
<!-- 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-omar

This model is a fine-tuned version of microsoft/resnet-50 on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2645
- Accuracy: 0.9144

## 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: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0695        | 1.0   | 111  | 1.0576          | 0.5315   |
| 0.971         | 2.0   | 223  | 0.9366          | 0.5416   |
| 0.8121        | 3.0   | 334  | 0.7493          | 0.7103   |
| 0.6861        | 4.0   | 446  | 0.5625          | 0.8363   |
| 0.606         | 5.0   | 557  | 0.4239          | 0.8816   |
| 0.5001        | 6.0   | 669  | 0.3159          | 0.9219   |
| 0.4704        | 7.0   | 780  | 0.3254          | 0.9118   |
| 0.4332        | 8.0   | 892  | 0.2808          | 0.9194   |
| 0.4432        | 9.0   | 1003 | 0.2854          | 0.9219   |
| 0.4768        | 10.0  | 1115 | 0.2782          | 0.9219   |
| 0.4432        | 11.0  | 1226 | 0.2768          | 0.9320   |
| 0.4752        | 12.0  | 1338 | 0.2744          | 0.9219   |
| 0.489         | 13.0  | 1449 | 0.2693          | 0.9194   |
| 0.3743        | 14.0  | 1561 | 0.2715          | 0.9270   |
| 0.417         | 14.93 | 1665 | 0.2645          | 0.9144   |


### Framework versions

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

depth-accepted not-accepted width-accepted