---
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: organamnist-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. -->

# organamnist-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: 0.2372
- Accuracy: 0.9329
- Precision: 0.9416
- Recall: 0.9296
- F1: 0.9340

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.6786        | 1.0   | 540  | 0.1776          | 0.9339   | 0.9507    | 0.9341 | 0.9385 |
| 0.7397        | 2.0   | 1081 | 0.1783          | 0.9407   | 0.9539    | 0.9346 | 0.9415 |
| 0.7151        | 3.0   | 1621 | 0.1297          | 0.9552   | 0.9611    | 0.9555 | 0.9572 |
| 0.4964        | 4.0   | 2162 | 0.0741          | 0.9735   | 0.9765    | 0.9702 | 0.9730 |
| 0.5509        | 5.0   | 2702 | 0.0671          | 0.9770   | 0.9776    | 0.9796 | 0.9783 |
| 0.5746        | 6.0   | 3243 | 0.0642          | 0.9754   | 0.9810    | 0.9788 | 0.9795 |
| 0.4066        | 7.0   | 3783 | 0.1196          | 0.9566   | 0.9693    | 0.9563 | 0.9614 |
| 0.4046        | 8.0   | 4324 | 0.0469          | 0.9798   | 0.9853    | 0.9821 | 0.9834 |
| 0.3314        | 9.0   | 4864 | 0.0388          | 0.9861   | 0.9892    | 0.9860 | 0.9874 |
| 0.2865        | 9.99  | 5400 | 0.0450          | 0.9831   | 0.9880    | 0.9862 | 0.9869 |


### Framework versions

- PEFT 0.11.1
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2