---
license: apache-2.0
base_model: microsoft/swin-base-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: swin-finetuned-food101
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.4166666666666667
    - name: F1
      type: f1
      value: 0.5882352941176471
    - name: Precision
      type: precision
      value: 0.4166666666666667
    - name: Recall
      type: recall
      value: 1.0
---

<!-- 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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7046
- Accuracy: 0.4167
- F1: 0.5882
- Precision: 0.4167
- Recall: 1.0
- Auc: 0.5742

## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- 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: 0.06
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall | Auc    |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|
| 0.6978        | 1.0   | 14   | 0.6847          | 0.5833   | 0.0    | 0.0       | 0.0    | 0.5717 |
| 0.7025        | 2.0   | 28   | 0.7120          | 0.4167   | 0.5882 | 0.4167    | 1.0    | 0.5570 |
| 0.6946        | 3.0   | 42   | 0.6955          | 0.4167   | 0.5882 | 0.4167    | 1.0    | 0.5662 |
| 0.6935        | 4.0   | 56   | 0.7047          | 0.4167   | 0.5882 | 0.4167    | 1.0    | 0.5644 |
| 0.6935        | 5.0   | 70   | 0.7046          | 0.4167   | 0.5882 | 0.4167    | 1.0    | 0.5742 |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0