---
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: 0.5917085427135679
---

<!-- 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 was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0104
- Accuracy: 0.5917

## 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: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0339        | 1.0   | 28   | 1.0541          | 0.5641   |
| 1.0193        | 2.0   | 56   | 1.0464          | 0.5622   |
| 1.0348        | 3.0   | 84   | 1.0331          | 0.5691   |
| 1.0072        | 4.0   | 112  | 1.0254          | 0.5848   |
| 0.9892        | 5.0   | 140  | 1.0121          | 0.5754   |
| 0.9379        | 6.0   | 168  | 1.0175          | 0.5810   |
| 0.9123        | 7.0   | 196  | 1.0120          | 0.5867   |
| 0.8865        | 8.0   | 224  | 1.0104          | 0.5917   |
| 0.8668        | 9.0   | 252  | 1.0236          | 0.5873   |
| 0.8189        | 10.0  | 280  | 1.0360          | 0.5829   |
| 0.7933        | 11.0  | 308  | 1.0395          | 0.5835   |
| 0.7765        | 12.0  | 336  | 1.0594          | 0.5729   |
| 0.7538        | 13.0  | 364  | 1.0552          | 0.5879   |
| 0.7146        | 14.0  | 392  | 1.0620          | 0.5829   |
| 0.6885        | 15.0  | 420  | 1.0783          | 0.5842   |
| 0.6556        | 16.0  | 448  | 1.1010          | 0.5817   |
| 0.6453        | 17.0  | 476  | 1.1131          | 0.5735   |
| 0.6175        | 18.0  | 504  | 1.1074          | 0.5892   |
| 0.5993        | 19.0  | 532  | 1.1328          | 0.5741   |
| 0.5683        | 20.0  | 560  | 1.1423          | 0.5791   |
| 0.5524        | 21.0  | 588  | 1.1517          | 0.5873   |
| 0.5151        | 22.0  | 616  | 1.1673          | 0.5766   |
| 0.5096        | 23.0  | 644  | 1.1760          | 0.5798   |
| 0.4937        | 24.0  | 672  | 1.1931          | 0.5817   |
| 0.487         | 25.0  | 700  | 1.2084          | 0.5735   |
| 0.4597        | 26.0  | 728  | 1.2270          | 0.5716   |
| 0.4482        | 27.0  | 756  | 1.2389          | 0.5829   |
| 0.4183        | 28.0  | 784  | 1.2430          | 0.5773   |
| 0.4228        | 29.0  | 812  | 1.2637          | 0.5741   |
| 0.4116        | 30.0  | 840  | 1.2688          | 0.5779   |
| 0.3942        | 31.0  | 868  | 1.2986          | 0.5879   |
| 0.3815        | 32.0  | 896  | 1.2911          | 0.5766   |
| 0.3828        | 33.0  | 924  | 1.3113          | 0.5773   |
| 0.3791        | 34.0  | 952  | 1.3317          | 0.5766   |
| 0.3701        | 35.0  | 980  | 1.3384          | 0.5773   |
| 0.3566        | 36.0  | 1008 | 1.3406          | 0.5754   |
| 0.3551        | 37.0  | 1036 | 1.3410          | 0.5766   |
| 0.3487        | 38.0  | 1064 | 1.3364          | 0.5867   |
| 0.3463        | 39.0  | 1092 | 1.3496          | 0.5810   |
| 0.3242        | 40.0  | 1120 | 1.3640          | 0.5747   |
| 0.3308        | 41.0  | 1148 | 1.3627          | 0.5716   |
| 0.3255        | 42.0  | 1176 | 1.3795          | 0.5804   |
| 0.3295        | 43.0  | 1204 | 1.3747          | 0.5798   |
| 0.3147        | 44.0  | 1232 | 1.3747          | 0.5861   |
| 0.3125        | 45.0  | 1260 | 1.3839          | 0.5817   |
| 0.3276        | 46.0  | 1288 | 1.3806          | 0.5842   |
| 0.2989        | 47.0  | 1316 | 1.3906          | 0.5886   |
| 0.2941        | 48.0  | 1344 | 1.3876          | 0.5867   |
| 0.3131        | 49.0  | 1372 | 1.3896          | 0.5823   |
| 0.2975        | 50.0  | 1400 | 1.3906          | 0.5835   |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.1.2+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2