What you would learn in YOLO: Custom Object Detection in Python course?
Object Detection is the most utilized software that makes up Computer Vision, where a computer/machine can identify and classify an object in an image.
In this course, we will be using the YOLO (You Only Look Once) as a powerful and popular unifying object detection model. YOLO makes use of neural networks to offer instantaneous object detection. This algorithm is well-known due to due to its efficiency and precision. It is used in numerous applications to recognize traffic signals, pedestrians' parking meters, and animals.
The course is divided into two parts. The first part focuses on detecting objects using customized datasets that find 20 types of objects. The second half will develop a web application that will provide the GUI experience for users. We will also put our design on the Cloud platform.
Let's now look at the topics covered in the course
A brief introduction to the theory behind the YOLO Object Detection
In this section, I will discuss the history behind Object Detection
Object Detection Metrics like IoU (Intersection Over Union), Precision, mean Average Precision (mAP), etc.
Then we will look at the mathematical reasoning behind YOLO
Additionally, I will explain how YOLO has improved with each new version
Once that is done, we prepare our PC to run Python programming by installing and downloading the Python package. Then, we will test and verify that everything is installed correctly.
2. Information Preparation for the YOLO model
In this section, we will put everything we learned to practice. This is a hands-on course where we'll write Python code and pandas data frames to create the data.
a. Rule of thumb to follow when you Collect Data
B. Label image to aid in the detection of objects: In this case, we will be using the tool LabelImg, which is an open-source program that can mark the object.
C. Parse information from XML files and extract details such as filename, size, bounding box information such as (xmin, atmax, xmax,)
D. Then, process the data in XML into a pandas-like data frame. Then divide the image, and save the label information for the test and train.
3. Train YOLO V5 Model
4. Create a Web App
This is all there is to know about the subjects included in this short course. The source code, images, and weights used in this course are uploaded and distributed in an archive, andI will provide a URL to download them in the final session or the resources section of this course. You are welcome to incorporate the code into your projects, with no need to ask questions.
Additionally, after you have completed this course, you will be presented with a certificate of completion that will enhance your resume.
Course Content:
- Python built YOLO object detection by using Custom Trained Dataset Models.
- YOLO Custom Training
- YOLO V5 Object Detection
- Develop Web App for Object Detection
Download YOLO: Custom Object Detection in Python from below links NOW!
