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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: 0.9383116883116883
---
<!-- 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: 0.2469
- Accuracy: 0.9383
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9843 | 0.99 | 43 | 0.8500 | 0.6948 |
| 0.5335 | 2.0 | 87 | 0.5584 | 0.7825 |
| 0.4263 | 2.99 | 130 | 0.4791 | 0.8117 |
| 0.3308 | 4.0 | 174 | 0.4269 | 0.8344 |
| 0.2882 | 4.99 | 217 | 0.3567 | 0.8636 |
| 0.2517 | 6.0 | 261 | 0.3317 | 0.8701 |
| 0.1908 | 6.99 | 304 | 0.3082 | 0.8815 |
| 0.187 | 8.0 | 348 | 0.3230 | 0.8799 |
| 0.1434 | 8.99 | 391 | 0.3323 | 0.9010 |
| 0.1277 | 10.0 | 435 | 0.2489 | 0.9075 |
| 0.156 | 10.99 | 478 | 0.3246 | 0.8880 |
| 0.0781 | 12.0 | 522 | 0.3121 | 0.9010 |
| 0.1001 | 12.99 | 565 | 0.2708 | 0.9058 |
| 0.0892 | 14.0 | 609 | 0.2582 | 0.9140 |
| 0.0644 | 14.99 | 652 | 0.2486 | 0.9221 |
| 0.0689 | 16.0 | 696 | 0.2465 | 0.9237 |
| 0.0547 | 16.99 | 739 | 0.2402 | 0.9334 |
| 0.0597 | 18.0 | 783 | 0.2534 | 0.9237 |
| 0.0512 | 18.99 | 826 | 0.2400 | 0.9318 |
| 0.041 | 20.0 | 870 | 0.2397 | 0.9286 |
| 0.0376 | 20.99 | 913 | 0.2663 | 0.9269 |
| 0.0412 | 22.0 | 957 | 0.3026 | 0.9221 |
| 0.0423 | 22.99 | 1000 | 0.2678 | 0.9302 |
| 0.0266 | 24.0 | 1044 | 0.2510 | 0.9318 |
| 0.0313 | 24.99 | 1087 | 0.2542 | 0.9334 |
| 0.0207 | 26.0 | 1131 | 0.2743 | 0.9334 |
| 0.0292 | 26.99 | 1174 | 0.2614 | 0.9318 |
| 0.0242 | 28.0 | 1218 | 0.2469 | 0.9383 |
| 0.0201 | 28.99 | 1261 | 0.2534 | 0.9367 |
| 0.0354 | 29.66 | 1290 | 0.2525 | 0.9367 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
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