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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: 0.797979797979798
---
<!-- 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: 0.4489
- Accuracy: 0.7980
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6872 | 1.0 | 11 | 0.6535 | 0.6061 |
| 0.7287 | 2.0 | 22 | 0.6601 | 0.6397 |
| 0.7212 | 3.0 | 33 | 0.6740 | 0.5657 |
| 0.6947 | 4.0 | 44 | 0.6531 | 0.6532 |
| 0.6783 | 5.0 | 55 | 0.6739 | 0.5724 |
| 0.6816 | 6.0 | 66 | 0.6274 | 0.6599 |
| 0.6428 | 7.0 | 77 | 0.6671 | 0.6330 |
| 0.6928 | 8.0 | 88 | 0.6380 | 0.6498 |
| 0.6767 | 9.0 | 99 | 0.6875 | 0.6061 |
| 0.6918 | 10.0 | 110 | 0.6859 | 0.5690 |
| 0.6845 | 11.0 | 121 | 0.6810 | 0.5657 |
| 0.6826 | 12.0 | 132 | 0.6919 | 0.5185 |
| 0.6877 | 13.0 | 143 | 0.6693 | 0.6061 |
| 0.6709 | 14.0 | 154 | 0.6660 | 0.5690 |
| 0.6707 | 15.0 | 165 | 0.6764 | 0.5690 |
| 0.6703 | 16.0 | 176 | 0.6467 | 0.6296 |
| 0.6629 | 17.0 | 187 | 0.6471 | 0.6431 |
| 0.6557 | 18.0 | 198 | 0.6597 | 0.6229 |
| 0.659 | 19.0 | 209 | 0.6451 | 0.6027 |
| 0.65 | 20.0 | 220 | 0.6638 | 0.6094 |
| 0.6453 | 21.0 | 231 | 0.6544 | 0.6162 |
| 0.6426 | 22.0 | 242 | 0.6565 | 0.5825 |
| 0.6339 | 23.0 | 253 | 0.6743 | 0.6296 |
| 0.6236 | 24.0 | 264 | 0.6669 | 0.5960 |
| 0.6427 | 25.0 | 275 | 0.6379 | 0.6532 |
| 0.6439 | 26.0 | 286 | 0.6361 | 0.6263 |
| 0.6212 | 27.0 | 297 | 0.6540 | 0.6465 |
| 0.6186 | 28.0 | 308 | 0.5925 | 0.6700 |
| 0.6162 | 29.0 | 319 | 0.6224 | 0.6734 |
| 0.6237 | 30.0 | 330 | 0.6018 | 0.6667 |
| 0.6061 | 31.0 | 341 | 0.5735 | 0.6801 |
| 0.6138 | 32.0 | 352 | 0.6425 | 0.6566 |
| 0.595 | 33.0 | 363 | 0.5827 | 0.6768 |
| 0.5869 | 34.0 | 374 | 0.5956 | 0.7172 |
| 0.577 | 35.0 | 385 | 0.5458 | 0.7003 |
| 0.5766 | 36.0 | 396 | 0.5603 | 0.6869 |
| 0.5726 | 37.0 | 407 | 0.5339 | 0.7340 |
| 0.5702 | 38.0 | 418 | 0.5577 | 0.7138 |
| 0.5762 | 39.0 | 429 | 0.5262 | 0.7374 |
| 0.5543 | 40.0 | 440 | 0.5091 | 0.7441 |
| 0.5339 | 41.0 | 451 | 0.5185 | 0.7542 |
| 0.5428 | 42.0 | 462 | 0.5023 | 0.7542 |
| 0.5349 | 43.0 | 473 | 0.5439 | 0.7306 |
| 0.5319 | 44.0 | 484 | 0.4745 | 0.7811 |
| 0.5294 | 45.0 | 495 | 0.5432 | 0.7172 |
| 0.5314 | 46.0 | 506 | 0.4511 | 0.7912 |
| 0.5073 | 47.0 | 517 | 0.4379 | 0.8047 |
| 0.5028 | 48.0 | 528 | 0.4487 | 0.7980 |
| 0.4985 | 49.0 | 539 | 0.4550 | 0.7946 |
| 0.4826 | 50.0 | 550 | 0.4489 | 0.7980 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
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