---
license: apache-2.0
base_model: microsoft/swin-large-patch4-window12-384-in22k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: microsoft/swin-large-patch4-window12-384-in22k
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: NIH-Xray
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.49376114081996436
---

<!-- 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. -->

# microsoft/swin-large-patch4-window12-384-in22k

This model is a fine-tuned version of [microsoft/swin-large-patch4-window12-384-in22k](https://huggingface.co/microsoft/swin-large-patch4-window12-384-in22k) on the NIH-Xray dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7711
- Accuracy: 0.4938

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.8318        | 0.9984 | 315  | 1.7651          | 0.5437   |
| 1.6067        | 2.0    | 631  | 1.6393          | 0.5455   |
| 1.406         | 2.9984 | 946  | 1.6472          | 0.5490   |
| 1.3983        | 4.0    | 1262 | 1.7344          | 0.5455   |
| 0.7272        | 4.9984 | 1577 | 2.1283          | 0.5258   |
| 0.3975        | 6.0    | 1893 | 2.5229          | 0.5134   |
| 0.2648        | 6.9984 | 2208 | 3.0333          | 0.5080   |
| 0.1232        | 8.0    | 2524 | 3.4626          | 0.5241   |
| 0.0873        | 8.9984 | 2839 | 3.6219          | 0.5027   |
| 0.0554        | 9.9842 | 3150 | 3.7711          | 0.4938   |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.19.1