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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: 0.9503105590062112
---
<!-- 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: 0.1879
- Accuracy: 0.9503
## 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.6144 | 0.96 | 11 | 1.0071 | 0.8447 |
| 0.8116 | 2.0 | 23 | 0.5227 | 0.8571 |
| 0.6078 | 2.96 | 34 | 0.4213 | 0.8571 |
| 0.5151 | 4.0 | 46 | 0.3357 | 0.8758 |
| 0.4499 | 4.96 | 57 | 0.3467 | 0.9068 |
| 0.4254 | 6.0 | 69 | 0.2344 | 0.9193 |
| 0.3266 | 6.96 | 80 | 0.2107 | 0.9379 |
| 0.3018 | 8.0 | 92 | 0.1818 | 0.9379 |
| 0.3339 | 8.96 | 103 | 0.1928 | 0.9379 |
| 0.2594 | 10.0 | 115 | 0.1936 | 0.9317 |
| 0.2476 | 10.96 | 126 | 0.1543 | 0.9317 |
| 0.2294 | 12.0 | 138 | 0.1827 | 0.9441 |
| 0.2193 | 12.96 | 149 | 0.1676 | 0.9317 |
| 0.1924 | 14.0 | 161 | 0.1553 | 0.9379 |
| 0.2148 | 14.96 | 172 | 0.1387 | 0.9379 |
| 0.1674 | 16.0 | 184 | 0.1449 | 0.9379 |
| 0.1815 | 16.96 | 195 | 0.1833 | 0.9317 |
| 0.1861 | 18.0 | 207 | 0.1818 | 0.9441 |
| 0.1629 | 18.96 | 218 | 0.2484 | 0.9255 |
| 0.1609 | 20.0 | 230 | 0.1661 | 0.9503 |
| 0.132 | 20.96 | 241 | 0.1538 | 0.9441 |
| 0.1468 | 22.0 | 253 | 0.1597 | 0.9565 |
| 0.0926 | 22.96 | 264 | 0.1613 | 0.9565 |
| 0.102 | 24.0 | 276 | 0.1420 | 0.9441 |
| 0.1178 | 24.96 | 287 | 0.1429 | 0.9441 |
| 0.1311 | 26.0 | 299 | 0.1832 | 0.9503 |
| 0.0982 | 26.96 | 310 | 0.2140 | 0.9441 |
| 0.0865 | 28.0 | 322 | 0.2040 | 0.9565 |
| 0.0919 | 28.96 | 333 | 0.1878 | 0.9503 |
| 0.085 | 30.0 | 345 | 0.1935 | 0.9565 |
| 0.0918 | 30.96 | 356 | 0.1787 | 0.9503 |
| 0.0939 | 32.0 | 368 | 0.1932 | 0.9441 |
| 0.1236 | 32.96 | 379 | 0.1736 | 0.9379 |
| 0.0819 | 34.0 | 391 | 0.1798 | 0.9503 |
| 0.0906 | 34.96 | 402 | 0.1937 | 0.9379 |
| 0.0865 | 36.0 | 414 | 0.1809 | 0.9379 |
| 0.0709 | 36.96 | 425 | 0.2062 | 0.9379 |
| 0.0781 | 38.0 | 437 | 0.1749 | 0.9503 |
| 0.0772 | 38.96 | 448 | 0.2176 | 0.9441 |
| 0.0535 | 40.0 | 460 | 0.2164 | 0.9503 |
| 0.0608 | 40.96 | 471 | 0.1976 | 0.9503 |
| 0.072 | 42.0 | 483 | 0.1837 | 0.9441 |
| 0.0657 | 42.96 | 494 | 0.2000 | 0.9565 |
| 0.0824 | 44.0 | 506 | 0.1865 | 0.9503 |
| 0.0584 | 44.96 | 517 | 0.1870 | 0.9565 |
| 0.0556 | 46.0 | 529 | 0.1863 | 0.9503 |
| 0.0516 | 46.96 | 540 | 0.1894 | 0.9503 |
| 0.06 | 47.83 | 550 | 0.1879 | 0.9503 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
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