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--- |
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license: bsd-3-clause |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: ast_20-finetuned-ICBHI |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# ast_20-finetuned-ICBHI |
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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 None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.3414 |
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- Accuracy: 0.6942 |
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- Sensitivity: 0.5361 |
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- Specificity: 0.8354 |
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- Score: 0.6857 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 16 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Sensitivity | Specificity | Score | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:-----------:|:------:| |
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| 0.895 | 1.0 | 345 | 0.9921 | 0.6029 | 0.2458 | 0.9218 | 0.5838 | |
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| 0.8244 | 2.0 | 690 | 0.8522 | 0.6717 | 0.4147 | 0.9012 | 0.6580 | |
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| 0.7002 | 3.0 | 1035 | 0.8378 | 0.6775 | 0.5346 | 0.8052 | 0.6699 | |
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| 0.415 | 4.0 | 1380 | 1.0645 | 0.6674 | 0.5392 | 0.7819 | 0.6605 | |
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| 0.19 | 5.0 | 1725 | 1.3827 | 0.6732 | 0.5207 | 0.8093 | 0.6650 | |
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| 0.0465 | 6.0 | 2070 | 1.7785 | 0.6754 | 0.5346 | 0.8011 | 0.6678 | |
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| 0.0078 | 7.0 | 2415 | 2.0612 | 0.6819 | 0.6252 | 0.7325 | 0.6789 | |
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| 0.0003 | 8.0 | 2760 | 2.2956 | 0.6971 | 0.5376 | 0.8395 | 0.6886 | |
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| 0.0001 | 9.0 | 3105 | 2.3499 | 0.6942 | 0.5192 | 0.8505 | 0.6848 | |
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| 0.0001 | 10.0 | 3450 | 2.3414 | 0.6942 | 0.5361 | 0.8354 | 0.6857 | |
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### Framework versions |
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- Transformers 4.29.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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