---
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_breastmnist
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: medmnist-v2
      type: medmnist-v2
      config: breastmnist
      split: validation
      args: breastmnist
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8653846153846154
    - name: F1
      type: f1
      value: 0.8156962025316457
---

<!-- 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_breastmnist

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: 0.3570
- Accuracy: 0.8654
- F1: 0.8157

## 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     |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|
| 0.5391        | 0.5556 | 10   | 0.4007          | 0.7949   | 0.6698 |
| 0.3685        | 1.1111 | 20   | 0.3650          | 0.8718   | 0.8120 |
| 0.2275        | 1.6667 | 30   | 0.3601          | 0.8462   | 0.8101 |
| 0.1604        | 2.2222 | 40   | 0.2938          | 0.8718   | 0.8319 |
| 0.0624        | 2.7778 | 50   | 0.2966          | 0.8846   | 0.8511 |
| 0.0597        | 3.3333 | 60   | 0.4313          | 0.8974   | 0.8556 |
| 0.029         | 3.8889 | 70   | 0.4105          | 0.8718   | 0.8194 |
| 0.0094        | 4.4444 | 80   | 0.3746          | 0.9103   | 0.8803 |
| 0.0077        | 5.0    | 90   | 0.4098          | 0.8974   | 0.8655 |
| 0.0082        | 5.5556 | 100  | 0.4451          | 0.9103   | 0.8803 |
| 0.0024        | 6.1111 | 110  | 0.4599          | 0.8974   | 0.8655 |
| 0.0028        | 6.6667 | 120  | 0.4739          | 0.8974   | 0.8608 |
| 0.0013        | 7.2222 | 130  | 0.4653          | 0.8974   | 0.8655 |
| 0.0016        | 7.7778 | 140  | 0.4927          | 0.8974   | 0.8608 |
| 0.0011        | 8.3333 | 150  | 0.5115          | 0.8974   | 0.8608 |
| 0.0015        | 8.8889 | 160  | 0.5055          | 0.8974   | 0.8608 |
| 0.0007        | 9.4444 | 170  | 0.4982          | 0.8974   | 0.8608 |
| 0.0011        | 10.0   | 180  | 0.4975          | 0.8974   | 0.8608 |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0