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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- precision
- recall
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: 0.9844444444444445
- name: F1
type: f1
value: 0.9844678306487884
- name: Precision
type: precision
value: 0.9846508141836958
- name: Recall
type: recall
value: 0.9844444444444445
---
<!-- 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: 0.0393
- Accuracy: 0.9844
- F1: 0.9845
- Precision: 0.9847
- Recall: 0.9844
## 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: 0.0001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.3039 | 1.0 | 95 | 0.1300 | 0.9607 | 0.9609 | 0.9619 | 0.9607 |
| 0.2357 | 2.0 | 190 | 0.0815 | 0.9678 | 0.9678 | 0.9685 | 0.9678 |
| 0.163 | 3.0 | 285 | 0.0559 | 0.9807 | 0.9807 | 0.9809 | 0.9807 |
| 0.1267 | 4.0 | 380 | 0.0492 | 0.9837 | 0.9837 | 0.9839 | 0.9837 |
| 0.1059 | 5.0 | 475 | 0.0393 | 0.9844 | 0.9845 | 0.9847 | 0.9844 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|