
# beit-base-patch16-224-pt22k-ft22k-finetuned-stroke-binary

This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on an "Binary Stroke Detection" dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2029
- Accuracy: 0.9222
- F1: 0.9214
- Precision: 0.9234
- Recall: 0.9222

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 48
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.7256        | 1.2477  | 100  | 0.6913          | 0.5685   | 0.4823 | 0.4731    | 0.5685 |
| 0.6695        | 2.4954  | 200  | 0.6480          | 0.6210   | 0.5164 | 0.5987    | 0.6210 |
| 0.5963        | 3.7430  | 300  | 0.5882          | 0.6725   | 0.6118 | 0.6993    | 0.6725 |
| 0.518         | 4.9907  | 400  | 0.4990          | 0.7481   | 0.7167 | 0.7891    | 0.7481 |
| 0.4325        | 6.2477  | 500  | 0.4090          | 0.8073   | 0.7957 | 0.8232    | 0.8073 |
| 0.3848        | 7.4954  | 600  | 0.3703          | 0.8340   | 0.8257 | 0.8482    | 0.8340 |
| 0.3532        | 8.7430  | 700  | 0.3958          | 0.8313   | 0.8201 | 0.8564    | 0.8313 |
| 0.3297        | 9.9907  | 800  | 0.3257          | 0.8611   | 0.8558 | 0.8718    | 0.8611 |
| 0.3281        | 11.2477 | 900  | 0.3169          | 0.8666   | 0.8612 | 0.8791    | 0.8666 |
| 0.2938        | 12.4954 | 1000 | 0.2814          | 0.8865   | 0.8841 | 0.8900    | 0.8865 |
| 0.2866        | 13.7430 | 1100 | 0.2828          | 0.8869   | 0.8837 | 0.8943    | 0.8869 |
| 0.2884        | 14.9907 | 1200 | 0.2929          | 0.8847   | 0.8810 | 0.8936    | 0.8847 |
| 0.2808        | 16.2477 | 1300 | 0.2458          | 0.9014   | 0.8999 | 0.9034    | 0.9014 |
| 0.258         | 17.4954 | 1400 | 0.2351          | 0.9091   | 0.9080 | 0.9102    | 0.9091 |
| 0.2744        | 18.7430 | 1500 | 0.2516          | 0.9014   | 0.8994 | 0.9057    | 0.9014 |
| 0.261         | 19.9907 | 1600 | 0.2453          | 0.9068   | 0.9050 | 0.9107    | 0.9068 |
| 0.2519        | 21.2477 | 1700 | 0.2564          | 0.8987   | 0.8961 | 0.9051    | 0.8987 |
| 0.2595        | 22.4954 | 1800 | 0.2318          | 0.9095   | 0.9079 | 0.9129    | 0.9095 |
| 0.2548        | 23.7430 | 1900 | 0.2196          | 0.9136   | 0.9128 | 0.9142    | 0.9136 |
| 0.2327        | 24.9907 | 2000 | 0.2376          | 0.9068   | 0.9050 | 0.9110    | 0.9068 |
| 0.2563        | 26.2477 | 2100 | 0.2421          | 0.9028   | 0.9005 | 0.9083    | 0.9028 |
| 0.2348        | 27.4954 | 2200 | 0.2213          | 0.9109   | 0.9095 | 0.9132    | 0.9109 |
| 0.2427        | 28.7430 | 2300 | 0.2308          | 0.9077   | 0.9060 | 0.9116    | 0.9077 |
| 0.2166        | 29.9907 | 2400 | 0.2152          | 0.9141   | 0.9128 | 0.9165    | 0.9141 |
| 0.2345        | 31.2477 | 2500 | 0.2283          | 0.9068   | 0.9049 | 0.9114    | 0.9068 |
| 0.2355        | 32.4954 | 2600 | 0.2173          | 0.9118   | 0.9103 | 0.9149    | 0.9118 |
| 0.2291        | 33.7430 | 2700 | 0.2149          | 0.9127   | 0.9113 | 0.9155    | 0.9127 |
| 0.2319        | 34.9907 | 2800 | 0.2123          | 0.9141   | 0.9127 | 0.9167    | 0.9141 |
| 0.222         | 36.2477 | 2900 | 0.2053          | 0.9181   | 0.9171 | 0.9197    | 0.9181 |
| 0.2235        | 37.4954 | 3000 | 0.2121          | 0.9141   | 0.9127 | 0.9166    | 0.9141 |
| 0.2221        | 38.7430 | 3100 | 0.2013          | 0.9195   | 0.9188 | 0.9200    | 0.9195 |
| 0.2262        | 39.9907 | 3200 | 0.2029          | 0.9222   | 0.9214 | 0.9234    | 0.9222 |
| 0.2171        | 41.2477 | 3300 | 0.2075          | 0.9181   | 0.9170 | 0.9202    | 0.9181 |
| 0.2268        | 42.4954 | 3400 | 0.2045          | 0.9190   | 0.9180 | 0.9208    | 0.9190 |
| 0.2222        | 43.7430 | 3500 | 0.2050          | 0.9204   | 0.9194 | 0.9222    | 0.9204 |
| 0.2169        | 44.9907 | 3600 | 0.2070          | 0.9177   | 0.9165 | 0.9197    | 0.9177 |
| 0.2245        | 46.2477 | 3700 | 0.2064          | 0.9181   | 0.9170 | 0.9201    | 0.9181 |
| 0.2148        | 47.4954 | 3800 | 0.2066          | 0.9181   | 0.9170 | 0.9201    | 0.9181 |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.6.0+cu124
- Datasets 3.4.0
- Tokenizers 0.21.0



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