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---
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
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