Position: Middle Data Scientist
Category: Data Science
English level: fluent
Years of experience: 2

I worked in a startup company that produces UAVs (drones) and have extensive experience in two areas: computer vision and electronics. Since I worked in a startup, I had to face many things and experience the full production cycle. Worked solo as a computer vision engineer and performed tasks from dataset collection/cleansing/preparation to model training and tuning on the UAV microprocessor. At first custom trained image classifer using ResNet18, then moved on Yolo2 object detector with DeepSORT object tracker algorithm. At the same time, I made settings for the UAV camera. I also designed and manufactured all the electronics, including the printed circuit board for the UAV catapult, and made a small but high-current switchboard for the UAV.
I developed a program for UAV (drone) to detect objects on earth and track them. Works done:
• Building a dataset and preparing data for model training
• Finding a suitable model and training model on a new dataset
• Model conversion from PyTorch -> to awnn to run drone microprocessor models. Setting microprocessor to install the model (created custom Linux distribution (OS) for drone microprocessor)
• Testing the model on the microprocessor, adding object tracking algorithm.
• Preparation, setup of the camera and its control (servo motors, zoom, focus).
Tech Stack: Python, PyTorch, OpenCV, Yolov2, DeepSORT, Maix ii Dock microprocessor(Linux, awnn), Orange Pi, GPS, 10x Camera.

Completed an object classification project for an unmanned aerial vehicle UAV (drone). Specifically:
• Dataset collection and images preparation for training the model
• Training the image classifier, converting the trained model from PyTorch -> awnn.
• Writing code for: finding classifying objects on video, finding their coordinates, providing 30 fps video stream, saving video in memory
• Assembling the payload of a drone consisting of camera, GPS and microprocessor
• Testing and improving the accuracy of the classifier
Tech Stack: Python, PyTorch, OpenCV, ResNet18, maix ii dock microprocessor(Linux, awnn), Orange Pi, GPS, web camera.