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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: 0.7433333333333333
- name: Precision
type: precision
value: 0.7306273291925466
- name: Recall
type: recall
value: 0.7433333333333333
---
<!-- 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: 0.5534
- Accuracy: 0.7433
- Precision: 0.7306
- Recall: 0.7433
- F1 Score: 0.7344
## 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
| No log | 1.0 | 4 | 0.7306 | 0.4 | 0.6521 | 0.4 | 0.3821 |
| No log | 2.0 | 8 | 0.5815 | 0.7333 | 0.8050 | 0.7333 | 0.6286 |
| No log | 3.0 | 12 | 0.5700 | 0.725 | 0.5256 | 0.725 | 0.6094 |
| No log | 4.0 | 16 | 0.5635 | 0.725 | 0.5256 | 0.725 | 0.6094 |
| No log | 5.0 | 20 | 0.5509 | 0.7292 | 0.8028 | 0.7292 | 0.6191 |
| No log | 6.0 | 24 | 0.5356 | 0.7417 | 0.7438 | 0.7417 | 0.6589 |
| No log | 7.0 | 28 | 0.5353 | 0.75 | 0.7360 | 0.75 | 0.6895 |
| No log | 8.0 | 32 | 0.5299 | 0.7375 | 0.7090 | 0.7375 | 0.6668 |
| No log | 9.0 | 36 | 0.5335 | 0.7667 | 0.7509 | 0.7667 | 0.7310 |
| No log | 10.0 | 40 | 0.5344 | 0.7417 | 0.7315 | 0.7417 | 0.6644 |
| No log | 11.0 | 44 | 0.5297 | 0.7458 | 0.7279 | 0.7458 | 0.6821 |
| No log | 12.0 | 48 | 0.5202 | 0.75 | 0.7360 | 0.75 | 0.6895 |
| 0.5942 | 13.0 | 52 | 0.5325 | 0.7542 | 0.7411 | 0.7542 | 0.7452 |
| 0.5942 | 14.0 | 56 | 0.5139 | 0.7583 | 0.7505 | 0.7583 | 0.7039 |
| 0.5942 | 15.0 | 60 | 0.5528 | 0.7417 | 0.7347 | 0.7417 | 0.7377 |
| 0.5942 | 16.0 | 64 | 0.5070 | 0.7625 | 0.7437 | 0.7625 | 0.7277 |
| 0.5942 | 17.0 | 68 | 0.5193 | 0.775 | 0.7594 | 0.775 | 0.7592 |
| 0.5942 | 18.0 | 72 | 0.5090 | 0.7583 | 0.7448 | 0.7583 | 0.7487 |
| 0.5942 | 19.0 | 76 | 0.5189 | 0.7792 | 0.7847 | 0.7792 | 0.7816 |
| 0.5942 | 20.0 | 80 | 0.5214 | 0.775 | 0.7795 | 0.775 | 0.7770 |
| 0.5942 | 21.0 | 84 | 0.5188 | 0.775 | 0.7710 | 0.775 | 0.7728 |
| 0.5942 | 22.0 | 88 | 0.5029 | 0.7667 | 0.7526 | 0.7667 | 0.7557 |
| 0.5942 | 23.0 | 92 | 0.5061 | 0.7833 | 0.7734 | 0.7833 | 0.7761 |
| 0.5942 | 24.0 | 96 | 0.5350 | 0.7667 | 0.7713 | 0.7667 | 0.7687 |
| 0.4829 | 25.0 | 100 | 0.5149 | 0.7542 | 0.7330 | 0.7542 | 0.7337 |
| 0.4829 | 26.0 | 104 | 0.5283 | 0.7583 | 0.7737 | 0.7583 | 0.7641 |
| 0.4829 | 27.0 | 108 | 0.5109 | 0.7792 | 0.7647 | 0.7792 | 0.7646 |
| 0.4829 | 28.0 | 112 | 0.5258 | 0.775 | 0.7729 | 0.775 | 0.7739 |
| 0.4829 | 29.0 | 116 | 0.5207 | 0.7625 | 0.745 | 0.7625 | 0.7468 |
| 0.4829 | 30.0 | 120 | 0.5306 | 0.75 | 0.7357 | 0.75 | 0.7400 |
| 0.4829 | 31.0 | 124 | 0.5455 | 0.75 | 0.7375 | 0.75 | 0.7417 |
| 0.4829 | 32.0 | 128 | 0.5653 | 0.7458 | 0.7380 | 0.7458 | 0.7412 |
| 0.4829 | 33.0 | 132 | 0.5565 | 0.7417 | 0.7212 | 0.7417 | 0.7256 |
| 0.4829 | 34.0 | 136 | 0.5468 | 0.7708 | 0.7658 | 0.7708 | 0.7679 |
| 0.4829 | 35.0 | 140 | 0.5268 | 0.7833 | 0.7723 | 0.7833 | 0.7747 |
| 0.4829 | 36.0 | 144 | 0.5260 | 0.775 | 0.7710 | 0.775 | 0.7728 |
| 0.4829 | 37.0 | 148 | 0.5281 | 0.775 | 0.7659 | 0.775 | 0.7689 |
| 0.3846 | 38.0 | 152 | 0.5385 | 0.7708 | 0.7742 | 0.7708 | 0.7724 |
| 0.3846 | 39.0 | 156 | 0.5253 | 0.7708 | 0.7623 | 0.7708 | 0.7653 |
| 0.3846 | 40.0 | 160 | 0.5319 | 0.7708 | 0.7719 | 0.7708 | 0.7714 |
| 0.3846 | 41.0 | 164 | 0.5311 | 0.775 | 0.7631 | 0.775 | 0.7660 |
| 0.3846 | 42.0 | 168 | 0.5325 | 0.7792 | 0.7683 | 0.7792 | 0.7711 |
| 0.3846 | 43.0 | 172 | 0.5254 | 0.7667 | 0.7606 | 0.7667 | 0.7631 |
| 0.3846 | 44.0 | 176 | 0.5232 | 0.7708 | 0.7623 | 0.7708 | 0.7653 |
| 0.3846 | 45.0 | 180 | 0.5291 | 0.7708 | 0.7640 | 0.7708 | 0.7667 |
| 0.3846 | 46.0 | 184 | 0.5356 | 0.7708 | 0.7607 | 0.7708 | 0.7639 |
| 0.3846 | 47.0 | 188 | 0.5400 | 0.7708 | 0.7607 | 0.7708 | 0.7639 |
| 0.3846 | 48.0 | 192 | 0.5409 | 0.7667 | 0.7540 | 0.7667 | 0.7573 |
| 0.3846 | 49.0 | 196 | 0.5403 | 0.7667 | 0.7540 | 0.7667 | 0.7573 |
| 0.3353 | 50.0 | 200 | 0.5397 | 0.7708 | 0.7592 | 0.7708 | 0.7624 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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