Objectives

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Fall term 

Description

The following steps have been done before and are not entirely unique. The steps to be completed in  fall term already have several examples by which to use as an example and to improve upon in order to lay the groundwork for later steps. 

Steps

    Step 1: Data set acquisition

• Determine the largest possible set given the compute resources
• Split data sets for recognition training tasks

Step 2 Preliminary identification training and model building

• Facial recognition
• Text recognition
• Object recognition

Step 3 Artistic Implementation and model building 

• IMage transformer
• Style differentiation
• Image Transformation
• DELIVERABLE: An AI model that is able to take an input image and transform it into the style of an artistic image.

Winter Term and Spring Term

Description

I believe this step will take the most time and will include making the previous model  more efficient in order to process videos in real time.If real time rendering proves unrealistic or too much work is requires the previous model will be made more efficient and aided by up scaling the fps either by training another mode

Steps

Step 3 Speed Up and Process Video

• Frame by frame to video
• Optimization analysis
• Optimize data pipeline 
• Optimize Neural Network and Hyperperameters
• FPS Upscaling if needed

 

Motivations

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I have always been fascinated by both art and technology. When choosing a path of study in college I felt the two were incompatible and decided to study computer science. This project is the opportunity to combine the arts with the state of the art.

Qualifications

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Minimum Qualifications:
None Listed

Preferred Qualifications:
None Listed

Details

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Project Partner:

Lucas Garcia

NDA/IPA:

No Agreement Required

Number Groups:

1

Project Status:

Accepting Applicants