This project implements YOLO/YOLOv2, which came from You Only Look Once: Unified, Real-Time Object Detection and YOLO9000: Better, Faster, Stronger.

• YOLO is a state-of-the-art object detection model that is fast and accurate
• It runs an input image through a CNN which outputs a 19x19x5x85 dimensional volume.
• The encoding can be seen as a grid where each of the 19x19 cells contains information about 5 boxes.
• You filter through all the boxes using non-max suppression. Specifically:
  • Score thresholding on the probability of detecting a class to keep only accurate (high probability) boxes
  • Intersection over Union (IoU) thresholding to eliminate overlapping boxes
• Because training a YOLO model from randomly initialized weights is non-trivial and requires a large dataset as well as lot of computation, we used previously trained model parameters in this exercise.

• Yolo-v4 and Yolo-v3/v2 for Windows and Linux
• Object Detection for Dummies
  • Part 1: Gradient Vector, HOG, and SS
  • Part 2: CNN, DPM and Overfeat
  • Part 3: R-CNN Family
  • Part 4: Fast Detection Models