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:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: finetuned-food
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: food_images_classification
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9281675392670157
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# finetuned-food

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 food_images_classification dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2816
- Accuracy: 0.9282

## 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: 0.0002
- train_batch_size: 15
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8456        | 0.39  | 500  | 0.8593          | 0.7634   |
| 0.7824        | 0.78  | 1000 | 0.6625          | 0.8172   |
| 0.4806        | 1.18  | 1500 | 0.4951          | 0.8618   |
| 0.6206        | 1.57  | 2000 | 0.4434          | 0.88     |
| 0.5096        | 1.96  | 2500 | 0.4937          | 0.8683   |
| 0.4576        | 2.35  | 3000 | 0.4060          | 0.8907   |
| 0.3284        | 2.75  | 3500 | 0.3414          | 0.9081   |
| 0.2022        | 3.14  | 4000 | 0.3330          | 0.9118   |
| 0.1332        | 3.53  | 4500 | 0.3043          | 0.9208   |
| 0.1821        | 3.92  | 5000 | 0.2816          | 0.9282   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.2.0.post100
- Datasets 2.12.0
- Tokenizers 0.13.2
