---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
base_model: microsoft/swin-large-patch4-window7-224-in22k
model-index:
- name: chest-swin-large-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. -->

# chest-swin-large-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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1159
- Accuracy: 0.9588
- Precision: 0.9599
- Recall: 0.9401
- F1: 0.9492

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.3305        | 0.99  | 63   | 0.1600          | 0.9365   | 0.9478    | 0.8868 | 0.9119 |
| 0.2335        | 1.99  | 127  | 0.1552          | 0.9313   | 0.8968    | 0.9472 | 0.9166 |
| 0.1977        | 3.0   | 191  | 0.0855          | 0.9734   | 0.9608    | 0.9714 | 0.9659 |
| 0.1746        | 4.0   | 255  | 0.0870          | 0.9794   | 0.9794    | 0.9669 | 0.9729 |
| 0.1797        | 4.99  | 318  | 0.0829          | 0.9700   | 0.9549    | 0.9690 | 0.9617 |
| 0.1436        | 5.99  | 382  | 0.0797          | 0.9708   | 0.9556    | 0.9707 | 0.9628 |
| 0.1632        | 7.0   | 446  | 0.0816          | 0.9700   | 0.9508    | 0.9754 | 0.9621 |
| 0.1125        | 8.0   | 510  | 0.1007          | 0.9614   | 0.9365    | 0.9717 | 0.9519 |
| 0.1076        | 8.99  | 573  | 0.0900          | 0.9691   | 0.9482    | 0.9770 | 0.9612 |
| 0.1188        | 9.88  | 630  | 0.1064          | 0.9622   | 0.9377    | 0.9723 | 0.9530 |


### Framework versions

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