---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- uta_rldd
metrics:
- accuracy
model-index:
- name: vit-base-nthu-ddd
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: uta_rldd
      type: uta_rldd
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9751972942502819
---

<!-- 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-nthu-ddd

This model is a fine-tuned version of [chbh7051/vit-base-nthu-ddd](https://huggingface.co/chbh7051/vit-base-nthu-ddd) on the uta_rldd dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0800
- Accuracy: 0.9752

## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0873        | 0.9   | 3000 | 0.1256          | 0.9550   |
| 0.06          | 1.8   | 6000 | 0.0800          | 0.9752   |


### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2