hiera-finetuned-stroke-multi-ultrasound-combined
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3436
- Accuracy: 0.8661
- F1: 0.8650
- Precision: 0.8650
- Recall: 0.8661
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 |
|---|
| 0.5712 | 1.7964 | 100 | 0.4956 | 0.7923 | 0.7933 | 0.7973 | 0.7923 |
| 0.4495 | 3.5792 | 200 | 0.4193 | 0.8309 | 0.8315 | 0.8325 | 0.8309 |
| 0.4266 | 5.3620 | 300 | 0.4364 | 0.8150 | 0.8091 | 0.8221 | 0.8150 |
| 0.3375 | 7.1448 | 400 | 0.3775 | 0.8513 | 0.8473 | 0.8535 | 0.8513 |
| 0.2979 | 8.9412 | 500 | 0.3360 | 0.8627 | 0.8617 | 0.8614 | 0.8627 |
| 0.2703 | 10.7240 | 600 | 0.3436 | 0.8661 | 0.8650 | 0.8650 | 0.8661 |
Framework versions
- Transformers 4.53.1
- Pytorch 2.7.1+cu126
- Datasets 3.6.0
- Tokenizers 0.21.2
