---
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-finalterm
  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: 0.88125
---

<!-- 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-finalterm

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: 0.3547
- Accuracy: 0.8812

## 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: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3999        | 1.0   | 10   | 1.1607          | 0.5094   |
| 0.993         | 2.0   | 20   | 0.7807          | 0.7031   |
| 0.6819        | 3.0   | 30   | 0.5753          | 0.8063   |
| 0.5485        | 4.0   | 40   | 0.6475          | 0.7594   |
| 0.463         | 5.0   | 50   | 0.4393          | 0.8406   |
| 0.3929        | 6.0   | 60   | 0.4067          | 0.8625   |
| 0.3636        | 7.0   | 70   | 0.3626          | 0.8875   |
| 0.3719        | 8.0   | 80   | 0.3613          | 0.8875   |
| 0.343         | 9.0   | 90   | 0.3624          | 0.8781   |
| 0.3297        | 10.0  | 100  | 0.3800          | 0.8625   |
| 0.2948        | 11.0  | 110  | 0.3320          | 0.8938   |
| 0.33          | 12.0  | 120  | 0.3481          | 0.8781   |
| 0.3281        | 13.0  | 130  | 0.3418          | 0.8875   |
| 0.3           | 14.0  | 140  | 0.3425          | 0.8844   |
| 0.3014        | 15.0  | 150  | 0.3547          | 0.8812   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1