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# ast_18-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: 1.0867
- Accuracy: 0.5757
- Sensitivity: 0.1164
- Specificity: 0.9183
- Score: 0.5173

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Sensitivity | Specificity | Score  |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:-----------:|:------:|
| 1.5436        | 0.98  | 32   | 1.4656          | 0.1684   | 0.3127      | 0.0608      | 0.1867 |
| 1.2922        | 2.0   | 65   | 1.2055          | 0.4530   | 0.1121      | 0.7072      | 0.4097 |
| 1.2213        | 2.98  | 97   | 1.1364          | 0.5387   | 0.0450      | 0.9068      | 0.4759 |
| 1.149         | 4.0   | 130  | 1.1176          | 0.5543   | 0.0731      | 0.9132      | 0.4931 |
| 1.1558        | 4.98  | 162  | 1.1035          | 0.5630   | 0.0705      | 0.9303      | 0.5004 |
| 1.1363        | 6.0   | 195  | 1.1006          | 0.5655   | 0.1020      | 0.9113      | 0.5066 |
| 1.1138        | 6.98  | 227  | 1.0938          | 0.5699   | 0.1121      | 0.9113      | 0.5117 |
| 1.0807        | 8.0   | 260  | 1.0897          | 0.5742   | 0.1147      | 0.9170      | 0.5158 |
| 1.1071        | 8.98  | 292  | 1.0867          | 0.5757   | 0.1138      | 0.9202      | 0.5170 |
| 1.1017        | 9.85  | 320  | 1.0867          | 0.5757   | 0.1164      | 0.9183      | 0.5173 |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "bsd-3-clause", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "ast_18-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\_18-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: 1.0867
* Accuracy: 0.5757
* Sensitivity: 0.1164
* Specificity: 0.9183
* Score: 0.5173


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: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10


### Training results



### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+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: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+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: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3