---
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-cifar10
  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: 0.9858
---

<!-- 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-cifar10

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: 0.0414
- Accuracy: 0.9858

## 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: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.303         | 0.03  | 10   | 2.1672          | 0.2334   |
| 2.0158        | 0.06  | 20   | 1.6672          | 0.657    |
| 1.4855        | 0.09  | 30   | 0.8292          | 0.8704   |
| 0.7451        | 0.11  | 40   | 0.2578          | 0.93     |
| 0.5618        | 0.14  | 50   | 0.1476          | 0.962    |
| 0.4545        | 0.17  | 60   | 0.1248          | 0.9642   |
| 0.4587        | 0.2   | 70   | 0.0941          | 0.9748   |
| 0.3911        | 0.23  | 80   | 0.0944          | 0.9712   |
| 0.3839        | 0.26  | 90   | 0.0848          | 0.9756   |
| 0.3864        | 0.28  | 100  | 0.0744          | 0.978    |
| 0.3141        | 0.31  | 110  | 0.0673          | 0.98     |
| 0.3764        | 0.34  | 120  | 0.0706          | 0.9764   |
| 0.3003        | 0.37  | 130  | 0.0600          | 0.984    |
| 0.3566        | 0.4   | 140  | 0.0562          | 0.9826   |
| 0.2855        | 0.43  | 150  | 0.0567          | 0.9816   |
| 0.3351        | 0.45  | 160  | 0.0543          | 0.9828   |
| 0.2977        | 0.48  | 170  | 0.0568          | 0.9798   |
| 0.2924        | 0.51  | 180  | 0.0577          | 0.9804   |
| 0.2884        | 0.54  | 190  | 0.0551          | 0.983    |
| 0.3067        | 0.57  | 200  | 0.0487          | 0.983    |
| 0.3159        | 0.6   | 210  | 0.0513          | 0.984    |
| 0.2795        | 0.63  | 220  | 0.0460          | 0.9846   |
| 0.3113        | 0.65  | 230  | 0.0495          | 0.9832   |
| 0.2882        | 0.68  | 240  | 0.0475          | 0.9838   |
| 0.263         | 0.71  | 250  | 0.0449          | 0.9854   |
| 0.2686        | 0.74  | 260  | 0.0510          | 0.9826   |
| 0.2705        | 0.77  | 270  | 0.0483          | 0.9846   |
| 0.2807        | 0.8   | 280  | 0.0430          | 0.9854   |
| 0.2583        | 0.82  | 290  | 0.0452          | 0.9858   |
| 0.2346        | 0.85  | 300  | 0.0435          | 0.9858   |
| 0.2294        | 0.88  | 310  | 0.0434          | 0.986    |
| 0.2608        | 0.91  | 320  | 0.0433          | 0.986    |
| 0.2642        | 0.94  | 330  | 0.0425          | 0.9866   |
| 0.2781        | 0.97  | 340  | 0.0417          | 0.986    |
| 0.247         | 1.0   | 350  | 0.0414          | 0.9858   |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.1
- Datasets 2.14.6
- Tokenizers 0.14.1