Technology

物体检测

终于有点时间，可以写继续写notes，这样可以让整个学习过程的印象更加深入。

本节课为CNN的第三周，总的来说就是讲述如何通过pre-train的物体模型，识别整张照片上的物体。和以往前两周的课程一样，围绕着若干篇论文算法展开 Detecting Algorithm

  • Object Localization
  • Landmark Detection, which describe less in the class about how to detect the interal features of an object by key landmarks
  • Object Detection, talking about how to detect an object with bounding box
  • Sliding Window

这里主要重点回顾YOLO（you only look once）。这是本课重点阐述的内容，video有两个，作业也是直接就是讲述YOLO，顺带一点其他算法。

  • 起点，将原图划分成19x19的区块 （方便简化计算）
  • Input image (608, 608, 3)
  • The input image goes through a CNN, resulting in a (19,19,5,85) dimensional output.
  • After flattening the last two dimensions, the output is a volume of shape (19, 19, 425):
    • Each cell in a 19x19 grid over the input image gives 425 numbers.
    • 425 = 5 x 85 because each cell contains predictions for 5 boxes, corresponding to 5 anchor boxes, as seen in lecture.
    • 85 = 5 + 80 where 5 is because (pc,bx,by,bh,bw)(pc,bx,by,bh,bw) has 5 numbers, and and 80 is the number of classes we’d like to detect
  • You then select only few boxes based on:
    • Score-thresholding: throw away boxes that have detected a class with a score less than the threshold
    • Non-max suppression: Compute the Intersection over Union and avoid selecting overlapping boxes
  • This gives you YOLO’s final output.

What you should remember:

  • 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. If you wish, you can also try fine-tuning the YOLO model with your own dataset, though this would be a fairly non-trivial exercise.