---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- fashion_mnist
metrics:
- accuracy
base_model: google/vit-base-patch16-224-in21k
model-index:
- name: fashion_classification_model
  results:
  - task:
      type: image-classification
      name: Image Classification
    dataset:
      name: fashion_mnist
      type: fashion_mnist
      config: fashion_mnist
      split: train[:5000]
      args: fashion_mnist
    metrics:
    - type: accuracy
      value: 0.792
      name: Accuracy
---

<!-- 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. -->

# fashion_classification_model

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 fashion_mnist dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0461
- Accuracy: 0.792

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.285         | 0.99  | 62   | 1.2299          | 0.718    |
| 1.2002        | 2.0   | 125  | 1.2043          | 0.744    |
| 1.0345        | 2.98  | 186  | 1.0461          | 0.792    |


### Framework versions

- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3