---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base-melSpecImagesCREMA
  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. -->

# vit-base-melSpecImagesCREMA

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the Supreeta03/CREMA-melSpecImages dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1416
- Accuracy: 0.5808

## 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: 0.0002
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5606        | 0.76  | 100  | 1.4424          | 0.4079   |
| 1.2841        | 1.53  | 200  | 1.4981          | 0.3695   |
| 1.0159        | 2.29  | 300  | 1.1693          | 0.5518   |
| 0.9868        | 3.05  | 400  | 1.0969          | 0.5931   |
| 0.8477        | 3.82  | 500  | 1.1719          | 0.5797   |
| 0.5495        | 4.58  | 600  | 1.2348          | 0.5806   |
| 0.2671        | 5.34  | 700  | 1.3457          | 0.5854   |
| 0.1388        | 6.11  | 800  | 1.3891          | 0.5787   |
| 0.1548        | 6.87  | 900  | 1.4216          | 0.5979   |
| 0.0906        | 7.63  | 1000 | 1.6401          | 0.5643   |
| 0.1047        | 8.4   | 1100 | 1.6780          | 0.5873   |
| 0.0583        | 9.16  | 1200 | 1.6795          | 0.5768   |
| 0.0228        | 9.92  | 1300 | 1.6926          | 0.5883   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2