What is object detection for computer vision? | AIFEE

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Deep learning for object detection using Tensorflow 2

Object detection as a concept in computer vision

Introduction and course outline (6:40)
What is object detection for computer vision? (2:43)
Object detection can be for multiple objects in the image (1:46)
Why deep learning for object detection? (2:02)
The two categories of neural networks used for object detection (3:49)
High level overview of Faster RCNN (11:49)
High level overview of SSD (10:35)
High level overview of YOLOv3 (12:48)

How to choose the right neural network for your object detection task

Introduction (1:57)
Speed and accuracy tradeoffs in object detection (reference article) (1:26)
Accuracy VS time (plot in article) (5:19)
Object detector accuracy with respect to feature extractor accuracy (4:54)
Accuracy stratified by object size (4:25)
How the image resolution affects your object detection model (4:38)
How YOLO compares to SSD and Faster RCNN (8:39)
Summary on how to choose the right model for your object detection task (9:50)

Software setup

Linux installation : How to install tensorflow 2 with GPU support (part 1) (9:09)
Linux installation : How to install tensorflow 2 with GPU support (part 2) (11:14)
Linux installation : How to install tensorflow 2 object detection API (7:07)
Windows installation : Installing miniconda (2:15)
Windows installation : Create virtual environment (1:58)
Windows installation : Installing tensorflow 2 object detection API (13:23)
Windows installation : Installing tensorflow with GPU support (23:01)

Data for object detection

Data preparation for object detection (4:12)
The dataset that we will use to build an object detection model (9:38)
Downloading and setting up our annotation tool : labelImg (11:24)
Annotating the dataset (12:25)
Transforming our xml files into one csv file (12:39)
Creating a labelmap for our dataset (4:04)
The tool that we will use to generate tfrecords (1:59)
Generating tfrecords (7:09)

Training an object detection model using Tensorflow 2 object detection API

Overview of the steps needed to build an object detector (2:02)
Transfer learning (4:49)
Downloading the pretrained model and getting its corresponding config file (8:38)
Preparing your config file (10:33)
Running the training and testing for experimentation (18:38)
Running Tensorboard to analyse the development of the loss and precision (9:35)
Settings for training and evaluating a Faster RCNN model on your local machine (4:11)
Training and evaluating an SSD based object detection model (9:58)
Running the training for SSD object detector (10:05)
Other important settings in your config file : anchor boxes (10:52)
Other important settings in your config file : data augmentation (8:21)

Training object detection API models using Google Cloud AI Platform

What is cloud computing and what is AI Platform? (optional) (5:48)
Creating a Google Cloud account (5:28)
Downloading Google Cloud SDK (5:50)
Creating a google bucket and uploading data to it (8:32)
Preparing our config file for training on google cloud (7:51)
Running the training using Faster RCNN model (22:57)
Running the evaluation during the training (8:16)
Analyzing the results after the training of Faster RCNN model is finished (16:51)
Possible things to do to improve our model performance (2:52)
Downloading the trained model and exporting the frozen model from checkpoints (24:51)
Running the frozen model on new examples locally (20:03)
Run training and evaluation using SSD model (17:10)
Analyzing the results after the training of SSD model is finished (8:29)

YOLO v3 for object detection

The new setup for training YOLOv3 model (repository) (4:08)
Installing the necessary requirements to run the code (2:34)
How will the dataset change to account for YOLO neural network (4:49)
Preparing the dataset : problem with our current dataset (2:40)
Preparing the dataset : getting the data from the original source (5:38)
Preparing the dataset : transforming our previous dataset to the right format (17:01)
Preparing the dataset : adding classes names (1:38)
Preparing the dataset : exploring and changing the config file (16:08)
Training YOLO v3 based object detection model (3:29)
Analyzing the results of the training (8:40)
Evaluating our YOLOv3 trained model using new images (7:28)
Computing the mean average precision of our trained model (6:21)
Other quantitative results (3:03)
Saving our model as a SavedModel format for production deployment (6:33)
Using our trained model to make predictions on new images (16:57)

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