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# finetuned-breast_cancer_images

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Accuracy: 0.9025
- Train Loss: 0.0125
- Accuracy: 0.9589
- Loss: 0.0175

## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 103

### Training results

| Training Loss | Epoch  | Step | Accuracy | Loss   | Validation Loss |
|:-------------:|:------:|:----:|:--------:|:------:|:---------------:|
| No log        | 5.06   | 200  | 0.9114   | 0.0241 | 0.0172          |
| No log        | 5.06   | 200  | 0.9114   | 0.0241 | 0.0172          |
| No log        | 10.13  | 400  | 0.9335   | 0.0209 | 0.0157          |
| No log        | 10.13  | 400  | 0.9335   | 0.0209 | 0.0157          |
| No log        | 15.19  | 600  | 0.9241   | 0.0189 | 0.0172          |
| No log        | 15.19  | 600  | 0.9241   | 0.0189 | 0.0172          |
| No log        | 20.25  | 800  | 0.9430   | 0.0182 | 0.0161          |
| No log        | 20.25  | 800  | 0.9430   | 0.0182 | 0.0161          |
| No log        | 25.32  | 1000 | 0.9399   | 0.0165 | 0.0186          |
| No log        | 25.32  | 1000 | 0.9399   | 0.0165 | 0.0186          |
| No log        | 30.38  | 1200 | 0.9557   | 0.0155 | 0.0137          |
| No log        | 30.38  | 1200 | 0.9557   | 0.0155 | 0.0137          |
| No log        | 35.44  | 1400 | 0.9335   | 0.0136 | 0.0180          |
| No log        | 35.44  | 1400 | 0.9335   | 0.0136 | 0.0180          |
| No log        | 40.51  | 1600 | 0.9525   | 0.0152 | 0.0139          |
| No log        | 40.51  | 1600 | 0.9525   | 0.0152 | 0.0139          |
| No log        | 45.57  | 1800 | 0.9494   | 0.0144 | 0.0149          |
| No log        | 45.57  | 1800 | 0.9494   | 0.0144 | 0.0149          |
| No log        | 50.63  | 2000 | 0.9430   | 0.0153 | 0.0187          |
| No log        | 50.63  | 2000 | 0.9430   | 0.0153 | 0.0187          |
| No log        | 55.7   | 2200 | 0.9557   | 0.0132 | 0.0149          |
| No log        | 55.7   | 2200 | 0.9557   | 0.0132 | 0.0149          |
| No log        | 60.76  | 2400 | 0.9430   | 0.0124 | 0.0149          |
| No log        | 60.76  | 2400 | 0.9430   | 0.0124 | 0.0149          |
| No log        | 65.82  | 2600 | 0.9525   | 0.0134 | 0.0164          |
| No log        | 65.82  | 2600 | 0.9525   | 0.0134 | 0.0164          |
| No log        | 70.89  | 2800 | 0.9557   | 0.0117 | 0.0143          |
| No log        | 70.89  | 2800 | 0.9557   | 0.0117 | 0.0143          |
| No log        | 75.95  | 3000 | 0.9557   | 0.0112 | 0.0166          |
| No log        | 75.95  | 3000 | 0.9557   | 0.0112 | 0.0166          |
| No log        | 81.01  | 3200 | 0.9589   | 0.0118 | 0.0163          |
| No log        | 81.01  | 3200 | 0.9589   | 0.0118 | 0.0163          |
| No log        | 86.08  | 3400 | 0.9494   | 0.0106 | 0.0188          |
| No log        | 86.08  | 3400 | 0.9494   | 0.0106 | 0.0188          |
| No log        | 91.14  | 3600 | 0.9462   | 0.0121 | 0.0186          |
| No log        | 91.14  | 3600 | 0.9462   | 0.0121 | 0.0186          |
| No log        | 96.2   | 3800 | 0.9525   | 0.0129 | 0.0171          |
| No log        | 96.2   | 3800 | 0.9525   | 0.0129 | 0.0171          |
| No log        | 101.27 | 4000 | 0.9589   | 0.0125 | 0.0175          |
| No log        | 101.27 | 4000 | 0.9589   | 0.0125 | 0.0175          |


### Framework versions

- Transformers 4.30.0
- Pytorch 2.3.0+cu121
- Tokenizers 0.13.3

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