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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: 0.8787128712871287
---
<!-- 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: 0.3505
- Accuracy: 0.8787
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5532 | 0.98 | 28 | 1.1704 | 0.6163 |
| 0.8115 | 2.0 | 57 | 0.6827 | 0.7673 |
| 0.5513 | 2.98 | 85 | 0.4525 | 0.8416 |
| 0.455 | 4.0 | 114 | 0.4012 | 0.8540 |
| 0.3901 | 4.98 | 142 | 0.3824 | 0.8614 |
| 0.4042 | 6.0 | 171 | 0.3797 | 0.8639 |
| 0.3591 | 6.98 | 199 | 0.3505 | 0.8787 |
| 0.2989 | 8.0 | 228 | 0.3551 | 0.8614 |
| 0.3029 | 8.98 | 256 | 0.3625 | 0.8663 |
| 0.2606 | 10.0 | 285 | 0.3615 | 0.8490 |
| 0.2413 | 10.98 | 313 | 0.3435 | 0.8787 |
| 0.2051 | 12.0 | 342 | 0.3371 | 0.8663 |
| 0.2477 | 12.98 | 370 | 0.3451 | 0.8639 |
| 0.2271 | 14.0 | 399 | 0.3364 | 0.8738 |
| 0.2112 | 14.98 | 427 | 0.3559 | 0.8639 |
| 0.1902 | 16.0 | 456 | 0.3630 | 0.8738 |
| 0.1739 | 16.98 | 484 | 0.3630 | 0.8713 |
| 0.195 | 18.0 | 513 | 0.3625 | 0.8663 |
| 0.1621 | 18.98 | 541 | 0.3571 | 0.8762 |
| 0.154 | 19.65 | 560 | 0.3555 | 0.8738 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
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