---
license: apache-2.0
base_model: microsoft/swin-base-patch4-window7-224-in22k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-base-patch4-window7-224-in22k-finetuned-CT-V2
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: Testing
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7944162436548223
---

<!-- 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-base-patch4-window7-224-in22k-finetuned-CT-V2

This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co./microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3847
- Accuracy: 0.7944

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 180  | 1.1743          | 0.6675   |
| No log        | 2.0   | 360  | 0.6626          | 0.8223   |
| No log        | 3.0   | 540  | 1.1876          | 0.7868   |
| No log        | 4.0   | 720  | 1.2469          | 0.8274   |
| 0.2764        | 5.0   | 900  | 1.3847          | 0.7944   |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.3.0+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1