---
license: apache-2.0
base_model: microsoft/swinv2-small-patch4-window8-256
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swinv2-small-patch4-window8-256-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: 0.6875
---

<!-- 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. -->

# swinv2-small-patch4-window8-256-finetuned-eurosat

This model is a fine-tuned version of [microsoft/swinv2-small-patch4-window8-256](https://huggingface.co/microsoft/swinv2-small-patch4-window8-256) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2717
- Accuracy: 0.6875

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 4    | 0.6579          | 0.6339   |
| No log        | 2.0   | 8    | 0.7129          | 0.5      |
| 0.6364        | 3.0   | 12   | 0.6774          | 0.5982   |
| 0.6364        | 4.0   | 16   | 0.6584          | 0.6786   |
| 0.3486        | 5.0   | 20   | 0.6864          | 0.6786   |
| 0.3486        | 6.0   | 24   | 0.8473          | 0.6429   |
| 0.3486        | 7.0   | 28   | 0.9735          | 0.6339   |
| 0.1224        | 8.0   | 32   | 0.8121          | 0.6964   |
| 0.1224        | 9.0   | 36   | 1.2379          | 0.6429   |
| 0.0424        | 10.0  | 40   | 1.1585          | 0.6875   |
| 0.0424        | 11.0  | 44   | 1.5274          | 0.6161   |
| 0.0424        | 12.0  | 48   | 1.1415          | 0.6607   |
| 0.0353        | 13.0  | 52   | 1.4422          | 0.6518   |
| 0.0353        | 14.0  | 56   | 1.6677          | 0.625    |
| 0.0141        | 15.0  | 60   | 1.1960          | 0.6696   |
| 0.0141        | 16.0  | 64   | 1.5515          | 0.625    |
| 0.0141        | 17.0  | 68   | 1.7990          | 0.6161   |
| 0.0135        | 18.0  | 72   | 1.4437          | 0.6607   |
| 0.0135        | 19.0  | 76   | 1.2816          | 0.7054   |
| 0.0073        | 20.0  | 80   | 1.2717          | 0.6875   |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2