---
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-ve-b-U10-12
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7450980392156863
---

<!-- 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-ve-b-U10-12

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: 0.9868
- Accuracy: 0.7451

## 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: 5.5e-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: 0.05
- num_epochs: 12

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.96  | 6    | 1.3771          | 0.3137   |
| 1.3705        | 1.92  | 12   | 1.3219          | 0.5490   |
| 1.3705        | 2.88  | 18   | 1.2517          | 0.5490   |
| 1.2535        | 4.0   | 25   | 1.1875          | 0.5882   |
| 1.1079        | 4.96  | 31   | 1.1237          | 0.6078   |
| 1.1079        | 5.92  | 37   | 1.1003          | 0.6275   |
| 1.0048        | 6.88  | 43   | 1.0609          | 0.6863   |
| 0.9172        | 8.0   | 50   | 1.0668          | 0.6078   |
| 0.9172        | 8.96  | 56   | 1.0031          | 0.6667   |
| 0.8558        | 9.92  | 62   | 0.9868          | 0.7451   |
| 0.8558        | 10.88 | 68   | 0.9763          | 0.7451   |
| 0.8284        | 11.52 | 72   | 0.9733          | 0.7451   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0