---
license: apache-2.0
base_model: microsoft/swinv2-tiny-patch4-window8-256
tags:
- generated_from_trainer
datasets:
- food101
metrics:
- accuracy
model-index:
- name: swinv2-tiny-patch4-window8-256-finetuned-eurosat
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: food101
      type: food101
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8858613861386139
---

<!-- 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-tiny-patch4-window8-256-finetuned-eurosat

This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on the food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3997
- Accuracy: 0.8859

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.8552        | 1.0   | 592  | 1.1245          | 0.6955   |
| 1.2938        | 2.0   | 1184 | 0.6712          | 0.8131   |
| 1.2294        | 3.0   | 1776 | 0.5354          | 0.8492   |
| 1.0199        | 4.0   | 2368 | 0.4958          | 0.8594   |
| 0.9914        | 5.0   | 2960 | 0.4633          | 0.8678   |
| 0.8786        | 6.0   | 3552 | 0.4390          | 0.8750   |
| 0.806         | 7.0   | 4144 | 0.4206          | 0.8791   |
| 0.7506        | 8.0   | 4736 | 0.4093          | 0.8832   |
| 0.7433        | 9.0   | 5328 | 0.4053          | 0.8841   |
| 0.6393        | 10.0  | 5920 | 0.3997          | 0.8859   |


### Framework versions

- Transformers 4.32.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3