Marco-Cheung / ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan

huggingface.co
Total runs: 164
24-hour runs: 0
7-day runs: 159
30-day runs: 155
Model's Last Updated: Juillet 31 2023
audio-classification

Introduction of ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan

Model Details of ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan

ast-finetuned-audioset-10-10-0.4593-finetuned2-gtzan

This model is a fine-tuned version of MIT/ast-finetuned-audioset-10-10-0.4593 on the GTZAN dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3235
  • Accuracy: 0.93
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: 5e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 10
Training results
Training Loss Epoch Step Validation Loss Accuracy
0.6202 0.99 28 0.6153 0.83
0.3175 1.98 56 0.4811 0.84
0.123 2.97 84 0.4716 0.85
0.0279 4.0 113 0.4575 0.88
0.0348 4.99 141 0.4270 0.88
0.0331 5.98 169 0.3423 0.89
0.0022 6.97 197 0.3178 0.94
0.0009 8.0 226 0.4422 0.9
0.0006 8.99 254 0.3187 0.92
0.0005 9.91 280 0.3235 0.93
Framework versions
  • Transformers 4.32.0.dev0
  • Pytorch 1.12.1+cu113
  • Datasets 2.14.2
  • Tokenizers 0.13.3

Runs of Marco-Cheung ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan on huggingface.co

164
Total runs
0
24-hour runs
0
3-day runs
159
7-day runs
155
30-day runs

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