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Build AI Powered Object Detector
by kandikits
AI Object Detection is used 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.
Using this 1-click install kandi kit you can build an application that can (a) localize and classify objects, (b) detect objects in a video stream. You can download this pre-trained model and run it on any device. It is fast and very effective at identifying objects in images with high accuracy (99%). It also provides many advanced features like face detection, smile detection, etc. without any extra effort from your side!
kandi kit provides you with a fully deployable AI Object Detection engine. Source code included so that you can customize it for your requirement.
While you are downloading this kit, here are other 1-click ready to deploy projects to try:
✅ Build AI Powered Virtual Assistant | ✅ Build Python Paraphrase Generator for NLP
You can also find popular libraries for: 🔎 Object Detector | 🔎 NLP | 🔎 AI LibrariesTraining and Certification - How to build AI Powered Object Detector
Watch this self-guided tutorial on importing computer vision libraries and pytorch, load pre-trained model and real-time detection to build your own Object Detector using Artificial Intelligence.
Completed the training? Apply for your Participation Certificate and Achievement Certificate now!
Tag us on social media with a screenshot or video of your working application for a chance to be featured as an Open Source Champion and get a verified badge.
Kit Deployment Instructions
Download the 1-Click kit installer file to get started. After download, extract this zip, run the file and follow the next steps below.
Note: Do ensure to extract the zip file before running it.
Follow the below instructions to deploy and run the solution.
1. After successful installation of the kit, locate the zip file 'realtime-object-detection.zip'
2. Extract the zip file and navigate to the directory 'realtime-object-detection'
3. Open command prompt in the extracted directory 'realtime-object-detection' and run the command 'jupyter notebook'
4. Locate and open the 'Realtime Object Detection.ipynb' notebook from the Jupyter Notebook browser window.
5. Execute cells in the notebook
Kit Solution Source
Object detection engine created using this kit are added in this section. The entire solution is available as a package to download from the source code repository.
Development Environment
VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers.
Jupyter Notebook is used for our development.
Image Preparation and Processing
These libraries help in preparing data by annotating and labelling images. Also processes images for running machine learning algorithm.
We use opencv library for capturing frames from live streaming videocam.
Machine Learning
There are libraries and model collections that help to create machine learning models for the core recognition use cases in our solution.
We will use pytorch to load pre-trained models of Object detection.
Support
If you need help to use this kit, you can email us at kandi.support@openweaver.com or direct message us on Twitter Message @OpenWeaverInc.