
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# ast_22-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: 2.5036
- Accuracy: 0.6867
- Sensitivity: 0.5346
- Specificity: 0.8228
- Score: 0.6787

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Sensitivity | Specificity | Score  |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:-----------:|:------:|
| 1.0662        | 1.0   | 345  | 0.9169          | 0.6389   | 0.3963      | 0.8558      | 0.6260 |
| 0.7055        | 2.0   | 690  | 0.8638          | 0.6512   | 0.4516      | 0.8297      | 0.6406 |
| 0.5748        | 3.0   | 1035 | 0.9060          | 0.6599   | 0.4409      | 0.8558      | 0.6483 |
| 0.3318        | 4.0   | 1380 | 1.1034          | 0.6555   | 0.3641      | 0.9162      | 0.6401 |
| 0.1411        | 5.0   | 1725 | 1.3586          | 0.6838   | 0.5346      | 0.8173      | 0.6759 |
| 0.0854        | 6.0   | 2070 | 2.1432          | 0.6759   | 0.4608      | 0.8681      | 0.6645 |
| 0.0186        | 7.0   | 2415 | 2.3421          | 0.6715   | 0.5545      | 0.7761      | 0.6653 |
| 0.0417        | 8.0   | 2760 | 2.4426          | 0.6824   | 0.5361      | 0.8132      | 0.6746 |
| 0.0001        | 9.0   | 3105 | 2.4895          | 0.6831   | 0.5346      | 0.8159      | 0.6752 |
| 0.0           | 10.0  | 3450 | 2.5036          | 0.6867   | 0.5346      | 0.8228      | 0.6787 |


### 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_22-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\_22-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: 2.5036
* Accuracy: 0.6867
* Sensitivity: 0.5346
* Specificity: 0.8228
* Score: 0.6787


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: 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: 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: 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: 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: 10 ### Training results ### Framework versions


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