---
library_name: transformers
license: apache-2.0
base_model: WinKawaks/vit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: mozilla_dataset_processed_mel_spec_vit_1
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.94
---

<!-- 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. -->

# mozilla_dataset_processed_mel_spec_vit_1

This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4348
- Accuracy: 0.94

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7532        | 1.0   | 11   | 0.6034          | 0.6367   |
| 0.4888        | 2.0   | 22   | 0.2861          | 0.9033   |
| 0.2919        | 3.0   | 33   | 0.2482          | 0.92     |
| 0.1771        | 4.0   | 44   | 0.2018          | 0.9233   |
| 0.1011        | 5.0   | 55   | 0.2074          | 0.9233   |
| 0.0563        | 6.0   | 66   | 0.2219          | 0.9367   |
| 0.0251        | 7.0   | 77   | 0.2835          | 0.9333   |
| 0.0041        | 8.0   | 88   | 0.3132          | 0.9367   |
| 0.001         | 9.0   | 99   | 0.4014          | 0.94     |
| 0.0           | 10.0  | 110  | 0.4260          | 0.9433   |
| 0.0           | 11.0  | 121  | 0.4316          | 0.94     |
| 0.0           | 12.0  | 132  | 0.4329          | 0.94     |
| 0.0           | 13.0  | 143  | 0.4327          | 0.9433   |
| 0.0           | 14.0  | 154  | 0.4334          | 0.94     |
| 0.0           | 15.0  | 165  | 0.4339          | 0.94     |
| 0.0           | 16.0  | 176  | 0.4340          | 0.94     |
| 0.0           | 17.0  | 187  | 0.4344          | 0.94     |
| 0.0           | 18.0  | 198  | 0.4346          | 0.94     |
| 0.0           | 19.0  | 209  | 0.4347          | 0.94     |
| 0.0           | 20.0  | 220  | 0.4348          | 0.94     |


### Framework versions

- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0