---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-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: 1.0
---

<!-- 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. -->

# swin-tiny-patch4-window7-224-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: 0.0600
- Accuracy: 1.0

## 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: 40

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 1    | 1.4318          | 0.8      |
| No log        | 2.0   | 2    | 1.3863          | 0.8      |
| No log        | 3.0   | 3    | 1.2880          | 0.8      |
| No log        | 4.0   | 4    | 1.1589          | 0.8      |
| No log        | 5.0   | 5    | 0.9954          | 0.8      |
| No log        | 6.0   | 6    | 0.8942          | 0.8      |
| No log        | 7.0   | 7    | 0.8269          | 0.8      |
| No log        | 8.0   | 8    | 0.7702          | 0.8      |
| No log        | 9.0   | 9    | 0.7138          | 1.0      |
| No log        | 10.0  | 10   | 0.6602          | 1.0      |
| No log        | 11.0  | 11   | 0.6255          | 1.0      |
| No log        | 12.0  | 12   | 0.5900          | 1.0      |
| No log        | 13.0  | 13   | 0.5367          | 1.0      |
| No log        | 14.0  | 14   | 0.4790          | 1.0      |
| No log        | 15.0  | 15   | 0.4158          | 1.0      |
| No log        | 16.0  | 16   | 0.3573          | 1.0      |
| No log        | 17.0  | 17   | 0.2964          | 1.0      |
| No log        | 18.0  | 18   | 0.2439          | 1.0      |
| No log        | 19.0  | 19   | 0.2028          | 1.0      |
| 0.5248        | 20.0  | 20   | 0.1671          | 1.0      |
| 0.5248        | 21.0  | 21   | 0.1399          | 1.0      |
| 0.5248        | 22.0  | 22   | 0.1182          | 1.0      |
| 0.5248        | 23.0  | 23   | 0.1013          | 1.0      |
| 0.5248        | 24.0  | 24   | 0.0897          | 1.0      |
| 0.5248        | 25.0  | 25   | 0.0824          | 1.0      |
| 0.5248        | 26.0  | 26   | 0.0769          | 1.0      |
| 0.5248        | 27.0  | 27   | 0.0721          | 1.0      |
| 0.5248        | 28.0  | 28   | 0.0701          | 1.0      |
| 0.5248        | 29.0  | 29   | 0.0697          | 1.0      |
| 0.5248        | 30.0  | 30   | 0.0693          | 1.0      |
| 0.5248        | 31.0  | 31   | 0.0672          | 1.0      |
| 0.5248        | 32.0  | 32   | 0.0646          | 1.0      |
| 0.5248        | 33.0  | 33   | 0.0633          | 1.0      |
| 0.5248        | 34.0  | 34   | 0.0628          | 1.0      |
| 0.5248        | 35.0  | 35   | 0.0626          | 1.0      |
| 0.5248        | 36.0  | 36   | 0.0626          | 1.0      |
| 0.5248        | 37.0  | 37   | 0.0617          | 1.0      |
| 0.5248        | 38.0  | 38   | 0.0608          | 1.0      |
| 0.5248        | 39.0  | 39   | 0.0603          | 1.0      |
| 0.2241        | 40.0  | 40   | 0.0600          | 1.0      |


### Framework versions

- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1