README.md

---
license: apache-2.0
tags:
- accelerator
metrics:
- accuracy
model-index:
- name: finetuned-vit-base-patch16-224-upside-down-detector
  results: []
widget:
- src: https://huggingface.co/jaygala24/finetuned-vit-base-patch16-224-upside-down-detector/resolve/main/original.jpg
  example_title: original
- src: https://huggingface.co/jaygala24/finetuned-vit-base-patch16-224-upside-down-detector/resolve/main/upside_down.jpg
  example_title: upside_down
---

# finetuned-vit-base-patch16-224-upside-down-detector

This model is a fine-tuned version of [vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the custom image orientation dataset adapted from the [beans](https://huggingface.co/datasets/beans) dataset. It achieves the following results on the evaluation set:
- Accuracy: 0.8947

## Training and evaluation data

The custom dataset for image orientation adapted from [beans](https://huggingface.co/datasets/beans) dataset contains a total of 2,590 image samples with 1,295 original and 1,295 upside down. The model was fine-tuned on the train subset and evaluated on validation and test subsets. The dataset splits are listed below:

| Split      | # examples |
|:----------:|:----------:|
| train      | 2068       |
| validation | 133        |
| test       | 128        |

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-04
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 32
- num_epochs: 5

### Training results

| Epoch      | Accuracy   |
|:----------:|:----------:|
| 0          | 0.8609     |
| 1          | 0.8835     |
| 2          | 0.8571     |
| 3          | 0.8941     |
| 4          | 0.8941     |

### Framework versions

- Transformers 4.17.0
- Pytorch 1.9.0+cu111
- Pytorch/XLA 1.9
- Datasets 2.0.0
- Tokenizers 0.12.0