Object detection is an essential task in computer vision that involves identifying objects within digital images and videos. With the increasing importance of this technology, there is a growing demand for effective training programs that can help individuals acquire the necessary skills and knowledge to become proficient in object detection. Online courses have emerged as a popular choice for many learners, thanks to their flexibility and accessibility. In this article, we will explore some of the best object detection courses available online, highlighting their features, benefits, and learning outcomes.
Here’s a look at the Best Object Detection Courses and Certifications Online and what they have to offer for you!
Tensorflow Object Detection Online Course
- Tensorflow Object Detection Online Course
- 1. Object Detection Web App with TensorFlow, OpenCV and Flask by Yaswanth Sai Palaghat (Udemy) (Our Best Pick)
- 2. Train YOLO for Object Detection with Custom Data by Valentyn Sichkar (Udemy)
- 3. YOLOv4 Object Detection Course by Augmented Startups, Geeky Bee AI Private Limited (Udemy)
- 4. Computer Vision: Python OCR & Object Detection Quick Starter by Abhilash Nelson (Udemy)
- 5. Computer Vision: YOLO Custom Object Detection with Colab GPU by Abhilash Nelson (Udemy)
- 6. Deep learning for object detection using Tensorflow 2 by Nour Islam Mokhtari (Udemy)
- 7. YOLOv3 – Robust Deep Learning Object Detection in 1 hour by Augmented Startups (Udemy)
- 8. YOLO: Automatic License Plate Detection & Extract text App by Data Science Anywhere, Gusksra R (Udemy)
- 9. OpenCV Practical with Python – 3 Complete Projects + CODE by Up Degree (Udemy)
- 10. Deep learning :End to End Object Detection Masters by Ineuron Intelligence (Udemy)
1. Object Detection Web App with TensorFlow, OpenCV and Flask by Yaswanth Sai Palaghat (Udemy) (Our Best Pick)
The Object Detection Web App with TensorFlow, OpenCV and Flask course, taught by Yaswanth Sai Palaghat, aims to teach students how to build an object detection model from scratch using deep learning and transfer learning. This course is designed for those interested in computer vision and its applications in self-driving cars, robotics, and image captioning. The focus of the course is on detecting objects and naming them from images, a challenging and fascinating field of computer vision.To achieve the course objectives, the instructor will use Python’s OpenCV library, which is built on top of the pre-trained Coco dataset. The model will be deployed as a web app using Flask, a Python framework. Students will learn about various technologies and tools such as Python, machine learning, deep learning, transfer learning, TensorFlow, OpenCV, and Flask.The course is divided into three main sections: introduction, getting started, and object detection project. In the introduction section, students will learn about the course objectives and prerequisites. The getting started section will cover the basics of Python and OpenCV. In the object detection project section, students will build an object detection model from scratch using deep learning and transfer learning techniques. They will also learn how to deploy the model as a web app using Flask.
This course, titled Train YOLO for Object Detection with Custom Data, is designed to teach participants how to build their own object detector using YOLO v3-v4 algorithms. The course instructors are Valentyn Sichkar.
Participants will begin by implementing pre-trained YOLO v3-v4 on the COCO dataset to detect objects in images, videos, and real-time using the OpenCV deep learning library. They will then learn to label individual datasets and create custom ones by extracting specific images from a large existing dataset.
Participants will also learn how to convert the Traffic Signs dataset into YOLO format and use the resulting code templates to modify and apply to other datasets in the future. Once the datasets are ready, they will train and test YOLO v3-v4 detectors in the Darknet framework.
The bonus section of the course covers how to build a graphical user interface for object detection using YOLO and PyQt. The course is organized into sections that include video lectures, coding activities, code templates, quizzes, downloadable instructions, and discussion opportunities.
The video lectures of the course have SMART objectives, meaning they are specific, measurable, attainable, result-oriented, and time-oriented. The course covers various topics such as welcome, object detection with YOLO v3-v4, labeling new datasets in YOLO format, creating custom datasets in YOLO format, converting Traffic Signs dataset into YOLO format, training YOLO v3-v4 in Darknet framework, and building PyQt user interface for object detection with YOLO v3-v4.
Overall, this course provides a comprehensive understanding of how to train a custom object detector using YOLO v3-v4 algorithms and is suitable for anyone looking to enhance their skills in this area.
The YOLOv4 Object Detection Course offered by Augmented Startups and Geeky Bee AI Private Limited provides a comprehensive guide on how to implement and train YOLOv4 for object detection. The course was designed to help individuals who are interested in AI object detection in computer vision, including non-programmers/non-computer science, hobbyists, students, researchers, and employees. The course covers the basics of YOLOv4, including installing all the necessary dependencies, executing YOLOv4 on images and videos, and implementing YOLOv4 on real-time applications such as a social distancing monitoring app.
