---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: microsoft/beit-base-patch16-224-pt22k-ft22k
datasets:
- medmnist-v2
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: blood-beit-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-beit-base-finetuned

This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the medmnist-v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0847
- Accuracy: 0.9737
- Precision: 0.9726
- Recall: 0.9724
- F1: 0.9724

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.4657        | 1.0   | 187  | 0.2452          | 0.9095   | 0.8964    | 0.9083 | 0.8973 |
| 0.4327        | 2.0   | 374  | 0.2111          | 0.9182   | 0.9299    | 0.8921 | 0.9007 |
| 0.3977        | 3.0   | 561  | 0.1743          | 0.9340   | 0.9229    | 0.9282 | 0.9244 |
| 0.3318        | 4.0   | 748  | 0.1776          | 0.9352   | 0.9248    | 0.9353 | 0.9285 |
| 0.3461        | 5.0   | 935  | 0.1703          | 0.9381   | 0.9311    | 0.9344 | 0.9305 |
| 0.3309        | 6.0   | 1122 | 0.1956          | 0.9369   | 0.9336    | 0.9397 | 0.9335 |
| 0.3088        | 7.0   | 1309 | 0.1179          | 0.9533   | 0.9427    | 0.9525 | 0.9461 |
| 0.2129        | 8.0   | 1496 | 0.0992          | 0.9638   | 0.9569    | 0.9674 | 0.9611 |
| 0.2049        | 9.0   | 1683 | 0.0847          | 0.9679   | 0.9627    | 0.9683 | 0.9651 |
| 0.2007        | 10.0  | 1870 | 0.0785          | 0.9708   | 0.9668    | 0.9737 | 0.9698 |


### Framework versions

- PEFT 0.11.1
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1