Build your own AI-based Object Detection under 30-minutes Use this kandi 1-Click Solution kit to build an AI Powered Object Detector in minutes with this fully editable source code. The entire solution is available as a package to download from the source code repository. ✅ Build an application that can (a) localize and classify objects, (b) detect objects in a video stream ✅ Create 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 ✅ Modify source code to customize as per your requirements

Download the installer; follow Kit Deployment instructions to deploy this in minutes and customize it as per your requirements.

Kit Deployment Instructions

⬇️ Download, extract and double-click kit_installer file to install the kit. 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 '' 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

Below libraries and model collections helps to create the machine learning models for the core recognition use cases in our solution. We will use pytorch to load pre-trained models of Object detection.


If you need help to use this kit, you can email us at or direct message us on Twitter Message @OpenWeaverInc.