The course is structured into three sections: YOLOv4 Starter Course – Introduction, YOLOv4 Trainers Course, and YOLOv4 PyQT Course. The YOLOv4 Starter Course provides a gentle introduction to the world of computer vision with YOLOv4, while the YOLOv4 Trainers Course covers the basics of building and training convolutional neural networks with YOLOv4. The YOLOv4 PyQT Course delves deeper into building cross-platform apps using YOLOv4 and PyQt.
To take the course, the individual needs to have a basic understanding of computer vision and python programming skills. It is also recommended to have a mid to high range PC/laptop running on Windows 10 and a CUDA-enabled GPU. The course is forward-thinking and aims to equip individuals with the necessary skills to solve real-world problems, freelance AI projects, get AI-related job opportunities, tackle research, and save time and money.
Through the YOLOv4 Object Detection Course, individuals can learn how to implement and train their own convolutional neural networks with YOLOv4 object detection pre-trained model. By the end of the course, individuals can use their newfound expertise to create their own applications and solve real-world problems.
This course is a quick starter for Optical Character Recognition (OCR), Image Recognition, and Object Detection using Python. It is the third course in the Computer Vision series, and these techniques are among the most used applications of Computer Vision. The computer can recognize and classify either the whole image or multiple objects inside a single image, predicting the class of the objects with the percentage accuracy score. Using OCR, it can recognize and convert text in the images to machine-readable formats, like text or a document. Object Detection and Object Recognition are widely used in many applications, including self-driving cars.
The course aims to provide a quick start for those who wish to dive into OCR, Image Recognition, and Object Detection using Python. The course covers the introductory theory session about OCR technology and prepares the computer for Python coding by downloading and installing the anaconda package. The course also covers basic Python programming skills, including assignment, flow-control, functions, and data structures. The dependencies and libraries required for OCR are installed, including Tesseract Library, OpenCV, and Pillow. A step-by-step guide to the OCR program implementation is provided, along with Character Recognition testing and verification of the results.
The course also covers Convolutional Neural Networks (CNN), which are used for Image Recognition. Installing additional dependencies for CNN is covered, with an introduction to VGGNet Architecture. The course then guides the user through Image Recognition using pre-trained models, including VGGNet16, VGGNet19, ResNet, Inception, and Xception models. The course also covers Object Recognition using MobileNet-SSD Pretrained Model, Mask-RCNN Pre-trained model, and YOLO Pre-trained model. The course provides a step-by-step guide to the implementation of these models, along with Real-time and Pre-saved Video Object Detection.
The course title is Computer Vision: YOLO Custom Object Detection with Colab GPU. The instructor is Abhilash Nelson. The course is divided into two parts – the first half deals with object recognition using a predefined dataset called the coco dataset, while the second half teaches how to create a custom dataset and train the YOLO model to create a coronavirus detection model. The course covers topics such as YOLO Object Detection system, python coding using anaconda package, OpenCV library, Convolutional Neural Networks, and Non Maxima Suppression.
The first phase of the custom model includes the preparation steps to implement the custom model, downloading the darknet source from Github, preparing it, downloading the weight files, and editing the configuration files. The second phase involves collecting data to train the model, labeling or annotating coronavirus objects in the images using labelImg, splitting the dataset, and editing the file locations. The third phase involves uploading the files to Google Drive, creating a Google Colab notebook, and unzipping the darknet. The fourth phase involves compiling the darknet framework source code and testing it with a sample image.
The fifth phase includes linking a backup folder in Google Drive to the Colab runtime to save weights periodically during training, and the sixth phase involves training the custom coronavirus model and monitoring the loss for every iteration to obtain the final weight. The course also covers case studies and provides a course completion certificate.
The topics covered in the course include environment setup, python basics, introduction to CNN, YOLO pre-trained object detection, custom trained YOLO model, and other sample real-world case studies. The source code, images, and weights used in the course are provided in a shared folder.
The Deep Learning for Object Detection using Tensorflow 2 course is designed to equip learners with the skills and knowledge needed to train and evaluate deep learning-based object detection models. The course covers Faster R-CNN, SSD, and YOLO v3 models, providing a high-level understanding of how they function.
Using Tensorflow 2, learners will learn how to train and evaluate these models on their local machines. Moreover, they will learn how to use Google Cloud AI Platform to leverage cloud computing and train and evaluate models on powerful GPUs offered by Google.
The course aims to help learners build their intuition on object detection using deep learning, an essential topic in computer vision and deep learning interviews. Additionally, the course teaches learners how to create their own models using custom datasets, enabling them to develop powerful AI solutions.
The course is divided into several sections, including Object Detection as a concept in computer vision, choosing the right neural network for the task, software setup, data for object detection, and training object detection models. The course also covers training object detection API models using Google Cloud AI Platform and YOLO v3 for object detection.
The YOLOv3 – Robust Deep Learning Object Detection in 1 hour course offered by Augmented Startups provides a comprehensive guide to creating custom AI object detection. The course covers the entire workflow, from training to inference, step-by-step. The course aims to help beginners in deep learning, particularly in computer vision, overcome common challenges such as tedious and corrupt label datasets, unclear instructions on model training, and duplicate image management.
