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---
license: apache-2.0
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: 0.7302889760970389
---
<!-- 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: 0.5574
- Accuracy: 0.7303
## 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: 256
- eval_batch_size: 256
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 1024
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6271 | 0.99 | 98 | 0.6035 | 0.6926 |
| 0.6156 | 1.99 | 197 | 0.5844 | 0.7006 |
| 0.6148 | 3.0 | 296 | 0.5758 | 0.7104 |
| 0.6055 | 4.0 | 395 | 0.5853 | 0.7015 |
| 0.5938 | 4.99 | 493 | 0.5858 | 0.7104 |
| 0.5878 | 5.99 | 592 | 0.5630 | 0.7210 |
| 0.5873 | 7.0 | 691 | 0.5620 | 0.7236 |
| 0.5947 | 8.0 | 790 | 0.5670 | 0.7196 |
| 0.5866 | 8.99 | 888 | 0.5592 | 0.7265 |
| 0.5807 | 9.99 | 987 | 0.5574 | 0.7254 |
| 0.5764 | 11.0 | 1086 | 0.5655 | 0.7245 |
| 0.5729 | 12.0 | 1185 | 0.5611 | 0.7237 |
| 0.577 | 12.99 | 1283 | 0.5702 | 0.7189 |
| 0.5702 | 13.99 | 1382 | 0.5588 | 0.7259 |
| 0.5717 | 15.0 | 1481 | 0.5565 | 0.7244 |
| 0.5646 | 16.0 | 1580 | 0.5536 | 0.7303 |
| 0.5591 | 16.99 | 1678 | 0.5525 | 0.7345 |
| 0.5586 | 17.99 | 1777 | 0.5565 | 0.7286 |
| 0.5668 | 19.0 | 1876 | 0.5520 | 0.7304 |
| 0.5617 | 20.0 | 1975 | 0.5557 | 0.7289 |
| 0.5546 | 20.99 | 2073 | 0.5561 | 0.7325 |
| 0.5579 | 21.99 | 2172 | 0.5537 | 0.7314 |
| 0.5604 | 23.0 | 2271 | 0.5545 | 0.7290 |
| 0.5563 | 24.0 | 2370 | 0.5591 | 0.7288 |
| 0.5634 | 24.99 | 2468 | 0.5546 | 0.7307 |
| 0.5563 | 25.99 | 2567 | 0.5557 | 0.7303 |
| 0.5563 | 27.0 | 2666 | 0.5571 | 0.7276 |
| 0.5544 | 28.0 | 2765 | 0.5551 | 0.7298 |
| 0.5491 | 28.99 | 2863 | 0.5596 | 0.7282 |
| 0.5461 | 29.77 | 2940 | 0.5574 | 0.7303 |
### Framework versions
- Transformers 4.29.1
- Pytorch 2.0.1
- Datasets 2.13.1
- Tokenizers 0.13.3
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