- Final code
- Accuracy of your network and the results of the time command.
XNOR_GATEandAND_GATEresults.
For the Jetson AI Certification, we will be evaluating the same criteria that NVIDIA will be reviewing. The requirements are produced below for your convenience.
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AI – The project uses deep learning, machine learning, and/or computer vision in a meaningful way, and demonstrates a fundamental understanding of creating applications with AI. Factors include the effectiveness, technical complexity, and performance of your AI solution on Jetson.
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Impact / Originality – The concept of your project is novel and applies AI to solve or address challenges or issues faced by yourself or society. Also, our ideas and work are either original or derivative in a significant way.
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Reproducibility – Any plans, code, and resources needed for someone else to build and use the project are included in the repository and are easy to follow.
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Presentation and Documentation – The video effectively demonstrates and explains various aspects of the project, and there exists a clear, complete README in the repository that documents any steps needed to build/run the project along with diagrams and images. Note that educators should have an oral presentation component to their video to highlight their teaching abilities.
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Citation of the original paper you are basing your reproduction study on. A short abstract in your own words of what the paper contributes to science. Why did you choose it?
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In your writeup, explain briefly the goal of the study that you chose to reproduce, as well as any obstacles you had to overcome to get their artifact running in your environment. Discuss how your results compare to the original results and try to explain when things diverge.