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

# derma-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: 0.6240
- Accuracy: 0.7561
- Precision: 0.5742
- Recall: 0.5353
- F1: 0.5271

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.9135        | 1.0   | 109  | 0.7698          | 0.7198   | 0.5179    | 0.3103 | 0.3050 |
| 0.8352        | 2.0   | 219  | 0.7352          | 0.7298   | 0.5362    | 0.4231 | 0.3884 |
| 0.7891        | 3.0   | 328  | 0.7575          | 0.7178   | 0.3954    | 0.4000 | 0.3667 |
| 0.7649        | 4.0   | 438  | 0.6879          | 0.7418   | 0.5009    | 0.3972 | 0.4146 |
| 0.8146        | 5.0   | 547  | 0.7471          | 0.7178   | 0.4490    | 0.4141 | 0.3641 |
| 0.6831        | 6.0   | 657  | 0.7007          | 0.7368   | 0.4777    | 0.4148 | 0.4252 |
| 0.695         | 7.0   | 766  | 0.6797          | 0.7428   | 0.4638    | 0.5334 | 0.4841 |
| 0.6646        | 8.0   | 876  | 0.6534          | 0.7537   | 0.6130    | 0.5077 | 0.4933 |
| 0.675         | 9.0   | 985  | 0.6238          | 0.7667   | 0.6518    | 0.5431 | 0.5308 |
| 0.6145        | 9.95  | 1090 | 0.6096          | 0.7727   | 0.6427    | 0.5346 | 0.5283 |


### Framework versions

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