---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- food101
metrics:
- accuracy
model-index:
- name: swin-finetuned-food101
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: food101
      type: food101
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9210297029702971
  - task:
      type: image-classification
      name: Image Classification
    dataset:
      name: food101
      type: food101
      config: default
      split: validation
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9135841584158416
      verified: true
    - name: Precision Macro
      type: precision
      value: 0.9151645786633058
      verified: true
    - name: Precision Micro
      type: precision
      value: 0.9135841584158416
      verified: true
    - name: Precision Weighted
      type: precision
      value: 0.915164578663306
      verified: true
    - name: Recall Macro
      type: recall
      value: 0.9135841584158414
      verified: true
    - name: Recall Micro
      type: recall
      value: 0.9135841584158416
      verified: true
    - name: Recall Weighted
      type: recall
      value: 0.9135841584158416
      verified: true
    - name: F1 Macro
      type: f1
      value: 0.9138785016966742
      verified: true
    - name: F1 Micro
      type: f1
      value: 0.9135841584158415
      verified: true
    - name: F1 Weighted
      type: f1
      value: 0.9138785016966743
      verified: true
    - name: loss
      type: loss
      value: 0.30761435627937317
      verified: true
---

<!-- 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-finetuned-food101

This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/microsoft/swin-base-patch4-window7-224) on the food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2772
- Accuracy: 0.9210

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5077        | 1.0   | 1183 | 0.3851          | 0.8893   |
| 0.3523        | 2.0   | 2366 | 0.3124          | 0.9088   |
| 0.1158        | 3.0   | 3549 | 0.2772          | 0.9210   |


### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1