---
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: minang_food_classification
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9277777777777778
---

<!-- 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. -->

# minang_food_classification

This model was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7860
- Accuracy: 0.9278

## 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: 1e-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3423        | 1.0   | 45   | 1.3263          | 0.7889   |
| 1.2638        | 2.0   | 90   | 1.2436          | 0.8278   |
| 1.2055        | 3.0   | 135  | 1.2503          | 0.8      |
| 1.14          | 4.0   | 180  | 1.1486          | 0.85     |
| 1.0908        | 5.0   | 225  | 1.0427          | 0.8778   |
| 1.0258        | 6.0   | 270  | 1.0210          | 0.8333   |
| 0.9776        | 7.0   | 315  | 0.9694          | 0.8722   |
| 0.9306        | 8.0   | 360  | 0.9379          | 0.8833   |
| 0.8985        | 9.0   | 405  | 0.9150          | 0.8778   |
| 0.8624        | 10.0  | 450  | 0.8884          | 0.8611   |
| 0.8243        | 11.0  | 495  | 0.8118          | 0.9222   |
| 0.8017        | 12.0  | 540  | 0.8394          | 0.8833   |
| 0.797         | 13.0  | 585  | 0.7761          | 0.9056   |
| 0.7765        | 14.0  | 630  | 0.7891          | 0.9111   |
| 0.7834        | 15.0  | 675  | 0.7945          | 0.8889   |
| 0.7483        | 16.0  | 720  | 0.7801          | 0.9      |
| 0.74          | 17.0  | 765  | 0.7524          | 0.9167   |
| 0.7315        | 18.0  | 810  | 0.7655          | 0.9111   |
| 0.7468        | 19.0  | 855  | 0.7860          | 0.8833   |
| 0.7393        | 20.0  | 900  | 0.7900          | 0.9056   |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1