.2536          | 0.6667   |


### Framework versions

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

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