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# resnet-18

This model was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9476
- Accuracy: 0.6473

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.472         | 1.0   | 252  | 1.3291          | 0.4887   |
| 1.2941        | 2.0   | 505  | 1.1145          | 0.5793   |
| 1.2117        | 3.0   | 757  | 1.0483          | 0.6043   |
| 1.1616        | 4.0   | 1010 | 1.0137          | 0.6233   |
| 1.1654        | 5.0   | 1262 | 0.9975          | 0.6291   |
| 1.1297        | 6.0   | 1515 | 0.9766          | 0.6414   |
| 1.0645        | 7.0   | 1767 | 0.9668          | 0.6372   |
| 1.0692        | 8.0   | 2020 | 0.9603          | 0.6450   |
| 1.0711        | 9.0   | 2272 | 0.9521          | 0.6425   |
| 1.0344        | 9.98  | 2520 | 0.9476          | 0.6473   |


### Framework versions

- Transformers 4.30.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.13.3

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