---
library_name: transformers
license: cc-by-nc-4.0
base_model: facebook/hiera-base-224-in1k-hf
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: hiera-finetuned-stroke-binary-ultrasound
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# hiera-finetuned-stroke-binary-ultrasound

This model is a fine-tuned version of [facebook/hiera-base-224-in1k-hf](https://huggingface.co/facebook/hiera-base-224-in1k-hf) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0098
- Accuracy: 0.9951
- F1: 0.9951
- Precision: 0.9951
- Recall: 0.9951

## 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: 12
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.038         | 0.8753  | 100  | 0.0184          | 0.9938   | 0.9938 | 0.9939    | 0.9938 |
| 0.0468        | 1.7440  | 200  | 0.0206          | 0.9926   | 0.9926 | 0.9927    | 0.9926 |
| 0.0445        | 2.6127  | 300  | 0.0225          | 0.9901   | 0.9901 | 0.9902    | 0.9901 |
| 0.0415        | 3.4814  | 400  | 0.0187          | 0.9889   | 0.9889 | 0.9889    | 0.9889 |
| 0.0465        | 4.3501  | 500  | 0.0098          | 0.9951   | 0.9951 | 0.9951    | 0.9951 |
| 0.0397        | 5.2188  | 600  | 0.0286          | 0.9901   | 0.9901 | 0.9903    | 0.9901 |
| 0.0257        | 6.0875  | 700  | 0.0188          | 0.9926   | 0.9926 | 0.9927    | 0.9926 |
| 0.0434        | 6.9628  | 800  | 0.0209          | 0.9938   | 0.9938 | 0.9939    | 0.9938 |
| 0.0261        | 7.8315  | 900  | 0.0154          | 0.9926   | 0.9926 | 0.9926    | 0.9926 |
| 0.0198        | 8.7002  | 1000 | 0.0094          | 0.9951   | 0.9951 | 0.9951    | 0.9951 |
| 0.0207        | 9.5689  | 1100 | 0.0122          | 0.9938   | 0.9938 | 0.9939    | 0.9938 |
| 0.0157        | 10.4376 | 1200 | 0.0101          | 0.9951   | 0.9951 | 0.9951    | 0.9951 |
| 0.0188        | 11.3063 | 1300 | 0.0104          | 0.9951   | 0.9951 | 0.9951    | 0.9951 |


### Framework versions

- Transformers 4.53.1
- Pytorch 2.7.1+cu126
- Datasets 3.6.0
- Tokenizers 0.21.2