---
license: apache-2.0
base_model: microsoft/swinv2-base-patch4-window12-192-22k
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: SwinV2-Base-30VN-Food
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: vuongnhathien/30VNFoods
      type: imagefolder
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8628968253968254
---

<!-- 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-Base-30VN-Food

This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window12-192-22k](https://huggingface.co/microsoft/swinv2-base-patch4-window12-192-22k) on the vuongnhathien/30VNFoods dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4828
- Accuracy: 0.8629

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8268        | 1.0   | 275  | 0.5937          | 0.8270   |
| 0.5113        | 2.0   | 550  | 0.5267          | 0.8545   |
| 0.331         | 3.0   | 825  | 0.5459          | 0.8545   |
| 0.2273        | 4.0   | 1100 | 0.6090          | 0.8441   |
| 0.1384        | 5.0   | 1375 | 0.6096          | 0.8736   |
| 0.0918        | 6.0   | 1650 | 0.6669          | 0.8414   |
| 0.0616        | 7.0   | 1925 | 0.6487          | 0.8891   |
| 0.0307        | 8.0   | 2200 | 0.6908          | 0.8787   |
| 0.0173        | 9.0   | 2475 | 0.6673          | 0.8938   |
| 0.0109        | 10.0  | 2750 | 0.6488          | 0.9014   |


### Framework versions

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