readme: ---
  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
  