HackerEarth’s Sigma-Thon 1.0 competition (July 2020 – HackerEarth)
To build a data-driven solution for retailers to innovate their retail channels with data models, recommendation engines, and much more. Grow your appetite for data-based solutions exponentially!
More info on the competition:
https://www.hackerearth.com/challenges/hackathon/sigma-thon-v1/
Have won Second Prize of 75$.
Project Title: “Smart video solutions for Fashion Store Optimization”
A Project which is a people counter system works on “Intel’s AI” which helps store managers to get insights on how well their store is performing.
More info on the project (GitHub Commits):
https://github.com/KVishnuVardhanR/smart-Video-Solutions-for-fashion-store-optimization
Intel Edge AI Foundation Course Scholarship (March 2020 – Udacity)
Earned a place within the Top 850 out of 16450. Awarded the Intel Edge AI for IoT Developers
Nanodegree program from Udacity worth of Rs: 58,257/-
Machine Learning Challenge – STD drug Effective or not (March 2020 – HackerEarth)
Achieved a Rank of 53 out of 3712, Created a model which has the ability to classify an STD drug is effective or not with 92.03% accuracy
IEEE-CIS Fraud Detection | Kaggle (October 2019 – Kaggle)
Earned a place within the Top 79% in 6381. Fraud Detection model score: 88.43%Computer Pointer Controller:
https://github.com/KVishnuVardhanR/Computer-Pointer-Controller
- Utilising Gaze Estimation model to control the mouse pointer of the computer.
- The Gaze Estimation model requires three inputs: The head pose, the left eye image and the right eye image.
- The total time taken by the models to perform inference is 8.1 seconds which has 0.49 FPS processing speed on Intel's i3 core processor.
- This project has the ability to run multiple models in the same machine and coordinate the how of data between those models.
Smart Queuing Systems:
https://github.com/KVishnuVardhanR/Smart-Queuing-Systems
- Deploying Person detection models in Edge devices by testing the hardware which best fits the client's requirements and their investments.
- The models have been tested on the following hardware using Intel DevCloud:
- CPU
- VPU : Intel Neural Compute Stick 2 [NCS2]
- IGPU
- FPGA : Intel Arria 10 FPGA
- Given different scenarios, the best hardware has been documented in proposed-template.pdf which meets the client's requirements and their investments to deploy AI models in Edge devices.
