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# mobilenet_v2_1.0_224-finetuned-papsmear

This model is a fine-tuned version of [google/mobilenet_v2_1.0_224](https://huggingface.co/google/mobilenet_v2_1.0_224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4698
- Accuracy: 0.8676

## 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: 60

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.7932        | 0.9935  | 38   | 1.7607          | 0.25     |
| 1.6542        | 1.9869  | 76   | 1.5736          | 0.3971   |
| 1.4692        | 2.9804  | 114  | 1.4805          | 0.3676   |
| 1.2759        | 4.0     | 153  | 1.2177          | 0.5809   |
| 1.1521        | 4.9935  | 191  | 1.0727          | 0.6471   |
| 1.078         | 5.9869  | 229  | 0.9996          | 0.6176   |
| 1.0235        | 6.9804  | 267  | 0.8680          | 0.7059   |
| 0.9554        | 8.0     | 306  | 0.9273          | 0.6397   |
| 0.7437        | 8.9935  | 344  | 0.7389          | 0.7059   |
| 0.7876        | 9.9869  | 382  | 0.6774          | 0.7426   |
| 0.7698        | 10.9804 | 420  | 0.6569          | 0.7206   |
| 0.7597        | 12.0    | 459  | 0.6758          | 0.7574   |
| 0.6114        | 12.9935 | 497  | 0.8279          | 0.7132   |
| 0.6847        | 13.9869 | 535  | 0.7505          | 0.7132   |
| 0.5902        | 14.9804 | 573  | 0.7919          | 0.6691   |
| 0.629         | 16.0    | 612  | 0.6117          | 0.7868   |
| 0.5071        | 16.9935 | 650  | 0.6048          | 0.7353   |
| 0.5453        | 17.9869 | 688  | 0.8086          | 0.7279   |
| 0.5071        | 18.9804 | 726  | 0.7835          | 0.7059   |
| 0.5328        | 20.0    | 765  | 0.6139          | 0.75     |
| 0.5053        | 20.9935 | 803  | 0.5981          | 0.7868   |
| 0.4436        | 21.9869 | 841  | 0.5219          | 0.8015   |
| 0.5025        | 22.9804 | 879  | 0.4959          | 0.8088   |
| 0.4984        | 24.0    | 918  | 0.5701          | 0.7794   |
| 0.4655        | 24.9935 | 956  | 0.7179          | 0.7206   |
| 0.3848        | 25.9869 | 994  | 0.5075          | 0.8088   |
| 0.3824        | 26.9804 | 1032 | 0.6645          | 0.7426   |
| 0.4901        | 28.0    | 1071 | 0.7288          | 0.6985   |
| 0.397         | 28.9935 | 1109 | 0.7251          | 0.7279   |
| 0.3818        | 29.9869 | 1147 | 0.6250          | 0.7941   |
| 0.3412        | 30.9804 | 1185 | 0.7065          | 0.7279   |
| 0.3627        | 32.0    | 1224 | 0.6877          | 0.7426   |
| 0.3557        | 32.9935 | 1262 | 0.4245          | 0.8529   |
| 0.441         | 33.9869 | 1300 | 0.6974          | 0.75     |
| 0.3036        | 34.9804 | 1338 | 0.6458          | 0.7426   |
| 0.3213        | 36.0    | 1377 | 0.5579          | 0.7941   |
| 0.402         | 36.9935 | 1415 | 0.4578          | 0.8382   |
| 0.2897        | 37.9869 | 1453 | 0.5369          | 0.7868   |
| 0.348         | 38.9804 | 1491 | 0.6819          | 0.7941   |
| 0.3929        | 40.0    | 1530 | 0.5810          | 0.7868   |
| 0.3173        | 40.9935 | 1568 | 0.7875          | 0.7426   |
| 0.3499        | 41.9869 | 1606 | 0.5051          | 0.8015   |
| 0.3053        | 42.9804 | 1644 | 0.7510          | 0.7426   |
| 0.4109        | 44.0    | 1683 | 0.6529          | 0.75     |
| 0.3846        | 44.9935 | 1721 | 0.9615          | 0.7132   |
| 0.3222        | 45.9869 | 1759 | 0.8889          | 0.6691   |
| 0.3293        | 46.9804 | 1797 | 0.4698          | 0.8676   |
| 0.293         | 48.0    | 1836 | 0.5996          | 0.8015   |
| 0.2363        | 48.9935 | 1874 | 0.5007          | 0.8309   |
| 0.2811        | 49.9869 | 1912 | 0.6748          | 0.7941   |
| 0.2403        | 50.9804 | 1950 | 0.6595          | 0.7941   |
| 0.2553        | 52.0    | 1989 | 0.5987          | 0.7794   |
| 0.2959        | 52.9935 | 2027 | 0.5459          | 0.8235   |
| 0.3066        | 53.9869 | 2065 | 0.6198          | 0.7868   |
| 0.2981        | 54.9804 | 2103 | 0.4886          | 0.8309   |
| 0.2658        | 56.0    | 2142 | 0.6422          | 0.7794   |
| 0.2371        | 56.9935 | 2180 | 0.5000          | 0.8382   |
| 0.2331        | 57.9869 | 2218 | 0.8854          | 0.7132   |
| 0.2777        | 58.9804 | 2256 | 0.6190          | 0.8015   |
| 0.3047        | 59.6078 | 2280 | 0.6048          | 0.7647   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1

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