# 1. Deep Learning for Vision
</p>
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 the 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 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 performance on these images in the future (you do not need to implement these suggestions)
{}
region:us
TAGS
#region-us 

# 1. Deep Learning for Vision
</p>
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 the 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 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 performance on these images in the future (you do not need to implement these suggestions)
# 1. Deep Learning for Vision
</p>
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 the 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 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 performance on these images in the future (you do not need to implement these suggestions)
TAGS
#region-us 
 # 1. Deep Learning for Vision
</p>
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 the 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 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 performance on these images in the future (you do not need to implement these suggestions)
passage: TAGS
#region-us 
# 1. Deep Learning for Vision
</p>
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 the 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 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 performance on these images in the future (you do not need to implement these suggestions)