---
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