| --- |
| library_name: transformers |
| license: bsd-3-clause |
| base_model: MIT/ast-finetuned-audioset-10-10-0.4593 |
| tags: |
| - generated_from_trainer |
| datasets: |
| - audiofolder |
| metrics: |
| - accuracy |
| model-index: |
| - name: ast-finetuned-audioset-10-10-0.4593-heart-sounds |
| results: |
| - task: |
| name: Audio Classification |
| type: audio-classification |
| dataset: |
| name: audiofolder |
| type: audiofolder |
| config: default |
| split: validation |
| args: default |
| metrics: |
| - name: Accuracy |
| type: accuracy |
| value: 0.9695 |
| --- |
| |
| <!-- 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. --> |
|
|
| [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/vldmrl-org/HeartDiseaseDetector/runs/n9fzp3eh) |
| # ast-finetuned-audioset-10-10-0.4593-heart-sounds |
|
|
| This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the audiofolder dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.1499 |
| - Accuracy: 0.9695 |
|
|
| ## 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: 1e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 16 |
| - seed: 42 |
| - gradient_accumulation_steps: 4 |
| - total_train_batch_size: 64 |
| - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_ratio: 0.1 |
| - num_epochs: 8 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| |:-------------:|:------:|:----:|:---------------:|:--------:| |
| | 0.2135 | 1.0 | 83 | 0.1724 | 0.9390 | |
| | 0.1655 | 2.0 | 166 | 0.2276 | 0.8918 | |
| | 0.0959 | 3.0 | 249 | 0.1166 | 0.9527 | |
| | 0.04 | 4.0 | 332 | 0.1106 | 0.9634 | |
| | 0.0142 | 5.0 | 415 | 0.1317 | 0.9634 | |
| | 0.0186 | 6.0 | 498 | 0.1424 | 0.9665 | |
| | 0.0022 | 7.0 | 581 | 0.1433 | 0.9695 | |
| | 0.0009 | 7.9119 | 656 | 0.1499 | 0.9680 | |
|
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|
|
| ### Framework versions |
|
|
| - Transformers 4.49.0 |
| - Pytorch 2.0.1+cu118 |
| - Datasets 3.3.2 |
| - Tokenizers 0.21.0 |
|
|
