
# vit-base-vocalsound

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on [VocalSound](https://github.com/YuanGongND/vocalsound) dataset.
It achieves the following results on the evaluation set:

- accuracy: 81.5
- precision (micro): 86.9
- recall (micro): 76.4
- f1 score (micro): 81.3
- f1 score (macro): 81.2

## Training and evaluation data

Training: VocalSound training split (#samples = 15570)

Evaluation: VocalSound test split(#samples = 3594)

### Training hyperparameters

The following hyperparameters were used during training:
- optimizer: AdamW
  - weight_decay: 0
  - learning_rate: 5e-5
- batch_size: 32
- training_precision: float32

### Framework versions

- Transformers 4.27.4
- TensorFlow 2.12.0
- Tokenizers 0.13.3
{"license": "apache-2.0", "model-index": [{"name": "vit-base-vocalsound", "results": []}]}
transformers tf vit feature-extraction license:apache-2.0 endpoints_compatible has_space region:us
TAGS
#transformers #tf #vit #feature-extraction #license-apache-2.0 #endpoints_compatible #has_space #region-us 


# vit-base-vocalsound

This model is a fine-tuned version of google/vit-base-patch16-224 on VocalSound dataset.
It achieves the following results on the evaluation set:

- accuracy: 81.5
- precision (micro): 86.9
- recall (micro): 76.4
- f1 score (micro): 81.3
- f1 score (macro): 81.2

## Training and evaluation data

Training: VocalSound training split (#samples = 15570)

Evaluation: VocalSound test split(#samples = 3594)

### Training hyperparameters

The following hyperparameters were used during training:
- optimizer: AdamW
  - weight_decay: 0
  - learning_rate: 5e-5
- batch_size: 32
- training_precision: float32

### Framework versions

- Transformers 4.27.4
- TensorFlow 2.12.0
- Tokenizers 0.13.3
# vit-base-vocalsound

This model is a fine-tuned version of google/vit-base-patch16-224 on VocalSound dataset.
It achieves the following results on the evaluation set:

- accuracy: 81.5
- precision (micro): 86.9
- recall (micro): 76.4
- f1 score (micro): 81.3
- f1 score (macro): 81.2 ## Training and evaluation data

Training: VocalSound training split (#samples = 15570)

Evaluation: VocalSound test split(#samples = 3594) ### Training hyperparameters

The following hyperparameters were used during training:
- optimizer: AdamW
  - weight_decay: 0
  - learning_rate: 5e-5
- batch_size: 32
- training_precision: float32 ### Framework versions

- Transformers 4.27.4
- TensorFlow 2.12.0
- Tokenizers 0.13.3
TAGS
#transformers #tf #vit #feature-extraction #license-apache-2.0 #endpoints_compatible #has_space #region-us 
 # vit-base-vocalsound

This model is a fine-tuned version of google/vit-base-patch16-224 on VocalSound dataset.
It achieves the following results on the evaluation set:

- accuracy: 81.5
- precision (micro): 86.9
- recall (micro): 76.4
- f1 score (micro): 81.3
- f1 score (macro): 81.2 ## Training and evaluation data

Training: VocalSound training split (#samples = 15570)

Evaluation: VocalSound test split(#samples = 3594) ### Training hyperparameters

The following hyperparameters were used during training:
- optimizer: AdamW
  - weight_decay: 0
  - learning_rate: 5e-5
- batch_size: 32
- training_precision: float32 ### Framework versions

- Transformers 4.27.4
- TensorFlow 2.12.0
- Tokenizers 0.13.3