---
license: bsd-3-clause
base_model: MIT/ast-finetuned-audioset-10-10-0.450
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: ast-finetuned-audioset-10-10-0.450_ESC50
  results: []
---

<!-- 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-finetuned-audioset-10-10-0.450_ESC50

This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.450](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.450) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2887
- Accuracy: 0.9275

## 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: 1e-05
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 24
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.7745        | 0.99  | 66   | 2.3340          | 0.605    |
| 0.7521        | 1.99  | 133  | 0.8978          | 0.8875   |
| 0.2307        | 3.0   | 200  | 0.5545          | 0.8975   |
| 0.0903        | 4.0   | 267  | 0.4063          | 0.925    |
| 0.03          | 4.99  | 333  | 0.3488          | 0.92     |
| 0.0123        | 5.99  | 400  | 0.2987          | 0.925    |
| 0.0101        | 7.0   | 467  | 0.2887          | 0.9275   |
| 0.0067        | 8.0   | 534  | 0.2808          | 0.9275   |
| 0.0055        | 8.99  | 600  | 0.2784          | 0.9275   |
| 0.0051        | 9.89  | 660  | 0.2778          | 0.9275   |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2