---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-pure-ViT
  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: 0.8714733542319749
---

<!-- 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-base-patch16-224-pure-ViT

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3270
- Accuracy: 0.8715

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4676        | 1.0   | 202  | 0.4042          | 0.8095   |
| 0.4605        | 2.0   | 404  | 0.3675          | 0.8377   |
| 0.4012        | 3.0   | 606  | 0.3486          | 0.8506   |
| 0.3727        | 4.0   | 808  | 0.3413          | 0.8481   |
| 0.3482        | 5.0   | 1010 | 0.3339          | 0.8614   |
| 0.354         | 6.0   | 1212 | 0.3436          | 0.8561   |
| 0.3212        | 7.0   | 1414 | 0.3415          | 0.8534   |
| 0.3263        | 8.0   | 1616 | 0.3281          | 0.8642   |
| 0.285         | 9.0   | 1818 | 0.3263          | 0.8673   |
| 0.2779        | 10.0  | 2020 | 0.3270          | 0.8715   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2