---
license: apache-2.0
base_model: microsoft/swinv2-small-patch4-window8-256
tags:
- generated_from_trainer
datasets:
- food101
metrics:
- accuracy
model-index:
- name: swinv2-small-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: 0.8846732673267327
---

<!-- 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 food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4063
- Accuracy: 0.8847

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3521        | 1.0   | 592  | 0.7233          | 0.7967   |
| 0.9887        | 2.0   | 1184 | 0.5211          | 0.8519   |
| 0.845         | 3.0   | 1776 | 0.4063          | 0.8847   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3