---
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-eurosat-leukemia-1000
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.945
---

<!-- 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-eurosat-leukemia-1000

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: 0.2078
- Accuracy: 0.945

## 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: 10

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.6899        | 0.9825 | 14   | 0.6198          | 0.6      |
| 0.5494        | 1.9649 | 28   | 0.4008          | 0.805    |
| 0.376         | 2.9474 | 42   | 0.4086          | 0.815    |
| 0.2852        | 4.0    | 57   | 0.4454          | 0.81     |
| 0.184         | 4.9825 | 71   | 0.8481          | 0.715    |
| 0.183         | 5.9649 | 85   | 0.1870          | 0.94     |
| 0.1465        | 6.9474 | 99   | 0.7121          | 0.8      |
| 0.1319        | 8.0    | 114  | 0.2078          | 0.945    |
| 0.1054        | 8.9825 | 128  | 0.3321          | 0.885    |
| 0.096         | 9.8246 | 140  | 0.3423          | 0.885    |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1