---
license: apache-2.0
base_model: google/vit-large-patch16-224-in21k
tags:
- image-classification
- vision
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: fashion-images-pack-types-vit-large-patch16-224-in21k
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: touchtech/fashion-images-pack-types
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9894336432797971
---

<!-- 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-images-pack-types-vit-large-patch16-224-in21k

This model is a fine-tuned version of [google/vit-large-patch16-224-in21k](https://huggingface.co/google/vit-large-patch16-224-in21k) on the touchtech/fashion-images-pack-types dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0343
- Accuracy: 0.9894

## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1556        | 1.0   | 1676 | 0.0490          | 0.9861   |
| 0.1185        | 2.0   | 3352 | 0.0343          | 0.9894   |
| 0.0815        | 3.0   | 5028 | 0.0537          | 0.9882   |
| 0.0503        | 4.0   | 6704 | 0.0374          | 0.9915   |
| 0.0447        | 5.0   | 8380 | 0.0362          | 0.9915   |


### Framework versions

- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3