wa976 / ast_9-finetuned-ICBHI

huggingface.co
Total runs: 164
24-hour runs: 0
7-day runs: -1
30-day runs: 157
Model's Last Updated: Mai 16 2023
audio-classification

Introduction of ast_9-finetuned-ICBHI

Model Details of ast_9-finetuned-ICBHI

ast_9-finetuned-ICBHI

This model is a fine-tuned version of MIT/ast-finetuned-audioset-10-10-0.4593 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.8548
  • Accuracy: 0.6572
  • Sensitivity: 0.4183
  • Specificity: 0.8710
  • Score: 0.6446
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: 3e-05
  • train_batch_size: 4
  • eval_batch_size: 4
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 5
Training results
Training Loss Epoch Step Validation Loss Accuracy Sensitivity Specificity Score
0.9335 1.0 258 0.9986 0.5986 0.5318 0.6582 0.5950
0.8392 2.0 517 0.8548 0.6572 0.4183 0.8710 0.6446
0.6187 3.0 776 0.8978 0.6554 0.4797 0.8126 0.6461
0.2052 4.0 1035 1.1767 0.6533 0.4536 0.8318 0.6427
0.1132 4.99 1290 1.4218 0.6525 0.5004 0.7886 0.6445
Framework versions
  • Transformers 4.29.1
  • Pytorch 2.0.0+cu118
  • Datasets 2.12.0
  • Tokenizers 0.13.3

Runs of wa976 ast_9-finetuned-ICBHI on huggingface.co

164
Total runs
0
24-hour runs
-1
3-day runs
-1
7-day runs
157
30-day runs

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