---
license: apache-2.0
base_model: microsoft/swinv2-tiny-patch4-window16-256
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: SwinV2-30VNFood
  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: 0.8771825396825397
---

<!-- 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. -->

# SwinV2-30VNFood

This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window16-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window16-256) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4561
- Accuracy: 0.8772

## 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: 0.0003
- train_batch_size: 64
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7587        | 1.0   | 275  | 0.5447          | 0.8477   |
| 0.4341        | 2.0   | 550  | 0.4809          | 0.8640   |
| 0.2737        | 3.0   | 825  | 0.4703          | 0.8763   |
| 0.1704        | 4.0   | 1100 | 0.5040          | 0.8791   |
| 0.1225        | 5.0   | 1375 | 0.4893          | 0.8879   |
| 0.0886        | 6.0   | 1650 | 0.5733          | 0.8863   |
| 0.0568        | 7.0   | 1925 | 0.5986          | 0.8803   |
| 0.0407        | 8.0   | 2200 | 0.5664          | 0.8998   |
| 0.0175        | 9.0   | 2475 | 0.5790          | 0.8998   |
| 0.0175        | 10.0  | 2750 | 0.5754          | 0.9038   |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2