---
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: breastmnist-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. -->

# breastmnist-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: 0.3595
- Accuracy: 0.8526
- Precision: 0.8162
- Recall: 0.8014
- F1: 0.8082

## 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     |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log        | 0.9143 | 8    | 0.5069          | 0.7436   | 0.8701    | 0.5238 | 0.4708 |
| 0.6976        | 1.9429 | 17   | 0.4591          | 0.8590   | 0.8190    | 0.8283 | 0.8234 |
| 0.5351        | 2.9714 | 26   | 0.3745          | 0.8846   | 0.8667    | 0.8308 | 0.8462 |
| 0.4998        | 4.0    | 35   | 0.3243          | 0.8974   | 0.8697    | 0.8697 | 0.8697 |
| 0.4569        | 4.9143 | 43   | 0.4070          | 0.8590   | 0.8306    | 0.7982 | 0.8120 |
| 0.4182        | 5.9429 | 52   | 0.3801          | 0.8718   | 0.8439    | 0.8221 | 0.8319 |
| 0.4432        | 6.9714 | 61   | 0.3071          | 0.8718   | 0.8371    | 0.8371 | 0.8371 |
| 0.3988        | 8.0    | 70   | 0.3205          | 0.8718   | 0.8332    | 0.8521 | 0.8417 |
| 0.3988        | 8.9143 | 78   | 0.3239          | 0.8846   | 0.8506    | 0.8609 | 0.8555 |
| 0.3993        | 9.1429 | 80   | 0.3214          | 0.8846   | 0.8506    | 0.8609 | 0.8555 |


### Framework versions

- PEFT 0.11.1
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1