Congratulations on choosing to participate in the Virtual Internship Program on AI Engineering with Open Weaver! This 4-week program is designed to be an interactive & practical internship and will help you gain industry-ready skills through project-based learning. The internship will consist of 3 Bootcamps (1 per week) , a few coding exercises, a project, and a final assessment.
WEEK 3 - AI ENGINEERING
Today, our focus will be on how AI Object Detector helps to build Computer Vision-based applications for face detection, vehicle detection, pedestrian counting, web images, security systems, and driverless cars with this ready-to-deploy template application. Through today’s Bootcamp and this week’s exercises, we will learn how to apply Computer Vision techniques for Building an Object Detector Engine on importing Computer Vision libraries and PyTorch, loading pre-trained models, and performing real-time detection.
Learning Objectives
After completing this course you will:
- Have a good working knowledge of the Fundamentals of Computer Vision.
- Learn various concepts in building an Object Detector, including image augmentation techniques for dataset preparation, utilization of existing datasets like COCO or Pascal, and understanding the inner workings of Neural Networks.
- Have a fully functional object detector prototype that you can customize and fine tune the model to enhance its performance.
10-Min Tutorial
AI Object Detector is developed using advanced computer vision algorithms and deep learning frameworks. The system is trained to identify objects within images or videos based on their visual characteristics. By inputting your own data into the detector, it provides a list of objects it recognizes with high accuracy.
Watch this tutorial on building your own AI Object Detector Engine & learn how to train the model, and use Computer Vision algorithms like convolutional neural networks (CNNs) and deep learning frameworks such as TensorFlow or PyTorch. Revisit the concepts discussed during the live bootcamp session in this 10-min tutorial video. If you would like to watch the recording of the entire bootcamp please click HERE.
Practical Exercise
Click the below button to access the Object Detector kandi kit. This kit has all the required dependencies and resources you need to build your application.
Click on the 1-Click Installer button on the kandi kit page to install the Object Detector kit. On installing and running this kit, you will have a working model that you can customize and use in your project.
Final Project
Congratulations on reaching the final stage of your internship! Please take some time to carefully complete the intriguing Final Assessment & Project Submission . Upon successful completion, you will be awarded your highly anticipated Internship Completion Certificate!
It is mandatory for you to complete your weekly Coding Exercises in order to receive your Internship Completion Certificate.
Below are three sample coding exercises that will help you advance in your journey in AI Object Detector. To get started, use the relevant keywords to search for simple code snippets in the search bar on kandi.
Sample Exercise 1 - Make an image blurred using opencv: To make an image blurry, you can use the GaussianBlur() method of OpenCV . The GaussianBlur() uses the Gaussian kernel, similarly there are various other techniques as Averaging, Median Blurring, Bilateral Filtering, etc.
Sample Exercise 2 - Convert image to grayscale using opencv: Grayscaling is the process of converting an image from other color spaces e.g. RGB, CMYK, HSV, etc. to shades of gray. It varies between complete black and complete white.
Sample Exercise 3 - Resize image using opencv: Image resizing refers to the scaling of images. It helps in reducing the number of pixels from an image and that has several advantages e.g. It can reduce the time of training of a neural network as the more the number of pixels in an image more is the number of input nodes that in turn increases the complexity of the model.
Support
Reach out to us by clicking on the reply button below for any help you may need with this course. You may also use the chat feature for support. To access the reply and chat feature, please sign-in to the the Community.
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