Welcome

Detecting objects in an image is an important task for machines:

• Self-driving cars need to detect pedestrians and traffic signs on the street; Cool YouTube video for ADAS

• Driving assistant technologies should find the eye's gazing direction to ensure that the driver is focused on driving; Driver Drowsiness Warning System (Ex1), Driver Drowsiness Warning System (Ex2)

This notebook gives step by step instruction to set up the environment to run the codes Use pretrained YOLO network for object detection, SJSU data science night.

Table of contents

• Setup environment

  • Install Anaconda 3.7

  • Set up virtual environment

  • Install python modules Tensorflow (v1.9.0), Keras (v2.1.2) and opencv3 (v3.4.2)

• Download workshop specific codes

Setup

Please take the following steps in Max OSX (Sorry for Windows users).

This is the seventh and final blog post of Object Detection with YOLO blog series. This blog performs inference using the model in trained in Part 5 Object Detection with Yolo using VOC 2012 data - training. I will use PASCAL VOC2012 data. This blog assumes that the readers have read the previous blog posts - Part 1

This is the sixth blog post of Object Detection with YOLO blog series. This blog performs inference using the model in trained in Part 5 Object Detection with Yolo using VOC 2012 data - training. I will use PASCAL VOC2012 data. This blog assumes that the readers have read the previous blog posts - Part 1

This is the fifth blog post of Object Detection with YOLO blog series. This blog finally train the model using the scripts that are developed in the previous blog posts. I will use PASCAL VOC2012 data. This blog assumes that the readers have read the previous blog posts - Part 1

experiencor/keras-yolo2's YOLO V2 loss¶

This is the fourth blog post of Object Detection with YOLO blog series. This blog discusses the YOLO's loss funciton. This will be the most intense blog post in Object Detection with YOLO blog series. as loss function of YOLO is quite complex. So please get excited! For demonstration of the code, I will agian use PASCAL VOC2012 data. This blog assumes that the readers have read the previous blog posts - Part 1

This is the third blog post of Object Detection with YOLO blog series. This blog discusses the YOLO's model architecture. I will use PASCAL VOC2012 data. This blog assumes that the readers have read the previous two blog posts - Part 1, Part 2.

Andrew Ng's YOLO lecture¶

• Neural Networks - Bounding Box Predictions

The screenshot of Andrew Ng's YOLO lecture

This is the second blog post of Object Detection with YOLO blog series. This blog discusses the YOLO's input and output encoding. I will use PASCAL VOC2012 data.

This blog assumes that the readers have watched Andrew Ng's YOLO lectures on youtube. Specifically, the following 5 videos. Each of these videos are (of course free and) about 10 minutes and in total it takes less than 45 minitues. So please watch through all the videos.

This is the first blog post of Object Detection with YOLO blog series. The goal of this blog series is to understand the state-of-art object detection algorithm, called YOLO (you only look once). Allegedly, Andrew Ng mentioned that YOLO is the best object detection algorithm in his course. See the youtube video below:

This is part of the blog series for Object Detection with R-CNN.

Cited from VGG in TensorFlow.

In this blog, we are now ready to train our classifier for object detection. We will use a large pre-trained CNN to extract a fixed-length feature vector from each region, and then create artificial neural networks that mapps the feature vector to the object class. We will focus on detecting a person.