---
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: 0.8910891089108911
---

<!-- 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: 0.3172
- Accuracy: 0.8911

## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.4879        | 0.9825 | 28   | 0.9158          | 0.7327   |
| 0.7072        | 2.0    | 57   | 0.4648          | 0.8366   |
| 0.521         | 2.9825 | 85   | 0.3816          | 0.8713   |
| 0.4664        | 4.0    | 114  | 0.4033          | 0.8564   |
| 0.3944        | 4.9825 | 142  | 0.3691          | 0.8738   |
| 0.3627        | 6.0    | 171  | 0.3214          | 0.8886   |
| 0.3298        | 6.9825 | 199  | 0.3172          | 0.8911   |
| 0.3203        | 8.0    | 228  | 0.3061          | 0.8911   |
| 0.2737        | 8.9825 | 256  | 0.3129          | 0.8861   |
| 0.2728        | 9.8246 | 280  | 0.3088          | 0.8861   |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1