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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: 0.8532818532818532
---
<!-- 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: 0.6144
- Accuracy: 0.8533
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1952 | 0.99 | 18 | 1.5914 | 0.5985 |
| 1.3705 | 1.97 | 36 | 1.2164 | 0.6873 |
| 1.026 | 2.96 | 54 | 0.9974 | 0.7375 |
| 0.829 | 4.0 | 73 | 0.7667 | 0.7722 |
| 0.6513 | 4.99 | 91 | 0.6674 | 0.8224 |
| 0.5516 | 5.97 | 109 | 0.5810 | 0.8378 |
| 0.4978 | 6.96 | 127 | 0.5498 | 0.8263 |
| 0.4568 | 8.0 | 146 | 0.5999 | 0.8185 |
| 0.4047 | 8.99 | 164 | 0.5211 | 0.8494 |
| 0.3696 | 9.97 | 182 | 0.5201 | 0.8571 |
| 0.3479 | 10.96 | 200 | 0.5310 | 0.8263 |
| 0.329 | 12.0 | 219 | 0.5439 | 0.8494 |
| 0.3376 | 12.99 | 237 | 0.5050 | 0.8494 |
| 0.2804 | 13.97 | 255 | 0.5709 | 0.8263 |
| 0.2941 | 14.96 | 273 | 0.6376 | 0.8147 |
| 0.3026 | 16.0 | 292 | 0.5447 | 0.8494 |
| 0.2578 | 16.99 | 310 | 0.5056 | 0.8803 |
| 0.219 | 17.97 | 328 | 0.5620 | 0.8610 |
| 0.2403 | 18.96 | 346 | 0.5582 | 0.8456 |
| 0.2258 | 20.0 | 365 | 0.5458 | 0.8494 |
| 0.2265 | 20.99 | 383 | 0.5411 | 0.8533 |
| 0.1893 | 21.97 | 401 | 0.5477 | 0.8494 |
| 0.1896 | 22.96 | 419 | 0.5125 | 0.8494 |
| 0.1976 | 24.0 | 438 | 0.5672 | 0.8340 |
| 0.1725 | 24.99 | 456 | 0.5581 | 0.8456 |
| 0.168 | 25.97 | 474 | 0.5965 | 0.8456 |
| 0.1821 | 26.96 | 492 | 0.5567 | 0.8610 |
| 0.1805 | 28.0 | 511 | 0.5998 | 0.8533 |
| 0.1616 | 28.99 | 529 | 0.5451 | 0.8533 |
| 0.1467 | 29.97 | 547 | 0.5574 | 0.8494 |
| 0.1439 | 30.96 | 565 | 0.5707 | 0.8571 |
| 0.13 | 32.0 | 584 | 0.6019 | 0.8378 |
| 0.1353 | 32.99 | 602 | 0.5952 | 0.8610 |
| 0.1329 | 33.97 | 620 | 0.6262 | 0.8378 |
| 0.1258 | 34.96 | 638 | 0.6314 | 0.8456 |
| 0.1408 | 36.0 | 657 | 0.5761 | 0.8494 |
| 0.1197 | 36.99 | 675 | 0.5703 | 0.8610 |
| 0.1208 | 37.97 | 693 | 0.6247 | 0.8456 |
| 0.1197 | 38.96 | 711 | 0.6026 | 0.8533 |
| 0.1271 | 40.0 | 730 | 0.5953 | 0.8533 |
| 0.1053 | 40.99 | 748 | 0.6070 | 0.8533 |
| 0.0846 | 41.97 | 766 | 0.6094 | 0.8610 |
| 0.1206 | 42.96 | 784 | 0.5912 | 0.8494 |
| 0.1225 | 44.0 | 803 | 0.6074 | 0.8494 |
| 0.1184 | 44.99 | 821 | 0.5943 | 0.8494 |
| 0.1027 | 45.97 | 839 | 0.6084 | 0.8494 |
| 0.1113 | 46.96 | 857 | 0.6034 | 0.8533 |
| 0.0945 | 48.0 | 876 | 0.6106 | 0.8494 |
| 0.1159 | 48.99 | 894 | 0.6143 | 0.8533 |
| 0.0963 | 49.32 | 900 | 0.6144 | 0.8533 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
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