---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
datasets:
- medmnist-v2
metrics:
- accuracy
- precision
- recall
- f1
base_model: google/vit-base-patch16-224-in21k
model-index:
- name: blood-vit-base-finetuned
  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. -->

# blood-vit-base-finetuned

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 medmnist-v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0692
- Accuracy: 0.9790
- Precision: 0.9772
- Recall: 0.9785
- F1: 0.9778

## 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: 0.005
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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 | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.4059        | 1.0   | 187  | 0.1878          | 0.9311   | 0.9132    | 0.9328 | 0.9201 |
| 0.3796        | 2.0   | 374  | 0.2729          | 0.9083   | 0.9131    | 0.8875 | 0.8861 |
| 0.424         | 3.0   | 561  | 0.3701          | 0.8668   | 0.8797    | 0.8520 | 0.8492 |
| 0.3141        | 4.0   | 748  | 0.1849          | 0.9381   | 0.9267    | 0.9336 | 0.9283 |
| 0.2553        | 5.0   | 935  | 0.1075          | 0.9644   | 0.9630    | 0.9612 | 0.9617 |
| 0.2686        | 6.0   | 1122 | 0.1679          | 0.9486   | 0.9561    | 0.9437 | 0.9489 |
| 0.2556        | 7.0   | 1309 | 0.0934          | 0.9661   | 0.9651    | 0.9599 | 0.9619 |
| 0.1777        | 8.0   | 1496 | 0.0835          | 0.9696   | 0.9697    | 0.9683 | 0.9686 |
| 0.1607        | 9.0   | 1683 | 0.0739          | 0.9772   | 0.9733    | 0.9792 | 0.9759 |
| 0.1898        | 10.0  | 1870 | 0.0627          | 0.9790   | 0.9764    | 0.9812 | 0.9786 |


### Framework versions

- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2