To address these challenges, the course suggests using Supervisely, a free object detection workflow tool that simplifies annotation, supports multiple models, and handles duplicate images. The course teaches students how to utilize this workflow by training their own custom YOLOv3 and deploying models using PyTorch. The course is designed to reduce debugging, speed up time to market, and deliver results quickly.
The course covers various topics such as state-of-the-art object detection using pre-trained YOLOv3 models, data gathering and annotation, data augmentation, AI labeling, custom model training, and model deployment. Additionally, students receive helpful bonuses such as neural network fundamentals, personal help within the course, and a certificate of completion upon finishing the course.
The course comes with a 30-day money-back guarantee and is regularly updated to reflect the current marketing landscape. By completing the course, students can demonstrate their expertise and increase their chances of getting hired for AI jobs. The course is also suitable for freelancers looking to expand their client base.
8. YOLO: Automatic License Plate Detection & Extract text App by Data Science Anywhere, Gusksra R (Udemy)
This course, titled YOLO: Automatic License Plate Detection & Extract Text App, is offered by Data Science Anywhere with instructor Gusksra R. The course teaches how to develop license plate object detection and optical character recognition (OCR), and create a web app project using deep learning, TensorFlow 2, and Flask. The course covers modeling techniques, including labeling object detection data, data preprocessing, deep learning model building (InceptionResNet V2), evaluation, and production (web app).
The course starts by introducing project architecture that was followed to develop the app in Python. It then explains how to gather data and label images for object detection of license plates using an open-source image annotation tool developed in Python GUI (PyQT). Next, it covers data preprocessing, deep learning object detection model building, and model evaluation. The course shows how to extract text from images using the Optical Character Recognition API Tesseract in Python (Pytesseract).
Finally, the course shows how to put everything together and build a pipeline deep learning model, followed by instructions on how to create a web app project using Flask Python. The course covers concepts such as URL routing, template rendering, template inheritance, HTML, and Bootstrap.
The course covers the following topics: Introduction, Labeling, Data Processing, Deep Learning for Object Detection, Pipeline Object Detection Model, Optical Character Recognition (OCR), Flask App, Number Plate Web App, Real Time Number Plate Recognition with YOLO, YOLO Number Plate Web App Code, and a bonus section. The course resources include all Notebooks and Py files that will be useful for reference.
The course is designed for individuals who want to enhance their skills in computer vision-based web app development. The course instructors welcome questions and are willing to provide answers in Q&A sessions.
The OpenCV Practical with Python course is designed for intermediate level users who are interested in creating practical computer vision applications using Python and OpenCV. The course focuses on building three interactive projects, including a motion detector app, hand detector app, and face recognition app. Before taking the course, students are expected to have basic knowledge in OpenCV and Python. OpenCV, or Open Source Computer Vision, is an open source library that offers a variety of algorithms for computer vision, image analysis, and machine learning. It allows users to identify faces, recognize objects, classify them, detect hand movements, and more. OpenCV is a multiplatform library available for Windows, Mac, Linux, and Android, and can be programmed with C, C++, Python, Java, and Matlab. The course consists of an overview and environment setup, followed by tools for OpenCV and an introduction to OpenCV and environment setup. The three projects are then covered in detail, including building a motion detector app, a hand detector app, and a face recognition app. The course is designed to be 100% practical, with a focus on app development and its features.
Ineuron Intelligence offers a course entitled Deep Learning: End to End Object Detection Masters Course. The course aims to help learners become skilled in Object Detection by using deep learning frameworks such as Tensorflow, Detectron2, and YoloV5. Through the course, learners will be able to build four different object detectors from scratch and create end-to-end web applications for object detectors. The course includes four real-time projects that use different frameworks.
The course is updated regularly, and the upcoming updates include tutorials on detecto, d2go, mmdetection, and how to use Paperspace and DataCrunch for training. The course will also cover other topics such as how to move from Flask to FastAPI, Dockerizing applications, and deploying applications in the cloud.
The course includes over 100 lectures that focus on practical understanding and implementation. It is designed to train learners to become data scientists and machine learning professionals in the tech industry. The course aims to help learners develop intuition in addressing most questions about object detection using deep learning, which is a common subject in interviews for roles in computer vision and deep learning. The course also shows learners how to create their own models using their own data, allowing them to develop effective Computer Vision solutions.
Learners who enroll in the course will have access to a Skype group, where they can communicate with the instructor and other classmates. The course covers various sections, such as Introduction and Setup, Covering Python Basics, Introduction to Object Detection, Object Detection using Tensorflow 1.x and 2.x, Object Detection using Facebook’s Detectron2, and Introduction to YoloV5. The course includes custom training with TFOD1.x, TFOD2.x, and YoloV5, as well as building web applications with these frameworks.
Overall, the course aims to help learners become object detection experts by using the latest frameworks available in the market.