---
license: apache-2.0
base_model: google/vit-base-patch32-224-in21k
tags:
- generated_from_trainer
datasets:
- snacks
metrics:
- accuracy
model-index:
- name: vit-model-rob-vilchis
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: snacks
      type: snacks
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8607329842931937
---

<!-- 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. -->

# vit-model-rob-vilchis

This model is a fine-tuned version of [google/vit-base-patch32-224-in21k](https://huggingface.co/google/vit-base-patch32-224-in21k) on the snacks dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5765
- Accuracy: 0.8607

## 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: 8
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2646        | 0.83  | 500  | 0.9471          | 0.7361   |
| 0.4485        | 1.65  | 1000 | 0.6931          | 0.8084   |
| 0.179         | 2.48  | 1500 | 0.7448          | 0.8157   |
| 0.052         | 3.31  | 2000 | 0.5765          | 0.8607   |


### Framework versions

- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3