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metadata
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: []

hiera-finetuned-stroke-binary-ultrasound

This model is a fine-tuned version of 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