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# resnet-18-finetuned-papsmear

This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2838
- Accuracy: 0.9118

## 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: 50

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log        | 0.9231  | 9    | 1.9256          | 0.1691   |
| 1.9692        | 1.9487  | 19   | 1.6557          | 0.2868   |
| 1.7979        | 2.9744  | 29   | 1.3300          | 0.5368   |
| 1.5079        | 4.0     | 39   | 1.0482          | 0.6324   |
| 1.217         | 4.9231  | 48   | 0.9019          | 0.6618   |
| 0.9536        | 5.9487  | 58   | 0.7687          | 0.6691   |
| 0.7881        | 6.9744  | 68   | 0.6150          | 0.7721   |
| 0.68          | 8.0     | 78   | 0.5481          | 0.7868   |
| 0.5678        | 8.9231  | 87   | 0.5341          | 0.7868   |
| 0.5169        | 9.9487  | 97   | 0.4800          | 0.7941   |
| 0.4838        | 10.9744 | 107  | 0.4356          | 0.8235   |
| 0.4738        | 12.0    | 117  | 0.4573          | 0.8162   |
| 0.3798        | 12.9231 | 126  | 0.4263          | 0.8088   |
| 0.3431        | 13.9487 | 136  | 0.4159          | 0.8382   |
| 0.3282        | 14.9744 | 146  | 0.3787          | 0.8603   |
| 0.3167        | 16.0    | 156  | 0.4234          | 0.8382   |
| 0.3186        | 16.9231 | 165  | 0.3853          | 0.8235   |
| 0.2568        | 17.9487 | 175  | 0.3904          | 0.8456   |
| 0.2528        | 18.9744 | 185  | 0.4013          | 0.8309   |
| 0.2661        | 20.0    | 195  | 0.3275          | 0.8824   |
| 0.2287        | 20.9231 | 204  | 0.3219          | 0.8824   |
| 0.2465        | 21.9487 | 214  | 0.3410          | 0.8529   |
| 0.2422        | 22.9744 | 224  | 0.3256          | 0.8603   |
| 0.222         | 24.0    | 234  | 0.3232          | 0.875    |
| 0.1917        | 24.9231 | 243  | 0.3307          | 0.8676   |
| 0.194         | 25.9487 | 253  | 0.3146          | 0.8971   |
| 0.212         | 26.9744 | 263  | 0.3125          | 0.8897   |
| 0.1718        | 28.0    | 273  | 0.3015          | 0.9044   |
| 0.1975        | 28.9231 | 282  | 0.3195          | 0.8824   |
| 0.1948        | 29.9487 | 292  | 0.3536          | 0.8971   |
| 0.1809        | 30.9744 | 302  | 0.3105          | 0.875    |
| 0.1744        | 32.0    | 312  | 0.3032          | 0.8824   |
| 0.1731        | 32.9231 | 321  | 0.2936          | 0.8971   |
| 0.1513        | 33.9487 | 331  | 0.2889          | 0.8824   |
| 0.1527        | 34.9744 | 341  | 0.2875          | 0.8897   |
| 0.1693        | 36.0    | 351  | 0.2754          | 0.8897   |
| 0.1743        | 36.9231 | 360  | 0.2875          | 0.8971   |
| 0.1463        | 37.9487 | 370  | 0.2961          | 0.8971   |
| 0.1429        | 38.9744 | 380  | 0.2848          | 0.8971   |
| 0.1483        | 40.0    | 390  | 0.2873          | 0.8897   |
| 0.1483        | 40.9231 | 399  | 0.2856          | 0.875    |
| 0.1613        | 41.9487 | 409  | 0.2801          | 0.8971   |
| 0.1358        | 42.9744 | 419  | 0.2838          | 0.9118   |
| 0.1453        | 44.0    | 429  | 0.2783          | 0.8971   |
| 0.1383        | 44.9231 | 438  | 0.2897          | 0.8897   |
| 0.1655        | 45.9487 | 448  | 0.2847          | 0.9044   |
| 0.1489        | 46.1538 | 450  | 0.2861          | 0.8897   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1

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