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---
library_name: transformers
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
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: 0.9714285714285714
- name: Precision
type: precision
value: 0.9696825396825397
- name: Recall
type: recall
value: 0.9714285714285714
- name: F1
type: f1
value: 0.9695078031212484
---
<!-- 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: 0.0814
- Accuracy: 0.9714
- Precision: 0.9697
- Recall: 0.9714
- F1: 0.9695
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.2262 | 0.9888 | 22 | 0.2061 | 0.9365 | 0.8770 | 0.9365 | 0.9058 |
| 0.1666 | 1.9775 | 44 | 0.1274 | 0.9333 | 0.8769 | 0.9333 | 0.9042 |
| 0.1168 | 2.9663 | 66 | 0.1054 | 0.9524 | 0.9461 | 0.9524 | 0.9438 |
| 0.0984 | 4.0 | 89 | 0.0824 | 0.9619 | 0.9591 | 0.9619 | 0.9599 |
| 0.1028 | 4.9888 | 111 | 0.0814 | 0.9714 | 0.9697 | 0.9714 | 0.9695 |
| 0.1082 | 5.9775 | 133 | 0.0835 | 0.9492 | 0.9518 | 0.9492 | 0.9329 |
| 0.0962 | 6.9663 | 155 | 0.0872 | 0.9587 | 0.9578 | 0.9587 | 0.9582 |
| 0.0799 | 8.0 | 178 | 0.0803 | 0.9587 | 0.9543 | 0.9587 | 0.9546 |
| 0.0954 | 8.9888 | 200 | 0.0685 | 0.9619 | 0.9584 | 0.9619 | 0.9587 |
| 0.0771 | 9.8876 | 220 | 0.0711 | 0.9619 | 0.9584 | 0.9619 | 0.9587 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.0.2
- Tokenizers 0.19.1
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