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# resnet-50-finetuned-cifar10-finetuned-cifar10

This model was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7363
- Accuracy: 0.561

## 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: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9934        | 0.14  | 10   | 1.8738          | 0.517    |
| 1.9493        | 0.28  | 20   | 1.8358          | 0.532    |
| 1.9328        | 0.43  | 30   | 1.7941          | 0.515    |
| 1.9175        | 0.57  | 40   | 1.7683          | 0.531    |
| 1.8875        | 0.71  | 50   | 1.7649          | 0.545    |
| 1.8752        | 0.85  | 60   | 1.7309          | 0.559    |
| 1.8881        | 0.99  | 70   | 1.7363          | 0.561    |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.1
- Datasets 2.14.6
- Tokenizers 0.14.1

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