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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-finetuned-sealv1
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: 0.9793103448275862
---
<!-- 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-sealv1
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: 0.0650
- Accuracy: 0.9793
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.95 | 5 | 1.2920 | 0.4966 |
| 1.1379 | 1.9 | 10 | 1.0177 | 0.4966 |
| 1.1379 | 2.86 | 15 | 0.7626 | 0.8759 |
| 0.6784 | 4.0 | 21 | 0.5388 | 0.9310 |
| 0.6784 | 4.95 | 26 | 0.4191 | 0.9103 |
| 0.3269 | 5.9 | 31 | 0.3990 | 0.8897 |
| 0.3269 | 6.86 | 36 | 0.2090 | 0.9517 |
| 0.2068 | 8.0 | 42 | 0.1819 | 0.9586 |
| 0.2068 | 8.95 | 47 | 0.1192 | 0.9655 |
| 0.1104 | 9.9 | 52 | 0.0682 | 0.9724 |
| 0.1104 | 10.86 | 57 | 0.0854 | 0.9724 |
| 0.0571 | 12.0 | 63 | 0.0816 | 0.9655 |
| 0.0571 | 12.95 | 68 | 0.0535 | 0.9793 |
| 0.0382 | 13.9 | 73 | 0.0491 | 0.9793 |
| 0.0382 | 14.86 | 78 | 0.0534 | 0.9793 |
| 0.0158 | 16.0 | 84 | 0.0369 | 0.9793 |
| 0.0158 | 16.95 | 89 | 0.1111 | 0.9724 |
| 0.0082 | 17.9 | 94 | 0.0515 | 0.9862 |
| 0.0082 | 18.86 | 99 | 0.0713 | 0.9793 |
| 0.0105 | 20.0 | 105 | 0.0598 | 0.9793 |
| 0.009 | 20.95 | 110 | 0.0759 | 0.9724 |
| 0.009 | 21.9 | 115 | 0.0769 | 0.9793 |
| 0.0134 | 22.86 | 120 | 0.0702 | 0.9793 |
| 0.0134 | 24.0 | 126 | 0.0605 | 0.9793 |
| 0.0042 | 24.95 | 131 | 0.0621 | 0.9793 |
| 0.0042 | 25.9 | 136 | 0.0654 | 0.9793 |
| 0.0027 | 26.86 | 141 | 0.0666 | 0.9724 |
| 0.0027 | 28.0 | 147 | 0.0665 | 0.9793 |
| 0.0065 | 28.57 | 150 | 0.0650 | 0.9793 |
### Framework versions
- Transformers 4.38.2
- Pytorch 1.10.2+cu113
- Datasets 2.18.0
- Tokenizers 0.15.2
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