readme: ---
  language : en
  tags     : image-classification
  license  : mit
  dataset  : cifar10
  metrics  : accuracy (https://hf.co/metrics/accuracy)
  ---
  
  ## Model description
  
  **Upside down detector**: Model to detect if images are upside down
  
  * Picked a dataset of natural images - cifar10
  * Synthetically turned some of images upside down. Created a training and test set.
  * Trained it to classify image orientation ie if the image is upside down or not.
  
  ## Intended uses & limitations
  Intended to showcase skill set of being able to train a simple CNN classifier.
  
  ## How to use
  n/a
  
  ## Limitations and bias
  Trained on a relatively small dataset, hence it's hard to derive conclusions. 
  
  ## Training data
  cifar10
  
  ## Training procedure
  Trained using Keras with Nadam classifier with ReduceLROnPlateau which halves the learning rate when the validation loss doesn't improve for 5 iterations
  
  ## Evaluation results
  The classifier was able to achieve 90% validation accuracy