transformers pytorch vit image-classification generated_from_trainer dataset:imagefolder base_model:google/vit-base-patch16-224-in21k license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: padang_cuisine_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: 0.950920245398773
---

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# padang_cuisine_classification

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8549
- Accuracy: 0.9509

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1256        | 0.98  | 10   | 2.0189          | 0.6012   |
| 1.839         | 1.95  | 20   | 1.6867          | 0.8834   |
| 1.5149        | 2.93  | 30   | 1.3800          | 0.9080   |
| 1.2405        | 4.0   | 41   | 1.1324          | 0.9141   |
| 1.0359        | 4.98  | 51   | 0.9649          | 0.9387   |
| 0.874         | 5.95  | 61   | 0.8402          | 0.9448   |
| 0.766         | 6.93  | 71   | 0.7901          | 0.9387   |
| 0.7065        | 8.0   | 82   | 0.7175          | 0.9448   |
| 0.6558        | 8.98  | 92   | 0.7112          | 0.9387   |
| 0.6537        | 9.76  | 100  | 0.7114          | 0.9325   |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
