File size: 4,416 Bytes
6c38bab e264463 6c38bab e264463 6c38bab 42c42ff 6c38bab 42c42ff 6c38bab 33b5487 e264463 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 |
---
library_name: transformers
license: apache-2.0
base_model: facebook/deit-base-patch16-224
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: deit-base-patch16-224-finetuned-stroke-binary
results: []
datasets:
- BTX24/tekno21-brain-stroke-dataset-binary
---
# deit-base-patch16-224-finetuned-stroke-binary
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on an BTX24/tekno21-brain-stroke-dataset-binary dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1527
- Accuracy: 0.9489
- F1: 0.9484
- Precision: 0.9505
- Recall: 0.9489
## 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: 48
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.1646 | 0.6202 | 100 | 0.1588 | 0.9430 | 0.9425 | 0.9442 | 0.9430 |
| 0.1417 | 1.2357 | 200 | 0.1640 | 0.9439 | 0.9433 | 0.9458 | 0.9439 |
| 0.1681 | 1.8558 | 300 | 0.1622 | 0.9453 | 0.9447 | 0.9470 | 0.9453 |
| 0.1512 | 2.4713 | 400 | 0.1510 | 0.9435 | 0.9430 | 0.9441 | 0.9435 |
| 0.1506 | 3.0868 | 500 | 0.1913 | 0.9340 | 0.9327 | 0.9391 | 0.9340 |
| 0.1654 | 3.7070 | 600 | 0.1679 | 0.9426 | 0.9419 | 0.9442 | 0.9426 |
| 0.1482 | 4.3225 | 700 | 0.1551 | 0.9403 | 0.9402 | 0.9402 | 0.9403 |
| 0.1599 | 4.9426 | 800 | 0.1489 | 0.9462 | 0.9457 | 0.9471 | 0.9462 |
| 0.1477 | 5.5581 | 900 | 0.1437 | 0.9426 | 0.9424 | 0.9425 | 0.9426 |
| 0.1308 | 6.1736 | 1000 | 0.1527 | 0.9417 | 0.9414 | 0.9416 | 0.9417 |
| 0.1362 | 6.7938 | 1100 | 0.1608 | 0.9426 | 0.9421 | 0.9432 | 0.9426 |
| 0.1494 | 7.4093 | 1200 | 0.1601 | 0.9435 | 0.9429 | 0.9451 | 0.9435 |
| 0.1592 | 8.0248 | 1300 | 0.1430 | 0.9430 | 0.9429 | 0.9429 | 0.9430 |
| 0.16 | 8.6450 | 1400 | 0.1504 | 0.9457 | 0.9451 | 0.9475 | 0.9457 |
| 0.1245 | 9.2605 | 1500 | 0.1506 | 0.9462 | 0.9458 | 0.9470 | 0.9462 |
| 0.1397 | 9.8806 | 1600 | 0.1971 | 0.9313 | 0.9300 | 0.9359 | 0.9313 |
| 0.1396 | 10.4961 | 1700 | 0.1527 | 0.9489 | 0.9484 | 0.9505 | 0.9489 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.6.0+cu124
- Datasets 3.4.0
- Tokenizers 0.21.0
 |