Practical Course

Object Detection with PyTorch

keyboard_arrow_right 48% students got a significant career improvement

keyboard_arrow_rightGet skills to work on Object Detection right after the course

keyboard_arrow_rightAdd new projects to your Portfolio

keyboard_arrow_rightFully functional & Tested code that you can use

doneFormat:Runnable notebooks with theory & practice

doneEnvironment:GPU Server included

doneSupport:You can ask question to lector any time

doneLanguage:English

PLAY VIDEOplay_circle_outline

About the Course

Get Demo Lecture

This course was designed as a next step of learning for people who already got basic knowledge from Coursera, Udemy, edX, etc and for people who want to expand their expertise in another area of Machine Learning. We are bridging the gap between theory and practical knowledge so you will be ready for real-life projects, master computer vision, and image processing and get a better job within months of the course completion.

It is created by developers for developers and provides a deep understanding of the object detection task in the computer vision field. Topics of the course will guide you through the path of developing modern object detection algorithms and models.

doneFormat:Runnable notebooks with theory & practice

doneEnvironment:GPU Server included

doneSupport:You can ask question to lector any time

doneLanguage:English

Estimated 1 month to complete

Car counting app with object detection
FREE

Demo Lecture

Road Traffic Counting with OpenCV
access_time4 hours

This lecture will teach you one of the classical ways of detecting objects without using neural networks and also will teach how to build an image processing pipeline for the task - Road Traffic Analytics.

play_circle_filledGet Demo Lecture

WEEK 1

Introduction to PyTorch and Localization

PyTorch Basics
access_time2 h

Single Object Localization
access_timeTheory: 3 h Practice: 2 h

Object detection can be hard if start learning it from the end. This lecture will teach make intro in detection task through simple localization models with using coordinates and masks.

WEEK 2

Single Shot Networks

You Only Look Once (YoloV1)
access_timeTheory: 6 h Practice: 6 h

YOLO - fist network that was aimed at fast inference. In this lecture we will get a deep understanding of its internals and why it was built in such a way and what problems developers tried to solve.

Single Shoot Detector (SSD300)
access_timeTheory: 6 h Practice: 1 d

SSD - doing the same task as YOLO but was developed few months later by another researchers and it more robust then YoloV1. We will learn internals of this network and understand in what things it differs from YoloV1 and why.

WEEK 3

Regional Proposal Networks

Fast R-CNN
access_timeTheory: 8 h Practice: 3 d

Regional Proposal Networks - came to us from classical computer vision and was updated with convolutional networks. In this lecture we will learn the history of such networks and put our hands in internals of Faster RCNN.

Mask R-CNN
access_timeTheory: 8 h Practice: 1.5 d

Object detection evolves every day and today is a good thing to create multi-task networks and not only because then can solve few tasks in the same time, but also because they achive much higher accuracy then ever. In this lecture we take a look on the internals of curent state-of-the-art algorithm - Mask RCNN.

Speeding up models with PyTorch Quantization
access_timeTheory: 3 h Practice: 1h

Once you learn how to build & train deep networks new problem arise - inference speed. In this lecture, I will cover standard ways of model optimization to decrease inference time and model size.

WEEK 4

Course Project

Master your knowledge with Nuclei Detection

Find the nuclei in divergent images to advance medical discovery in Kaggle "Data Science Bowl"

Instructor

Andrey Nikishaev - ML Tech Lead

Andrey Nikishaev

Computer Science & Mathematical departments, Taras Shevchenko National University
Machine Learning & Computer Vision Expert

Entrepreneur and Software Engineer with 15+ years of experience. Specialist in HL+HA and BigData Systems. Was working with Top10 IT product companies. The last 3 years mostly engaged in ML projects. Currently developing a Real-Time gaming recommendation engine heavily based on Computer Vision.

Register to Course

$149

By enrolling you agree with
Distance Learning Service Agreement and Privacy Policy

Articles

Making Backpropagation, Autograd, MNIST Classifier from scratch in Python
Tutorial

Making Backpropagation, Autograd, MNIST Classifier from scratch in Python

Simple practical examples to give you a good understanding of how all this NN/AI things really work

Speeding up model with fusing batch normalization and convolution
Article

Speeding up model with fusing batch normalization and convolution

This is a common technique in quantized and inference optimized models.

Linear Algebra. Points matching with SVD in 3D space
Article

Linear Algebra. Points matching with SVD in 3D space

Learn how to match and translate points in space using SVD

OpenCV. Automatic cropping and image warping
Tutorial

OpenCV. Automatic cropping and image warping

Learn how to find homography transformation between two images

Subscribe for newsletter

Machine Learning World
Ukraine Animal Help
Data Science UA
Google