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---
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-PE
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: 0.5833333333333334
---
<!-- 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-PE
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: 0.6756
- Accuracy: 0.5833
## 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: 0.0025
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5675 | 0.99 | 20 | 0.5504 | 0.7463 |
| 0.7158 | 1.98 | 40 | 0.9070 | 0.5944 |
| 0.6498 | 2.96 | 60 | 0.6501 | 0.6037 |
| 0.6405 | 4.0 | 81 | 0.5655 | 0.7389 |
| 0.7003 | 4.99 | 101 | 0.6786 | 0.5907 |
| 0.6857 | 5.98 | 121 | 0.6820 | 0.5370 |
| 0.6933 | 6.96 | 141 | 0.6819 | 0.5926 |
| 0.6795 | 8.0 | 162 | 0.6783 | 0.5481 |
| 0.6872 | 8.99 | 182 | 0.6907 | 0.5370 |
| 0.6942 | 9.98 | 202 | 0.6922 | 0.5407 |
| 0.6945 | 10.96 | 222 | 0.6935 | 0.4630 |
| 0.6936 | 12.0 | 243 | 0.6974 | 0.4630 |
| 0.6935 | 12.99 | 263 | 0.6907 | 0.5407 |
| 0.6925 | 13.98 | 283 | 0.6945 | 0.4241 |
| 0.6927 | 14.96 | 303 | 0.6952 | 0.4630 |
| 0.6921 | 16.0 | 324 | 0.6901 | 0.5463 |
| 0.6937 | 16.99 | 344 | 0.6935 | 0.4407 |
| 0.6933 | 17.98 | 364 | 0.6922 | 0.5537 |
| 0.6929 | 18.96 | 384 | 0.6971 | 0.4630 |
| 0.6919 | 20.0 | 405 | 0.6901 | 0.5630 |
| 0.6903 | 20.99 | 425 | 0.6850 | 0.5722 |
| 0.6892 | 21.98 | 445 | 0.6876 | 0.5611 |
| 0.6846 | 22.96 | 465 | 0.6871 | 0.5463 |
| 0.6841 | 24.0 | 486 | 0.6742 | 0.5685 |
| 0.682 | 24.99 | 506 | 0.6776 | 0.5741 |
| 0.6796 | 25.98 | 526 | 0.6850 | 0.5407 |
| 0.6849 | 26.96 | 546 | 0.6722 | 0.5907 |
| 0.6855 | 28.0 | 567 | 0.6818 | 0.5648 |
| 0.6903 | 28.99 | 587 | 0.7024 | 0.4685 |
| 0.6845 | 29.98 | 607 | 0.6781 | 0.5630 |
| 0.6806 | 30.96 | 627 | 0.6771 | 0.5778 |
| 0.6808 | 32.0 | 648 | 0.6718 | 0.5833 |
| 0.6811 | 32.99 | 668 | 0.6715 | 0.5833 |
| 0.6814 | 33.98 | 688 | 0.6641 | 0.6370 |
| 0.6848 | 34.96 | 708 | 0.6736 | 0.6111 |
| 0.6848 | 36.0 | 729 | 0.6694 | 0.6259 |
| 0.6848 | 36.99 | 749 | 0.6757 | 0.5907 |
| 0.6865 | 37.98 | 769 | 0.6763 | 0.5667 |
| 0.6876 | 38.96 | 789 | 0.6812 | 0.5889 |
| 0.6858 | 40.0 | 810 | 0.6763 | 0.5926 |
| 0.6863 | 40.99 | 830 | 0.6743 | 0.5981 |
| 0.6838 | 41.98 | 850 | 0.6740 | 0.5796 |
| 0.6833 | 42.96 | 870 | 0.6770 | 0.5611 |
| 0.6883 | 44.0 | 891 | 0.6733 | 0.6037 |
| 0.684 | 44.99 | 911 | 0.6730 | 0.6019 |
| 0.6869 | 45.98 | 931 | 0.6731 | 0.6130 |
| 0.6861 | 46.96 | 951 | 0.6752 | 0.5704 |
| 0.686 | 48.0 | 972 | 0.6761 | 0.5704 |
| 0.683 | 48.99 | 992 | 0.6759 | 0.5722 |
| 0.6847 | 49.38 | 1000 | 0.6756 | 0.5833 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu117
- Datasets 2.15.0
- Tokenizers 0.15.0
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