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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-woody
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: 0.7927272727272727
---
<!-- 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-finetuned-woody
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: 0.4349
- Accuracy: 0.7927
## 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: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.632 | 1.0 | 58 | 0.5883 | 0.6836 |
| 0.6067 | 2.0 | 116 | 0.6017 | 0.6848 |
| 0.5865 | 3.0 | 174 | 0.5695 | 0.7042 |
| 0.553 | 4.0 | 232 | 0.5185 | 0.7515 |
| 0.5468 | 5.0 | 290 | 0.5108 | 0.7430 |
| 0.5473 | 6.0 | 348 | 0.4882 | 0.7648 |
| 0.5381 | 7.0 | 406 | 0.4800 | 0.7588 |
| 0.5468 | 8.0 | 464 | 0.5056 | 0.7358 |
| 0.5191 | 9.0 | 522 | 0.4784 | 0.7673 |
| 0.5318 | 10.0 | 580 | 0.4762 | 0.7636 |
| 0.5079 | 11.0 | 638 | 0.4859 | 0.7673 |
| 0.5216 | 12.0 | 696 | 0.4691 | 0.7697 |
| 0.515 | 13.0 | 754 | 0.4857 | 0.7624 |
| 0.5186 | 14.0 | 812 | 0.4685 | 0.7733 |
| 0.4748 | 15.0 | 870 | 0.4536 | 0.7818 |
| 0.4853 | 16.0 | 928 | 0.4617 | 0.7770 |
| 0.4868 | 17.0 | 986 | 0.4622 | 0.7782 |
| 0.4572 | 18.0 | 1044 | 0.4583 | 0.7770 |
| 0.4679 | 19.0 | 1102 | 0.4590 | 0.7733 |
| 0.4508 | 20.0 | 1160 | 0.4576 | 0.7903 |
| 0.4663 | 21.0 | 1218 | 0.4542 | 0.7891 |
| 0.4533 | 22.0 | 1276 | 0.4428 | 0.7903 |
| 0.4892 | 23.0 | 1334 | 0.4372 | 0.7867 |
| 0.4704 | 24.0 | 1392 | 0.4414 | 0.7903 |
| 0.4304 | 25.0 | 1450 | 0.4430 | 0.7988 |
| 0.4411 | 26.0 | 1508 | 0.4348 | 0.7818 |
| 0.4604 | 27.0 | 1566 | 0.4387 | 0.7927 |
| 0.441 | 28.0 | 1624 | 0.4378 | 0.7964 |
| 0.442 | 29.0 | 1682 | 0.4351 | 0.7915 |
| 0.4585 | 30.0 | 1740 | 0.4349 | 0.7927 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.0
- Tokenizers 0.13.1
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