---
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-U8-10b
  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: 0.8627450980392157
---

<!-- 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-U8-10b

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 "dmae-ve-U8".
It achieves the following results on the evaluation set:
- Loss: 0.5349
- Accuracy: 0.8627

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2473        | 1.0   | 20   | 1.1671          | 0.5882   |
| 0.955         | 2.0   | 40   | 0.9392          | 0.6471   |
| 0.735         | 3.0   | 60   | 0.7247          | 0.6863   |
| 0.5341        | 4.0   | 80   | 0.5977          | 0.8235   |
| 0.3864        | 5.0   | 100  | 0.6556          | 0.7451   |
| 0.2837        | 6.0   | 120  | 0.6781          | 0.7255   |
| 0.2332        | 7.0   | 140  | 0.5419          | 0.8431   |
| 0.1974        | 8.0   | 160  | 0.5349          | 0.8627   |
| 0.1857        | 9.0   | 180  | 0.5606          | 0.8235   |
| 0.1907        | 10.0  | 200  | 0.4875          | 0.8431   |


### Framework versions

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