---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: microsoft/swin-large-patch4-window7-224-in22k
datasets:
- medmnist-v2
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: organamnist-swin-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-swin-base-finetuned

This model is a fine-tuned version of [microsoft/swin-large-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-large-patch4-window7-224-in22k) on the medmnist-v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2521
- Accuracy: 0.9387
- Precision: 0.9430
- Recall: 0.9343
- F1: 0.9373

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.6172        | 1.0   | 540  | 0.1913          | 0.9373   | 0.9481    | 0.9427 | 0.9422 |
| 0.6346        | 2.0   | 1081 | 0.0756          | 0.9760   | 0.9799    | 0.9752 | 0.9770 |
| 0.6405        | 3.0   | 1621 | 0.1310          | 0.9553   | 0.9600    | 0.9515 | 0.9537 |
| 0.5005        | 4.0   | 2162 | 0.1138          | 0.9663   | 0.9757    | 0.9718 | 0.9729 |
| 0.5669        | 5.0   | 2702 | 0.1142          | 0.9603   | 0.9704    | 0.9647 | 0.9665 |
| 0.5548        | 6.0   | 3243 | 0.0569          | 0.9772   | 0.9812    | 0.9785 | 0.9795 |
| 0.4298        | 7.0   | 3783 | 0.0989          | 0.9663   | 0.9770    | 0.9723 | 0.9736 |
| 0.3932        | 8.0   | 4324 | 0.0335          | 0.9884   | 0.9903    | 0.9887 | 0.9894 |
| 0.3409        | 9.0   | 4864 | 0.0371          | 0.9878   | 0.9900    | 0.9877 | 0.9887 |
| 0.3111        | 9.99  | 5400 | 0.0433          | 0.9846   | 0.9888    | 0.9864 | 0.9874 |


### Framework versions

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