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# ast-finetuned-audioset-10-10-0.4593_ft_env_0-12

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.
It achieves the following results on the evaluation set:
- Loss: 0.3804
- Accuracy: 0.9643
- Precision: 0.9702
- Recall: 0.9643
- F1: 0.9643

## 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: 1.5e-06
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 56
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 2.0371        | 1.0   | 28   | 1.9267          | 0.1429   | 0.3214    | 0.1429 | 0.1482 |
| 1.7315        | 2.0   | 56   | 1.5823          | 0.3214   | 0.3667    | 0.3214 | 0.2973 |
| 1.3081        | 3.0   | 84   | 1.2250          | 0.75     | 0.8423    | 0.75   | 0.7499 |
| 0.9664        | 4.0   | 112  | 0.9526          | 0.8214   | 0.8616    | 0.8214 | 0.8078 |
| 0.6607        | 5.0   | 140  | 0.7525          | 0.8571   | 0.8795    | 0.8571 | 0.8520 |
| 0.5239        | 6.0   | 168  | 0.6080          | 0.8929   | 0.9152    | 0.8929 | 0.8866 |
| 0.453         | 7.0   | 196  | 0.5089          | 0.9286   | 0.9286    | 0.9286 | 0.9286 |
| 0.323         | 8.0   | 224  | 0.4353          | 0.9286   | 0.9286    | 0.9286 | 0.9286 |
| 0.296         | 9.0   | 252  | 0.3804          | 0.9643   | 0.9702    | 0.9643 | 0.9643 |
| 0.2167        | 10.0  | 280  | 0.3382          | 0.9643   | 0.9702    | 0.9643 | 0.9643 |
| 0.186         | 11.0  | 308  | 0.3157          | 0.9643   | 0.9702    | 0.9643 | 0.9643 |
| 0.1748        | 12.0  | 336  | 0.2931          | 0.9643   | 0.9702    | 0.9643 | 0.9643 |
| 0.1367        | 13.0  | 364  | 0.2781          | 0.9643   | 0.9702    | 0.9643 | 0.9643 |
| 0.1469        | 14.0  | 392  | 0.2705          | 0.9643   | 0.9702    | 0.9643 | 0.9643 |
| 0.1308        | 15.0  | 420  | 0.2679          | 0.9643   | 0.9702    | 0.9643 | 0.9643 |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0
- Datasets 2.10.1
- Tokenizers 0.11.0

{"license": "bsd-3-clause", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "model-index": [{"name": "ast-finetuned-audioset-10-10-0.4593_ft_env_0-12", "results": []}]}
transformers pytorch audio-spectrogram-transformer audio-classification generated_from_trainer license:bsd-3-clause endpoints_compatible region:us
TAGS
#transformers #pytorch #audio-spectrogram-transformer #audio-classification #generated_from_trainer #license-bsd-3-clause #endpoints_compatible #region-us 

ast-finetuned-audioset-10-10-0.4593\_ft\_env\_0-12
==================================================


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: 0.3804
* Accuracy: 0.9643
* Precision: 0.9702
* Recall: 0.9643
* F1: 0.9643


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: 1.5e-06
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 56
* num\_epochs: 15


### Training results



### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0
* Datasets 2.10.1
* Tokenizers 0.11.0



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1.5e-06
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 56
* num\_epochs: 15 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0
* Datasets 2.10.1
* Tokenizers 0.11.0
TAGS
#transformers #pytorch #audio-spectrogram-transformer #audio-classification #generated_from_trainer #license-bsd-3-clause #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1.5e-06
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 56
* num\_epochs: 15 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0
* Datasets 2.10.1
* Tokenizers 0.11.0