---
library_name: transformers
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
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: 0.8553054662379421
    - name: Precision
      type: precision
      value: 0.8675973805921082
    - name: Recall
      type: recall
      value: 0.8553054662379421
    - name: F1
      type: f1
      value: 0.8581712564304036
---

<!-- 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: 0.2816
- Accuracy: 0.8553
- Precision: 0.8676
- Recall: 0.8553
- F1: 0.8582

## 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 | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5793        | 1.0   | 22   | 0.5874          | 0.6785   | 0.4603    | 0.6785 | 0.5485 |
| 0.3711        | 2.0   | 44   | 0.4135          | 0.7781   | 0.8169    | 0.7781 | 0.7395 |
| 0.2961        | 3.0   | 66   | 0.2816          | 0.8553   | 0.8676    | 0.8553 | 0.8582 |
| 0.2576        | 4.0   | 88   | 0.2899          | 0.7942   | 0.7884    | 0.7942 | 0.7857 |
| 0.261         | 5.0   | 110  | 0.2469          | 0.8103   | 0.8057    | 0.8103 | 0.8037 |
| 0.2559        | 6.0   | 132  | 0.2548          | 0.8360   | 0.8632    | 0.8360 | 0.8179 |
| 0.2249        | 7.0   | 154  | 0.2835          | 0.8135   | 0.8479    | 0.8135 | 0.7882 |
| 0.2242        | 8.0   | 176  | 0.2335          | 0.8296   | 0.8261    | 0.8296 | 0.8262 |
| 0.2215        | 9.0   | 198  | 0.2293          | 0.8521   | 0.8549    | 0.8521 | 0.8532 |
| 0.2269        | 10.0  | 220  | 0.2213          | 0.8424   | 0.8396    | 0.8424 | 0.8393 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.0.2
- Tokenizers 0.19.1