---
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-cifar10
  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. -->

# swin-tiny-patch4-window7-224-finetuned-cifar10

This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0760
- Accuracy: 0.9758

## 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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.314         | 1.0   | 176  | 0.1211          | 0.9612   |
| 0.2992        | 2.0   | 352  | 0.1186          | 0.9622   |
| 0.3544        | 3.0   | 528  | 0.0989          | 0.968    |
| 0.3068        | 4.0   | 704  | 0.0872          | 0.9724   |
| 0.3421        | 5.0   | 880  | 0.0858          | 0.972    |
| 0.2915        | 6.0   | 1056 | 0.0824          | 0.9724   |
| 0.3051        | 7.0   | 1232 | 0.0822          | 0.974    |
| 0.2849        | 8.0   | 1408 | 0.0770          | 0.975    |
| 0.2661        | 9.0   | 1584 | 0.0773          | 0.9756   |
| 0.2504        | 10.0  | 1760 | 0.0760          | 0.9758   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.0