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# resnet-50-finetuned-cifar10

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: 1.9060
- Accuracy: 0.5076

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.3058        | 0.03  | 10   | 2.3106          | 0.0794   |
| 2.3033        | 0.06  | 20   | 2.3026          | 0.0892   |
| 2.3012        | 0.09  | 30   | 2.2971          | 0.1042   |
| 2.2914        | 0.11  | 40   | 2.2890          | 0.1254   |
| 2.2869        | 0.14  | 50   | 2.2816          | 0.16     |
| 2.2785        | 0.17  | 60   | 2.2700          | 0.1902   |
| 2.2712        | 0.2   | 70   | 2.2602          | 0.2354   |
| 2.2619        | 0.23  | 80   | 2.2501          | 0.2688   |
| 2.2509        | 0.26  | 90   | 2.2383          | 0.3022   |
| 2.2382        | 0.28  | 100  | 2.2229          | 0.3268   |
| 2.2255        | 0.31  | 110  | 2.2084          | 0.353    |
| 2.2164        | 0.34  | 120  | 2.1939          | 0.3608   |
| 2.2028        | 0.37  | 130  | 2.1829          | 0.3668   |
| 2.1977        | 0.4   | 140  | 2.1646          | 0.401    |
| 2.1844        | 0.43  | 150  | 2.1441          | 0.4244   |
| 2.1689        | 0.45  | 160  | 2.1323          | 0.437    |
| 2.1555        | 0.48  | 170  | 2.1159          | 0.4462   |
| 2.1448        | 0.51  | 180  | 2.0992          | 0.45     |
| 2.1313        | 0.54  | 190  | 2.0810          | 0.4642   |
| 2.1189        | 0.57  | 200  | 2.0589          | 0.4708   |
| 2.1111        | 0.6   | 210  | 2.0430          | 0.4828   |
| 2.0905        | 0.63  | 220  | 2.0288          | 0.4938   |
| 2.082         | 0.65  | 230  | 2.0089          | 0.4938   |
| 2.0646        | 0.68  | 240  | 1.9970          | 0.5014   |
| 2.0636        | 0.71  | 250  | 1.9778          | 0.4946   |
| 2.0579        | 0.74  | 260  | 1.9609          | 0.49     |
| 2.028         | 0.77  | 270  | 1.9602          | 0.4862   |
| 2.0447        | 0.8   | 280  | 1.9460          | 0.4934   |
| 2.0168        | 0.82  | 290  | 1.9369          | 0.505    |
| 2.0126        | 0.85  | 300  | 1.9317          | 0.4926   |
| 2.0099        | 0.88  | 310  | 1.9235          | 0.4952   |
| 1.9978        | 0.91  | 320  | 1.9174          | 0.4972   |
| 1.9951        | 0.94  | 330  | 1.9119          | 0.507    |
| 1.9823        | 0.97  | 340  | 1.9120          | 0.4992   |
| 1.985         | 1.0   | 350  | 1.9064          | 0.5022   |


### Framework versions

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

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