1. Deep Learning for Vision
Upside down detector: Train a model to detect if images are upside down

Pick a dataset of natural images (we suggest looking at datasets on the Hugging Face Hub)
Synthetically turn some of images upside down. Create a training and test set.
Build a neural network (using Tensorflow, PyTorch, or any framework you like)
Train it to classify image orientation until a reasonable accuracy is reached
Upload the the model to the Hugging Face Hub, and add a link to your model below.
Look at some of the images that were classified incorrectly. Please explain what you might do to improve your model's performance on these images in the future (you do not need to impelement these suggestions)
Submission instructions: Please write your code below and include some examples of images that were classified
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1. Deep Learning for Vision
Upside down detector: Train a model to detect if images are upside down

Pick a dataset of natural images (we suggest looking at datasets on the Hugging Face Hub)
Synthetically turn some of images upside down. Create a training and test set.
Build a neural network (using Tensorflow, PyTorch, or any framework you like)
Train it to classify image orientation until a reasonable accuracy is reached
Upload the the model to the Hugging Face Hub, and add a link to your model below.
Look at some of the images that were classified incorrectly. Please explain what you might do to improve your model's performance on these images in the future (you do not need to impelement these suggestions)
Submission instructions: Please write your code below and include some examples of images that were classified
TAGS
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passage: TAGS
#region-us 
