---
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: ai_vs_real-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: 0.9901960784313726
---

<!-- 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. -->

# ai_vs_real-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: 0.0432
- Accuracy: 0.9902

## 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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.8   | 3    | 0.7072          | 0.5      |
| No log        | 1.87  | 7    | 0.5099          | 0.7255   |
| 0.6036        | 2.93  | 11   | 0.3836          | 0.8529   |
| 0.6036        | 4.0   | 15   | 0.2382          | 0.9118   |
| 0.6036        | 4.8   | 18   | 0.1662          | 0.9412   |
| 0.2575        | 5.87  | 22   | 0.1505          | 0.9412   |
| 0.2575        | 6.93  | 26   | 0.0722          | 0.9804   |
| 0.0813        | 8.0   | 30   | 0.0788          | 0.9608   |
| 0.0813        | 8.8   | 33   | 0.0697          | 0.9608   |
| 0.0813        | 9.87  | 37   | 0.0596          | 0.9608   |
| 0.053         | 10.93 | 41   | 0.0437          | 0.9902   |
| 0.053         | 12.0  | 45   | 0.0432          | 0.9902   |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2