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# ast_15-finetuned-ICBHI

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: 1.1688
- Accuracy: 0.5397
- Sensitivity: 0.2727
- Specificity: 0.7389
- Score: 0.5058

## 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: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Sensitivity | Specificity | Score  |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:-----------:|:------:|
| 0.7488        | 1.0   | 259  | 1.1831          | 0.5241   | 0.3551      | 0.6502      | 0.5027 |
| 0.7831        | 2.0   | 518  | 1.1688          | 0.5397   | 0.2727      | 0.7389      | 0.5058 |
| 0.7471        | 3.0   | 777  | 1.1593          | 0.5198   | 0.3772      | 0.6261      | 0.5017 |
| 0.5336        | 4.0   | 1036 | 1.4082          | 0.5281   | 0.3152      | 0.6869      | 0.5011 |
| 0.3833        | 5.0   | 1295 | 2.0232          | 0.4838   | 0.3840      | 0.5583      | 0.4712 |
| 0.1721        | 6.0   | 1554 | 2.5558          | 0.4893   | 0.3534      | 0.5906      | 0.4720 |
| 0.2745        | 7.0   | 1813 | 3.3175          | 0.4900   | 0.3917      | 0.5634      | 0.4775 |
| 0.0596        | 8.0   | 2072 | 3.6548          | 0.5143   | 0.3628      | 0.6274      | 0.4951 |
| 0.0034        | 9.0   | 2331 | 3.9119          | 0.5082   | 0.4053      | 0.5849      | 0.4951 |
| 0.0008        | 10.0  | 2590 | 4.3407          | 0.4875   | 0.4562      | 0.5108      | 0.4835 |
| 0.0           | 11.0  | 2849 | 4.1927          | 0.5136   | 0.3636      | 0.6255      | 0.4946 |
| 0.0           | 12.0  | 3108 | 4.2227          | 0.5111   | 0.3645      | 0.6204      | 0.4924 |
| 0.0           | 13.0  | 3367 | 4.2399          | 0.5114   | 0.3653      | 0.6204      | 0.4929 |
| 0.0           | 14.0  | 3626 | 4.2521          | 0.5114   | 0.3662      | 0.6198      | 0.4930 |
| 0.0           | 15.0  | 3885 | 4.2556          | 0.5114   | 0.3662      | 0.6198      | 0.4930 |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "bsd-3-clause", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "ast_15-finetuned-ICBHI", "results": []}]}
transformers pytorch tensorboard audio-spectrogram-transformer audio-classification generated_from_trainer license:bsd-3-clause endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #audio-spectrogram-transformer #audio-classification #generated_from_trainer #license-bsd-3-clause #endpoints_compatible #region-us 

ast\_15-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: 1.1688
* Accuracy: 0.5397
* Sensitivity: 0.2727
* Specificity: 0.7389
* Score: 0.5058


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: 15


### Training results



### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### 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: 15 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #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: 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: 15 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3