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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: 0.8842443729903537
- name: Precision
type: precision
value: 0.8965561746996403
- name: Recall
type: recall
value: 0.8842443729903537
- name: F1
type: f1
value: 0.8866264991906878
---
<!-- 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: 0.2001
- Accuracy: 0.8842
- Precision: 0.8966
- Recall: 0.8842
- F1: 0.8866
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.2604 | 1.0 | 22 | 0.2334 | 0.8392 | 0.8375 | 0.8392 | 0.8333 |
| 0.2492 | 2.0 | 44 | 0.2621 | 0.8264 | 0.8229 | 0.8264 | 0.8232 |
| 0.2373 | 3.0 | 66 | 0.2334 | 0.8617 | 0.8639 | 0.8617 | 0.8626 |
| 0.2623 | 4.0 | 88 | 0.2036 | 0.8810 | 0.8908 | 0.8810 | 0.8832 |
| 0.2378 | 5.0 | 110 | 0.2944 | 0.8199 | 0.8165 | 0.8199 | 0.8173 |
| 0.221 | 6.0 | 132 | 0.2027 | 0.8682 | 0.8752 | 0.8682 | 0.8701 |
| 0.2339 | 7.0 | 154 | 0.2291 | 0.8585 | 0.8573 | 0.8585 | 0.8577 |
| 0.2215 | 8.0 | 176 | 0.2732 | 0.8682 | 0.8685 | 0.8682 | 0.8683 |
| 0.2162 | 9.0 | 198 | 0.2260 | 0.8682 | 0.8713 | 0.8682 | 0.8693 |
| 0.2226 | 10.0 | 220 | 0.2001 | 0.8842 | 0.8966 | 0.8842 | 0.8866 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.0.2
- Tokenizers 0.19.1
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