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---
library_name: transformers
license: apache-2.0
base_model: microsoft/beit-base-patch16-224-pt22k-ft22k
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: beit-base-patch16-224-pt22k-ft22k-finetuned-stroke-binary
results: []
datasets:
- BTX24/tekno21-brain-stroke-dataset-binary
pipeline_tag: image-classification
---
# 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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