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

Audio Classification
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Safetensors
audio-spectrogram-transformer
Generated from Trainer
Eval Results
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End of training

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Files changed (4) hide show
  1. README.md +21 -31
  2. model.safetensors +1 -1
  3. runs/Aug27_09-10-11_75209667f075/events.out.tfevents.1724749825.75209667f075.596.0 +3 -0
  4. training_args.bin +1 -1
README.md CHANGED
@@ -23,10 +23,10 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.86
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  - name: F1
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  type: f1
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- value: 0.8599999999999999
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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
@@ -36,9 +36,9 @@ should probably proofread and complete it, then remove this comment. -->
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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 GTZAN dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.7149
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- - Accuracy: 0.86
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- - F1: 0.8600
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  ## Model description
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@@ -61,43 +61,33 @@ The following hyperparameters were used during training:
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  - train_batch_size: 2
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  - eval_batch_size: 2
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  - seed: 2024
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- - gradient_accumulation_steps: 4
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- - total_train_batch_size: 8
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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: 20
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  - mixed_precision_training: Native AMP
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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- |:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|
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- | 0.6891 | 0.9956 | 112 | 0.6422 | 0.76 | 0.76 |
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- | 0.7267 | 2.0 | 225 | 0.8163 | 0.78 | 0.78 |
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- | 0.7077 | 2.9956 | 337 | 0.7802 | 0.8 | 0.8000 |
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- | 0.1884 | 4.0 | 450 | 0.6157 | 0.87 | 0.87 |
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- | 0.0209 | 4.9956 | 562 | 0.7885 | 0.84 | 0.8400 |
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- | 0.117 | 6.0 | 675 | 0.6744 | 0.85 | 0.85 |
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- | 0.0098 | 6.9956 | 787 | 0.6213 | 0.85 | 0.85 |
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- | 0.0002 | 8.0 | 900 | 1.0599 | 0.82 | 0.82 |
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- | 0.0001 | 8.9956 | 1012 | 0.7052 | 0.86 | 0.8600 |
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- | 0.0001 | 10.0 | 1125 | 0.6891 | 0.85 | 0.85 |
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- | 0.0001 | 10.9956 | 1237 | 0.6718 | 0.86 | 0.8600 |
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- | 0.0001 | 12.0 | 1350 | 0.6712 | 0.85 | 0.85 |
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- | 0.0001 | 12.9956 | 1462 | 0.6942 | 0.86 | 0.8600 |
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- | 0.0001 | 14.0 | 1575 | 0.7002 | 0.86 | 0.8600 |
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- | 0.0176 | 14.9956 | 1687 | 0.7053 | 0.86 | 0.8600 |
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- | 0.0001 | 16.0 | 1800 | 0.7140 | 0.86 | 0.8600 |
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- | 0.0001 | 16.9956 | 1912 | 0.7089 | 0.86 | 0.8600 |
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- | 0.0 | 18.0 | 2025 | 0.7120 | 0.86 | 0.8600 |
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- | 0.0628 | 18.9956 | 2137 | 0.7162 | 0.86 | 0.8600 |
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- | 0.0037 | 19.9111 | 2240 | 0.7149 | 0.86 | 0.8600 |
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  ### Framework versions
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  - Transformers 4.42.4
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- - Pytorch 2.3.1+cu121
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  - Datasets 2.21.0
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  - Tokenizers 0.19.1
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.87
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  - name: F1
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  type: f1
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+ value: 0.87
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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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  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.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5658
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+ - Accuracy: 0.87
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+ - F1: 0.87
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  ## Model description
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  - train_batch_size: 2
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  - eval_batch_size: 2
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  - seed: 2024
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+ - gradient_accumulation_steps: 8
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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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  - mixed_precision_training: Native AMP
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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+ |:-------------:|:------:|:----:|:---------------:|:--------:|:------:|
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+ | 0.8357 | 0.9956 | 56 | 0.6582 | 0.82 | 0.82 |
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+ | 0.4742 | 1.9911 | 112 | 0.6527 | 0.81 | 0.81 |
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+ | 0.3344 | 2.9867 | 168 | 0.9048 | 0.76 | 0.76 |
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+ | 0.0659 | 4.0 | 225 | 0.6998 | 0.84 | 0.8400 |
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+ | 0.0966 | 4.9956 | 281 | 0.6737 | 0.83 | 0.83 |
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+ | 0.0026 | 5.9911 | 337 | 0.5133 | 0.89 | 0.89 |
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+ | 0.0038 | 6.9867 | 393 | 0.5704 | 0.86 | 0.8600 |
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+ | 0.0005 | 8.0 | 450 | 0.5722 | 0.86 | 0.8600 |
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+ | 0.0003 | 8.9956 | 506 | 0.5632 | 0.87 | 0.87 |
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+ | 0.0003 | 9.9556 | 560 | 0.5658 | 0.87 | 0.87 |
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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  - Transformers 4.42.4
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+ - Pytorch 2.4.0+cu121
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  - Datasets 2.21.0
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  - Tokenizers 0.19.1
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