---
tags:
- generated_from_trainer
datasets:
- cifar100
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-cifar_100f_from_10
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: cifar100
      type: cifar100
      config: cifar100
      split: train
      args: cifar100
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.5582
---

<!-- 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-cifar_100f_from_10

This model was trained from scratch on the cifar100 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1818
- Accuracy: 0.5582

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.4964        | 1.0   | 351  | 3.1548          | 0.3374   |
| 2.8648        | 2.0   | 703  | 2.3713          | 0.524    |
| 2.758         | 2.99  | 1053 | 2.1818          | 0.5582   |


### Framework versions

- Transformers 4.30.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3