Build AI Powered Object Detector
by kandikits Updated: Oct 20, 2022
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!
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.
- Download, extract and double-click kit installer file to install the kit. Ensure you extract the zip file before running it.
- After successful installation of the kit, locate the zip file 'realtime-object-detection.zip'
- Extract the zip file and navigate to the directory 'realtime-object-detection'
- Open command prompt in the extracted directory 'realtime-object-detection' and run the command 'jupyter notebook'
- Locate and open the 'Realtime Object Detection.ipynb' notebook from the Jupyter Notebook browser window.
- Execute cells in the notebook
Click on the button below to download the solution and follow the deployment instructions to begin set-up. This 1-click kit has all the required dependencies and resources you may need to build your Object Detector App.
For a detailed tutorial on installing & executing the solution as well as learning resources including training & certification opportunities, please visit the OpenWeaver Community
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.
Jupyter Interactive Notebook
Jupyter Notebook 9901 Version:v7.0.0a18 License: Permissive (BSD-3-Clause)
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.
Automated CI toolchain to produce precompiled opencv-python, opencv-python-headless, opencv-contrib-python and opencv-contrib-python-headless packages.
Shell 3346 Version:72 License: Permissive (MIT)
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.
Tensors and Dynamic neural networks in Python with strong GPU acceleration
C++ 64580 Version:v2.0.0 License: Others (Non-SPDX)
Kit Solution Source
Detects objects in images/streaming video
Jupyter Notebook 0 Version:v1.0.0 License: Strong Copyleft (GPL-3.0)