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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: Electrcical-IMAGE-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: 0.8960396039603961
---
<!-- 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. -->
# Electrcical-IMAGE-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: 0.3583
- Accuracy: 0.8960
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.6143 | 0.98 | 28 | 1.2882 | 0.5347 |
| 0.8597 | 2.0 | 57 | 0.7302 | 0.7649 |
| 0.5858 | 2.98 | 85 | 0.4849 | 0.8465 |
| 0.4332 | 4.0 | 114 | 0.4274 | 0.8614 |
| 0.4054 | 4.98 | 142 | 0.3687 | 0.8787 |
| 0.3826 | 6.0 | 171 | 0.3788 | 0.8614 |
| 0.3561 | 6.98 | 199 | 0.3700 | 0.8936 |
| 0.2838 | 8.0 | 228 | 0.3550 | 0.8812 |
| 0.2897 | 8.98 | 256 | 0.3698 | 0.8886 |
| 0.2519 | 10.0 | 285 | 0.3459 | 0.8837 |
| 0.2194 | 10.98 | 313 | 0.3583 | 0.8960 |
| 0.1955 | 12.0 | 342 | 0.3442 | 0.8886 |
| 0.2443 | 12.98 | 370 | 0.3801 | 0.8787 |
| 0.207 | 14.0 | 399 | 0.3499 | 0.8861 |
| 0.2078 | 14.98 | 427 | 0.3701 | 0.8837 |
| 0.1873 | 16.0 | 456 | 0.3773 | 0.8861 |
| 0.1697 | 16.98 | 484 | 0.3753 | 0.8861 |
| 0.1812 | 18.0 | 513 | 0.3747 | 0.8911 |
| 0.151 | 18.98 | 541 | 0.3736 | 0.8861 |
| 0.1567 | 19.65 | 560 | 0.3726 | 0.8861 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
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