Deep Learning for Object Detection with Python and PyTorch

Name: Deep Learning for Object Detection with Python and PyTorch
Rating: 4.1 (175 reviews)

Object Detection for Computer Vision using Deep Learning with Python. Train and Deploy (Detectron2, Faster RCNN, YOLO11)

Created byMazhar Hussain, AI & Computer Science School

Last updated 6/2026

English

English [Auto],

What you'll learn

Learn Object Detection with Python and Pytorch Coding
Learn Object Detection using Deep Learning Models
Single-Stage Object Detection vs Two-Stage Objection Detection with Python
Learn RCNN, Fast RCNN, Faster RCNN, Mask RCNN and YOLO8, YOLO11 Architectures
Perform Object Detection with Fast RCNN and Faster RCNN
Perform Real-time Video Object Detection with YOLOv8 and YOLO11
Train, Test and Deploy YOLOv8 for Video Object Detection
Introduction to Detectron2 by Facebook AI Research (FAIR)
Preform Object Detection with Detectron2 Models
Explore Custom Object Detection Datasets with Annotations
Perform Object Detection on Custom Datasets using Deep Learning
Train, Test, Evaluate Your Own Object Detection Models and Visualize Results
Perform Object Instance Segmentation at Pixel Level using Mask RCNN
Perform Object Instance Segmentation on Custom Dataset with Pytorch and Python

Course content

21 sections • 40 lectures • 3h 43m total length

Introduction2:32
Explore deep learning for object detection with Python and PyTorch, building and evaluating models that classify objects and predict bounding boxes for real-world applications.

Introduction to CNN8:18
Explore how convolutional neural networks perform image classification and empower object detection and segmentation, detailing core blocks—convolutional layers, activations, pooling, and fully connected layers—and tracing architectures from LeNet to ResNet.
RCNN Deep Learning Architectures8:24
Explore region-based convolutional neural networks for object detection, detailing the two-stage R-CNN approach with region proposals, selective search, CNN feature extraction, and bounding box refinement.
Fast RCNN Deep Learning Architecture5:16
Explore the faster r-cnn architecture, unifying training into one model with region of interest pooling over region proposals, shared feature maps, and fast classification and regression.
Faster RCNN Deep Learning Architectures3:15
Explore how faster r-cnn unifies a region proposal network with fast r-cnn, sharing features for end-to-end training and near real-time object detection.
Mask RCNN Deep Learning Architectures4:24
Explore mask r-cnn architecture for instance segmentation and object detection, highlighting region of interest align, a parallel mask branch, RPN proposals, and pixel-level segmentation.

Set-up Google Colab for Writing Python Code5:11
Set up and run Python code in Google Colab to practice PyTorch without installation, leveraging GPUs or TPUs, and saving notebooks to Google Drive for easy sharing.
Connect Google Colab with Google Drive to Read and Write Data2:43
Connect Google Colab with Google Drive to read and write data by mounting the drive and granting access for seamless data transfer.

Detectron2 for Ojbect Detection with PyTorch18:10
Detectron2, a modular PyTorch library for fast object detection and segmentation, featuring Faster R-CNN, RetinaNet, and RPN with pre-trained models from the model zoo.
Perform Object Detection using Detectron2 Pretrained Models10:41
Explore object detection with Detectron2 pre-trained models by loading model zoo configurations, applying faster R-CNN with FPN and ResNet on COCO data, and visualizing bounding boxes.
Python and PyTorch Code2:00

Requirements

Object Detection using Deep Learning with Python and PyTorch is taught in this course by following a complete pipeline from Zero to Hero
No prior knowledge of Semantic Segmentation is assumed. Everything will be covered with hands-on trainings
A Google Gmail account is required to get started with Google Colab to write Python Code

Description

Are you ready to dive into the fascinating world of object detection using deep learning? In our comprehensive course "Deep Learning for Object Detection with Python and PyTorch", we will guide you through the essential concepts and techniques required to detect, classify, and locate objects in images. Object Detection has wide range of potential real life application in many fields. Object detection is used for autonomous vehicles to perceive and understand their surroundings. It helps in detecting and tracking pedestrians, vehicles, traffic signs, traffic lights, and other objects on the road. Object Detection is used for surveillance and security using drones to identify and track suspicious activities, intruders, and objects of interest. Object Detection is used for traffic monitoring, helmet and license plate detection, player tracking, defect detection, industrial usage and much more.

With the powerful combination of Python programming and the PyTorch deep learning framework, you'll explore state-of-the-art algorithms and architectures like R-CNN, Fast RCNN and Faster R-CNN. Throughout the course, you'll gain a solid understanding of Convolutional Neural Networks (CNNs) and their role in Object Detection. You'll learn how to leverage pre-trained models, fine-tune them for Object Detection using Detectron2 Library developed by by Facebook AI Research (FAIR).

The course covers the complete pipeline with hands-on experience of Object Detection using Deep Learning with Python and PyTorch as follows:

Course BreakDown:

Learn Object Detection with Python and Pytorch Coding
Learn Object Detection using Deep Learning Models
Introduction to Convolutional Neural Networks (CNN)
Learn RCNN, Fast RCNN, Faster RCNN, Mask RCNN and YOLO8, YOLO11 Architectures
Perform Object Detection with Fast RCNN and Faster RCNN
Perform Real-time Video Object Detection with YOLOv8 and YOLO11
Train, Test and Deploy YOLOv8 for Video Object Detection
Introduction to Detectron2 by Facebook AI Research (FAIR)
Preform Object Detection with Detectron2 Models
Explore Custom Object Detection Dataset with Annotations
Perform Object Detection on Custom Dataset using Deep Learning
Train, Test, Evaluate Your Own Object Detection Models and Visualize Results
Perform Object Instance Segmentation at Pixel Level using Mask RCNN
Perform Object Instance Segmentation on Custom Dataset with Pytorch and Python

By the end of this course, you'll have the knowledge and skills you need to start applying Deep Learning to Object Detection problems in your own work or research. Whether you're a Computer Vision Engineer, Data Scientist, or Developer, this course is the perfect way to take your understanding of Deep Learning to the next level. Let's get started on this exciting journey of Deep Learning for Object Detection with Python and PyTorch.

See you inside the class!

Who this course is for:

This course is designed for a wide range of Students and Professionals, including but not limited to: Machine Learning Engineers, Deep Learning Engineers, Data Scientists, Computer Vision Engineers, and Researchers who want to learn how to use PyTorch to build and train deep learning models for Object Detection
In general, the course is for anyone who wants to learn how to use Deep Learning to extract meaning from visual data and gain a deeper understanding of the theory and practical applications of Object Detection using Python and PyTorch

Deep Learning for Object Detection with Python and PyTorch

What you'll learn

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Course content

Introduction to Course1 lecture • 3min

Object Detection and How it Works1 lecture • 7min

Single-shot vs Two-shot Object Detection2 lectures • 2min

Deep Learning Architectures for Object Detection (R-CNN Family)5 lectures • 30min

Google Colab for Writing Python Code2 lectures • 8min

Detectron2 for Ojbect Detection3 lectures • 31min

Annotation tools to Label Your Own Dataset for Object Detection1 lecture • 2min

Custom Dataset for Object Detection2 lectures • 12min

Training, Evaluating and Visualizing Object Detection on Custom Dataset2 lectures • 17min

Complete Code and Custom Dataset for Object Detection1 lecture • 1min

Requirements

Description

Who this course is for: