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

# blood-swin-base-finetuned-wandb

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: 0.1036
- Accuracy: 0.9649
- Precision: 0.9627
- Recall: 0.9616
- F1: 0.9619

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5141        | 1.0   | 187  | 0.2833          | 0.9065   | 0.8954    | 0.8949 | 0.8873 |
| 0.4176        | 2.0   | 374  | 0.1986          | 0.9311   | 0.9243    | 0.9209 | 0.9182 |
| 0.3454        | 3.0   | 561  | 0.1567          | 0.9504   | 0.9427    | 0.9397 | 0.9403 |
| 0.3228        | 4.0   | 748  | 0.1849          | 0.9357   | 0.9232    | 0.9426 | 0.9283 |
| 0.3382        | 5.0   | 935  | 0.1627          | 0.9398   | 0.9302    | 0.9397 | 0.9321 |
| 0.3363        | 6.0   | 1122 | 0.1414          | 0.9509   | 0.9498    | 0.9442 | 0.9456 |
| 0.2981        | 7.0   | 1309 | 0.1117          | 0.9544   | 0.9458    | 0.9542 | 0.9480 |
| 0.2214        | 8.0   | 1496 | 0.1131          | 0.9650   | 0.9642    | 0.9584 | 0.9610 |
| 0.1928        | 9.0   | 1683 | 0.0966          | 0.9650   | 0.9632    | 0.9628 | 0.9624 |
| 0.1901        | 10.0  | 1870 | 0.0775          | 0.9714   | 0.9690    | 0.9699 | 0.9692 |


### Framework versions

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