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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: 0.4532 Accuracy: 0.89
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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:
The following hyperparameters were used during training:
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1 fonteVerificado 13 de set.
Artefatos de modelo
1 artefatoTrechos de fonte
2 trechos| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.7894 | 1.0 | 57 | 0.5304 | 0.86 |
| 0.5723 | 2.0 | 114 | 1.0449 | 0.69 |
| 0.5660 | 3.0 | 171 | 0.5600 | 0.82 |
| 0.3927 | 4.0 | 228 | 0.6672 | 0.83 |
| 0.2168 | 5.0 | 285 | 0.8590 | 0.8 |
--- library_name: transformers license: bsd-3-clause base_model: MIT/ast-finetuned-audioset-10-10-0.4593 tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan results: - task: name: Audio Classification type: audio-classification dataset: name: GTZAN type: marsyas/gtzan config: all split: train args: all metrics: - name: Accuracy type: accuracy value: 0.89 --- <!-- 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. --> # ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan 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 GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.4532 - Accuracy: 0.89 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7894 | 1.0 | 57 | 0.5304 | 0.86 | | 0.5723 | 2.0 | 114 | 1.0449 | 0.69 | | 0.5660 | 3.0 | 171 | 0.5600 | 0.82 | | 0.3927 | 4.0 | 228 | 0.6672 | 0.83 | | 0.2168 | 5.0 | 285 | 0.8590 | 0.8 | | 0.1481 | 6.0 | 342 | 0.7175 | 0.84 | | 0.0909 | 7.0 | 399 | 0.7492 | 0.83 | | 0.0099 | 8.0 | 456 | 0.6389 | 0.87 | | 0.0006 | 9.0 | 513 | 0.4595 | 0.88 | | 0.0002 | 10.0 | 570 | 0.4532 | 0.89 | ### Framework versions - Transformers 5.1.0 - Pytorch 2.10.0+cu128 - Datasets 2.16.1 - Tokenizers 0.22.2
Source context: 2 downloads · 0 likes · Pipeline audio-classification · Library transformers · Repo vishal98m/ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan
| 0.1481 | 6.0 | 342 | 0.7175 | 0.84 |
| 0.0909 | 7.0 | 399 | 0.7492 | 0.83 |
| 0.0099 | 8.0 | 456 | 0.6389 | 0.87 |
| 0.0006 | 9.0 | 513 | 0.4595 | 0.88 |
| 0.0002 | 10.0 | 570 | 0.4532 | 0.89 |
