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# ast_9-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: 0.8548
- Accuracy: 0.6572
- Sensitivity: 0.4183
- Specificity: 0.8710
- Score: 0.6446

## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Sensitivity | Specificity | Score  |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:-----------:|:------:|
| 0.9335        | 1.0   | 258  | 0.9986          | 0.5986   | 0.5318      | 0.6582      | 0.5950 |
| 0.8392        | 2.0   | 517  | 0.8548          | 0.6572   | 0.4183      | 0.8710      | 0.6446 |
| 0.6187        | 3.0   | 776  | 0.8978          | 0.6554   | 0.4797      | 0.8126      | 0.6461 |
| 0.2052        | 4.0   | 1035 | 1.1767          | 0.6533   | 0.4536      | 0.8318      | 0.6427 |
| 0.1132        | 4.99  | 1290 | 1.4218          | 0.6525   | 0.5004      | 0.7886      | 0.6445 |


### Framework versions

- Transformers 4.29.1
- 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_9-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\_9-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: 0.8548
* Accuracy: 0.6572
* Sensitivity: 0.4183
* Specificity: 0.8710
* Score: 0.6446


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: 5


### Training results



### Framework versions


* Transformers 4.29.1
* 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: 5 ### Training results ### Framework versions


* Transformers 4.29.1
* 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: 5 ### Training results ### Framework versions


* Transformers 4.29.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3