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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: 0.796756082345602
---
<!-- 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: 0.6838
- Accuracy: 0.7968
## 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.5891 | 0.9966 | 218 | 1.3833 | 0.5723 |
| 1.2997 | 1.9977 | 437 | 1.0831 | 0.6700 |
| 1.1166 | 2.9989 | 656 | 0.9937 | 0.6958 |
| 1.0464 | 4.0 | 875 | 0.9180 | 0.7231 |
| 0.982 | 4.9966 | 1093 | 0.8399 | 0.7432 |
| 0.9472 | 5.9977 | 1312 | 0.8127 | 0.7536 |
| 0.8751 | 6.9989 | 1531 | 0.7852 | 0.7639 |
| 0.9107 | 8.0 | 1750 | 0.7644 | 0.7713 |
| 0.8464 | 8.9966 | 1968 | 0.7322 | 0.7830 |
| 0.8398 | 9.9977 | 2187 | 0.7243 | 0.7798 |
| 0.7534 | 10.9989 | 2406 | 0.7088 | 0.7845 |
| 0.7051 | 12.0 | 2625 | 0.6982 | 0.7935 |
| 0.7359 | 12.9966 | 2843 | 0.6985 | 0.7916 |
| 0.7641 | 13.9977 | 3062 | 0.6838 | 0.7968 |
| 0.7372 | 14.9486 | 3270 | 0.6781 | 0.7968 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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