---
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-rsna-2018
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7410179640718563
---

<!-- 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. -->

# swin-tiny-patch4-window7-224-finetuned-rsna-2018

This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co./microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5745
- Accuracy: 0.7410

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.6448        | 0.9940  | 83   | 0.6735          | 0.6737   |
| 0.736         | 2.0     | 167  | 0.6969          | 0.6557   |
| 0.6895        | 2.9940  | 250  | 0.6265          | 0.6916   |
| 0.6631        | 4.0     | 334  | 0.6275          | 0.7156   |
| 0.6725        | 4.9940  | 417  | 0.6311          | 0.7126   |
| 0.6778        | 6.0     | 501  | 0.6194          | 0.7066   |
| 0.6734        | 6.9940  | 584  | 0.6024          | 0.7141   |
| 0.6231        | 8.0     | 668  | 0.6082          | 0.7231   |
| 0.6164        | 8.9940  | 751  | 0.5846          | 0.7171   |
| 0.6261        | 10.0    | 835  | 0.5682          | 0.7380   |
| 0.6153        | 10.9940 | 918  | 0.6007          | 0.7186   |
| 0.6046        | 12.0    | 1002 | 0.5745          | 0.7410   |
| 0.5679        | 12.9940 | 1085 | 0.5957          | 0.7231   |
| 0.6027        | 14.0    | 1169 | 0.5884          | 0.7216   |
| 0.6249        | 14.9940 | 1252 | 0.5808          | 0.7365   |
| 0.6059        | 16.0    | 1336 | 0.5699          | 0.7350   |
| 0.5776        | 16.9940 | 1419 | 0.5770          | 0.7320   |
| 0.5903        | 18.0    | 1503 | 0.5806          | 0.7216   |
| 0.5633        | 18.9940 | 1586 | 0.5768          | 0.7380   |
| 0.5544        | 20.0    | 1670 | 0.5830          | 0.7350   |
| 0.5515        | 20.9940 | 1753 | 0.5966          | 0.7260   |
| 0.5249        | 22.0    | 1837 | 0.6079          | 0.7335   |
| 0.5212        | 22.9940 | 1920 | 0.5972          | 0.7246   |
| 0.5268        | 24.0    | 2004 | 0.5922          | 0.7231   |
| 0.5406        | 24.9940 | 2087 | 0.6100          | 0.7350   |
| 0.5257        | 26.0    | 2171 | 0.6004          | 0.7305   |
| 0.5152        | 26.9940 | 2254 | 0.6092          | 0.7320   |
| 0.4858        | 28.0    | 2338 | 0.6100          | 0.7231   |
| 0.5412        | 28.9940 | 2421 | 0.6116          | 0.7350   |
| 0.4972        | 29.8204 | 2490 | 0.6120          | 0.7290   |


### Framework versions

- Transformers 4.43.3
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1