---
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
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: 0.8111298482293423
---

<!-- 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: 0.4435
- Accuracy: 0.8111

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5077        | 0.98  | 41   | 0.6378          | 0.6796   |
| 0.5111        | 1.99  | 83   | 0.7097          | 0.6577   |
| 0.5395        | 2.99  | 125  | 0.5374          | 0.7470   |
| 0.5498        | 4.0   | 167  | 0.5524          | 0.7420   |
| 0.4754        | 4.98  | 208  | 0.5324          | 0.7639   |
| 0.4662        | 5.99  | 250  | 0.4962          | 0.7639   |
| 0.4677        | 6.99  | 292  | 0.5070          | 0.7774   |
| 0.4525        | 8.0   | 334  | 0.5144          | 0.7673   |
| 0.4635        | 8.98  | 375  | 0.4978          | 0.7757   |
| 0.4309        | 9.99  | 417  | 0.5388          | 0.7774   |
| 0.4292        | 10.99 | 459  | 0.4937          | 0.7825   |
| 0.4182        | 12.0  | 501  | 0.5234          | 0.7808   |
| 0.4242        | 12.98 | 542  | 0.4539          | 0.7960   |
| 0.4053        | 13.99 | 584  | 0.5089          | 0.7858   |
| 0.4135        | 14.99 | 626  | 0.4655          | 0.8044   |
| 0.3888        | 16.0  | 668  | 0.4398          | 0.8212   |
| 0.3701        | 16.98 | 709  | 0.4258          | 0.8145   |
| 0.3641        | 17.99 | 751  | 0.4339          | 0.8196   |
| 0.3547        | 18.99 | 793  | 0.4556          | 0.7993   |
| 0.3623        | 19.64 | 820  | 0.4435          | 0.8111   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0