

# dino-vitb8-finetuned-stroke-binary

This model is a fine-tuned version of [facebook/dino-vitb8](https://huggingface.co/facebook/dino-vitb8) on an - BTX24/tekno21-brain-stroke-dataset-binary
 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1127
- Accuracy: 0.9597
- F1: 0.9595
- Precision: 0.9602
- Recall: 0.9597

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 36
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.7965        | 0.6202  | 100  | 0.8312          | 0.5382   | 0.5058 | 0.4968    | 0.5382 |
| 0.6839        | 1.2357  | 200  | 0.6796          | 0.6246   | 0.5750 | 0.5991    | 0.6246 |
| 0.5662        | 1.8558  | 300  | 0.5344          | 0.7318   | 0.7119 | 0.7377    | 0.7318 |
| 0.4408        | 2.4713  | 400  | 0.4082          | 0.8123   | 0.8082 | 0.8120    | 0.8123 |
| 0.3611        | 3.0868  | 500  | 0.3335          | 0.8602   | 0.8597 | 0.8596    | 0.8602 |
| 0.3121        | 3.7070  | 600  | 0.2746          | 0.8860   | 0.8832 | 0.8914    | 0.8860 |
| 0.2614        | 4.3225  | 700  | 0.2299          | 0.9050   | 0.9040 | 0.9058    | 0.9050 |
| 0.242         | 4.9426  | 800  | 0.2103          | 0.9177   | 0.9178 | 0.9179    | 0.9177 |
| 0.2239        | 5.5581  | 900  | 0.2298          | 0.9082   | 0.9090 | 0.9136    | 0.9082 |
| 0.1979        | 6.1736  | 1000 | 0.2059          | 0.9209   | 0.9197 | 0.9230    | 0.9209 |
| 0.2082        | 6.7938  | 1100 | 0.1779          | 0.9263   | 0.9261 | 0.9261    | 0.9263 |
| 0.1723        | 7.4093  | 1200 | 0.1693          | 0.9308   | 0.9302 | 0.9315    | 0.9308 |
| 0.1877        | 8.0248  | 1300 | 0.1681          | 0.9380   | 0.9382 | 0.9385    | 0.9380 |
| 0.2           | 8.6450  | 1400 | 0.1482          | 0.9403   | 0.9402 | 0.9402    | 0.9403 |
| 0.1642        | 9.2605  | 1500 | 0.1637          | 0.9331   | 0.9322 | 0.9352    | 0.9331 |
| 0.1525        | 9.8806  | 1600 | 0.1494          | 0.9421   | 0.9417 | 0.9425    | 0.9421 |
| 0.158         | 10.4961 | 1700 | 0.1403          | 0.9484   | 0.9480 | 0.9495    | 0.9484 |
| 0.1327        | 11.1116 | 1800 | 0.1329          | 0.9498   | 0.9498 | 0.9498    | 0.9498 |
| 0.1465        | 11.7318 | 1900 | 0.1233          | 0.9525   | 0.9524 | 0.9525    | 0.9525 |
| 0.1311        | 12.3473 | 2000 | 0.1280          | 0.9521   | 0.9520 | 0.9520    | 0.9521 |
| 0.129         | 12.9674 | 2100 | 0.1173          | 0.9557   | 0.9556 | 0.9556    | 0.9557 |
| 0.1425        | 13.5829 | 2200 | 0.1190          | 0.9552   | 0.9552 | 0.9552    | 0.9552 |
| 0.1256        | 14.1984 | 2300 | 0.1225          | 0.9566   | 0.9563 | 0.9570    | 0.9566 |
| 0.1461        | 14.8186 | 2400 | 0.1171          | 0.9588   | 0.9588 | 0.9588    | 0.9588 |
| 0.133         | 15.4341 | 2500 | 0.1165          | 0.9548   | 0.9546 | 0.9549    | 0.9548 |
| 0.1258        | 16.0496 | 2600 | 0.1302          | 0.9480   | 0.9474 | 0.9500    | 0.9480 |
| 0.115         | 16.6698 | 2700 | 0.1320          | 0.9534   | 0.9537 | 0.9552    | 0.9534 |
| 0.1134        | 17.2853 | 2800 | 0.1171          | 0.9552   | 0.9549 | 0.9562    | 0.9552 |
| 0.1069        | 17.9054 | 2900 | 0.1127          | 0.9597   | 0.9595 | 0.9602    | 0.9597 |


### Framework versions

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


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