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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-blank_img
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: 0.9448669201520913
---
<!-- 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-blank_img
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: 0.1727
- Accuracy: 0.9449
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2646 | 1.0 | 74 | 0.1974 | 0.9392 |
| 0.2287 | 2.0 | 148 | 0.1979 | 0.9354 |
| 0.198 | 3.0 | 222 | 0.1727 | 0.9449 |
| 0.1889 | 4.0 | 296 | 0.1747 | 0.9430 |
| 0.223 | 5.0 | 370 | 0.1711 | 0.9449 |
| 0.1771 | 6.0 | 444 | 0.1697 | 0.9382 |
| 0.1864 | 7.0 | 518 | 0.1672 | 0.9392 |
| 0.1716 | 8.0 | 592 | 0.1801 | 0.9430 |
| 0.192 | 9.0 | 666 | 0.1754 | 0.9411 |
| 0.1886 | 10.0 | 740 | 0.1766 | 0.9420 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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