---
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: organsmnist-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. -->

# organsmnist-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: 0.4609
- Accuracy: 0.8240
- Precision: 0.7895
- Recall: 0.7821
- F1: 0.7852

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.9608        | 1.0   | 218  | 0.6055          | 0.7765   | 0.7235    | 0.7233 | 0.7007 |
| 0.9984        | 2.0   | 436  | 0.4812          | 0.8067   | 0.7265    | 0.7321 | 0.7114 |
| 0.8265        | 3.0   | 654  | 0.3726          | 0.8520   | 0.8005    | 0.7713 | 0.7683 |
| 0.7938        | 4.0   | 872  | 0.3913          | 0.8507   | 0.7812    | 0.7831 | 0.7554 |
| 0.8149        | 5.0   | 1090 | 0.3676          | 0.8532   | 0.7687    | 0.8002 | 0.7702 |
| 0.6737        | 6.0   | 1308 | 0.3305          | 0.8675   | 0.8306    | 0.8117 | 0.7934 |
| 0.5695        | 7.0   | 1526 | 0.2481          | 0.9029   | 0.8546    | 0.8469 | 0.8321 |
| 0.5857        | 8.0   | 1744 | 0.2912          | 0.8923   | 0.8464    | 0.8356 | 0.8340 |
| 0.4834        | 9.0   | 1962 | 0.2658          | 0.8997   | 0.8428    | 0.8410 | 0.8286 |
| 0.5287        | 10.0  | 2180 | 0.2590          | 0.9050   | 0.8524    | 0.8468 | 0.8468 |


### Framework versions

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