---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- medmnist-v2
metrics:
- accuracy
- f1
model-index:
- name: ViT_bloodmnist
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: medmnist-v2
      type: medmnist-v2
      config: bloodmnist
      split: validation
      args: bloodmnist
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9748611517100263
    - name: F1
      type: f1
      value: 0.97180354304681
---

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

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the medmnist-v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0879
- Accuracy: 0.9749
- F1: 0.9718

## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.2747        | 1.0   | 374  | 0.0930          | 0.9696   | 0.9652 |
| 0.0955        | 2.0   | 748  | 0.0998          | 0.9702   | 0.9670 |
| 0.0405        | 3.0   | 1122 | 0.0812          | 0.9743   | 0.9725 |
| 0.0194        | 4.0   | 1496 | 0.0829          | 0.9796   | 0.9784 |
| 0.0081        | 5.0   | 1870 | 0.1328          | 0.9720   | 0.9696 |
| 0.0026        | 6.0   | 2244 | 0.1252          | 0.9743   | 0.9735 |
| 0.0004        | 7.0   | 2618 | 0.0997          | 0.9790   | 0.9778 |
| 0.0001        | 8.0   | 2992 | 0.1049          | 0.9784   | 0.9768 |
| 0.0001        | 9.0   | 3366 | 0.1072          | 0.9778   | 0.9761 |
| 0.0001        | 10.0  | 3740 | 0.1077          | 0.9778   | 0.9761 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.2
- Tokenizers 0.19.1