---
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