---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-ecg
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9642857142857143
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# vit-base-ecg

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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1003
- Accuracy: 0.9643

## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.596         | 2.4390  | 100  | 0.5431          | 0.8214   |
| 0.0656        | 4.8780  | 200  | 0.1628          | 0.95     |
| 0.0192        | 7.3171  | 300  | 0.1003          | 0.9643   |
| 0.0926        | 9.7561  | 400  | 0.1262          | 0.95     |
| 0.0064        | 12.1951 | 500  | 0.1611          | 0.9643   |
| 0.0049        | 14.6341 | 600  | 0.1539          | 0.9643   |
| 0.0044        | 17.0732 | 700  | 0.1509          | 0.9643   |
| 0.0041        | 19.5122 | 800  | 0.1499          | 0.9643   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1