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---
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
model-index:
- name: swin-tiny-patch4-window7-224
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8933333333333333
- name: Precision
type: precision
value: 0.8772576832151301
- name: Recall
type: recall
value: 0.8933333333333333
---
<!-- 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
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: 0.2912
- Accuracy: 0.8933
- Precision: 0.8773
- Recall: 0.8933
- F1 Score: 0.8762
## 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
| No log | 1.0 | 4 | 0.4588 | 0.8708 | 0.7584 | 0.8708 | 0.8107 |
| No log | 2.0 | 8 | 0.3854 | 0.8708 | 0.7584 | 0.8708 | 0.8107 |
| No log | 3.0 | 12 | 0.4070 | 0.8708 | 0.7584 | 0.8708 | 0.8107 |
| 0.4953 | 4.0 | 16 | 0.3890 | 0.8708 | 0.7584 | 0.8708 | 0.8107 |
| 0.4953 | 5.0 | 20 | 0.3688 | 0.8708 | 0.7584 | 0.8708 | 0.8107 |
| 0.4953 | 6.0 | 24 | 0.3549 | 0.8708 | 0.7584 | 0.8708 | 0.8107 |
| 0.4953 | 7.0 | 28 | 0.3138 | 0.8708 | 0.7584 | 0.8708 | 0.8107 |
| 0.4217 | 8.0 | 32 | 0.3330 | 0.8708 | 0.8312 | 0.8708 | 0.8308 |
| 0.4217 | 9.0 | 36 | 0.2946 | 0.9 | 0.8881 | 0.9 | 0.8845 |
| 0.4217 | 10.0 | 40 | 0.2753 | 0.9042 | 0.8938 | 0.9042 | 0.8905 |
| 0.4217 | 11.0 | 44 | 0.2996 | 0.9 | 0.8909 | 0.9 | 0.8935 |
| 0.3747 | 12.0 | 48 | 0.2684 | 0.9 | 0.8883 | 0.9 | 0.8894 |
| 0.3747 | 13.0 | 52 | 0.2670 | 0.9 | 0.8883 | 0.9 | 0.8894 |
| 0.3747 | 14.0 | 56 | 0.2722 | 0.9042 | 0.8940 | 0.9042 | 0.8951 |
| 0.3579 | 15.0 | 60 | 0.2718 | 0.9042 | 0.8940 | 0.9042 | 0.8951 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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