---
library_name: transformers
license: apache-2.0
base_model: timm/levit_128.fb_dist_in1k
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: levit_128.fb_dist_in1k-finetuned-stroke-binary
  results: []
datasets:
- BTX24/tekno21-brain-stroke-dataset-binary
pipeline_tag: image-classification
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# levit_128.fb_dist_in1k-finetuned-stroke-binary

This model is a fine-tuned version of [timm/levit_128.fb_dist_in1k](https://huggingface.co/timm/levit_128.fb_dist_in1k) on an binary stroke detection dataset.
It achieves the following results on the evaluation set:
- Accuracy: 0.8598
- F1: 0.8577
- Precision: 0.8602
- Recall: 0.8598

## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.7002        | 0.6202 | 100  | nan             | 0.5690   | 0.5387 | 0.5349    | 0.5690 |
| 0.681         | 1.2357 | 200  | nan             | 0.5834   | 0.5331 | 0.5372    | 0.5834 |
| 0.6874        | 1.8558 | 300  | nan             | 0.6002   | 0.5596 | 0.5665    | 0.6002 |
| 0.6774        | 2.4713 | 400  | nan             | 0.6124   | 0.5811 | 0.5867    | 0.6124 |
| 0.6533        | 3.0868 | 500  | nan             | 0.6852   | 0.6694 | 0.6767    | 0.6852 |
| 0.6368        | 3.7070 | 600  | nan             | 0.7205   | 0.7153 | 0.7153    | 0.7205 |
| 0.6196        | 4.3225 | 700  | nan             | 0.7603   | 0.7471 | 0.7650    | 0.7603 |
| 0.5663        | 4.9426 | 800  | nan             | 0.7883   | 0.7843 | 0.7864    | 0.7883 |
| 0.5196        | 5.5581 | 900  | nan             | 0.8078   | 0.7972 | 0.8206    | 0.8078 |
| 0.4704        | 6.1736 | 1000 | nan             | 0.8363   | 0.8317 | 0.8396    | 0.8363 |
| 0.4715        | 6.7938 | 1100 | nan             | 0.8349   | 0.8292 | 0.8409    | 0.8349 |
| 0.452         | 7.4093 | 1200 | nan             | 0.8503   | 0.8479 | 0.8505    | 0.8503 |
| 0.4538        | 8.0248 | 1300 | nan             | 0.8598   | 0.8577 | 0.8602    | 0.8598 |


### Framework versions

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