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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-eurosat
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: 0.9100719424460432
---
<!-- 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-eurosat
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: 0.2633
- Accuracy: 0.9101
## 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: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8026 | 0.96 | 19 | 0.9313 | 0.5612 |
| 0.7571 | 1.97 | 39 | 0.8835 | 0.5755 |
| 0.7061 | 2.99 | 59 | 0.7589 | 0.6871 |
| 0.5911 | 4.0 | 79 | 0.6329 | 0.7482 |
| 0.5194 | 4.96 | 98 | 0.5634 | 0.7698 |
| 0.4471 | 5.97 | 118 | 0.4552 | 0.8165 |
| 0.3743 | 6.99 | 138 | 0.3760 | 0.8525 |
| 0.3686 | 8.0 | 158 | 0.3233 | 0.8705 |
| 0.318 | 8.96 | 177 | 0.3141 | 0.8777 |
| 0.3163 | 9.97 | 197 | 0.2772 | 0.8993 |
| 0.2871 | 10.99 | 217 | 0.2707 | 0.9029 |
| 0.2909 | 11.54 | 228 | 0.2633 | 0.9101 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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