
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# resnet-50-finetuned-omars6

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: 0.4990
- Accuracy: 0.8328

## 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: 5e-05
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3594        | 0.99  | 92   | 1.3630          | 0.5015   |
| 1.3214        | 2.0   | 185  | 1.3252          | 0.5714   |
| 1.2633        | 2.99  | 277  | 1.2851          | 0.6140   |
| 1.2693        | 4.0   | 370  | 1.2385          | 0.6626   |
| 1.1902        | 4.99  | 462  | 1.1837          | 0.6991   |
| 1.1421        | 6.0   | 555  | 1.1255          | 0.7568   |
| 1.1979        | 6.99  | 647  | 1.0094          | 0.8024   |
| 0.9431        | 8.0   | 740  | 0.9544          | 0.8237   |
| 0.9627        | 8.99  | 832  | 0.8864          | 0.8267   |
| 0.8556        | 10.0  | 925  | 0.8365          | 0.8328   |
| 0.7792        | 10.99 | 1017 | 0.7762          | 0.8359   |
| 0.7941        | 12.0  | 1110 | 0.7467          | 0.8359   |
| 0.8361        | 12.99 | 1202 | 0.7345          | 0.8237   |
| 0.7757        | 14.0  | 1295 | 0.7228          | 0.8146   |
| 0.6977        | 14.99 | 1387 | 0.6923          | 0.8267   |
| 0.6874        | 16.0  | 1480 | 0.6540          | 0.8146   |
| 0.6887        | 16.99 | 1572 | 0.6276          | 0.8298   |
| 0.7204        | 18.0  | 1665 | 0.5989          | 0.8267   |
| 0.8334        | 18.99 | 1757 | 0.6027          | 0.8237   |
| 0.7654        | 20.0  | 1850 | 0.5699          | 0.8511   |
| 0.7628        | 20.99 | 1942 | 0.5465          | 0.8389   |
| 0.7874        | 22.0  | 2035 | 0.5621          | 0.8298   |
| 0.8149        | 22.99 | 2127 | 0.5474          | 0.8298   |
| 0.7565        | 24.0  | 2220 | 0.5388          | 0.8480   |
| 0.7241        | 24.99 | 2312 | 0.5351          | 0.8267   |
| 0.7894        | 26.0  | 2405 | 0.5327          | 0.8389   |
| 0.7664        | 26.99 | 2497 | 0.5065          | 0.8450   |
| 0.6655        | 28.0  | 2590 | 0.5309          | 0.8359   |
| 0.607         | 28.99 | 2682 | 0.5061          | 0.8541   |
| 0.6462        | 29.84 | 2760 | 0.4990          | 0.8328   |


### Framework versions

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

depthread depthunread widthread widthunread