This project counts number of people coming in and going out of structures such as building, stores,etc. based on tripline crossing.
Real-time LISA Traffic Sign Detection by YOLOv2
YOLOv4 (v3/v2) - Windows and Linux version of Darknet Neural Networks for object detection (Tensor Cores are used)
Conversion of YOLOv2_tiny from Caffe to NCNN, and tested under NCNN
Mice object detection and real time processing which aided in the experiments in "Correlated Neural Activity and Encoding of Behavior Across Brains of Socially Interacting Animals" (Cell 2019)
An end-to-end, reproducible example of taking an object-detection model → extracting/quantizing weights → building an HLS accelerator → integrating it in Vivado → deploying it on a Kria KV260 → running a Linux userspace application that produces correct detections. Validated end-to-end with YOLOv2 INT16 on KV260.
A python wrapper to handle numpy arrays for YOLOV2.
Preservation of a fullstack demo of deep learning through mobile computer vision, processed in the cloud
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