Model save

README.md

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+---
+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: 0.0104
+- 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

model.safetensors

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 size 203065440