Official English Documentation for ImageAI!¶
ImageAI is a python library built to empower developers, reseachers and students to build applications and systems with self-contained Deep Learning and Computer Vision capabilities using simple and few lines of code. This documentation is provided to provide detailed insight into all the classes and functions available in ImageAI, coupled with a number of code examples. ImageAI is a project developed by Moses Olafenwa and John Olafenwa , the DeepQuest AI team.
The Official GitHub Repository of ImageAI is https://github.com/OlafenwaMoses/ImageAI
ImageAI requires that you have Python 3.5.1 or higher installed as well as some other Python libraries and frameworks. Before you install ImageAI, you must install the following dependencies.
Python 3.5.1 or higher, Download Python
pip3 , Download PyPi
Tensorflow 1.4.0 or higher
pip3 install --upgrade tensorflow
pip3 install opencv-python
pip3 install keras
pip3 install imageai --upgrade
Once ImageAI is installed, you can start running very few lines of code to perform very powerful computer visions tasks as seen below.
Recognize 1000 different objects in images
- convertible : 52.459555864334106
- sports_car : 37.61284649372101
- pickup : 3.1751200556755066
- car_wheel : 1.817505806684494
- minivan : 1.7487050965428352
Image Object Detection
Detect 80 most common everyday objects in images.
Video Object Detection
Detect 80 most common everyday objects in videos.
Video Detection Analysis
Generate time based analysis of objects detected in videos.
Custom Image Recognition Training and Inference
Train new image new deep learning models on recognize custom objects
Custom Objects Detection Training and Inference
Train new YOLOv3 models to detect custom objects
Follow the links in the Content section below to see all the code samples and full documentation of available classes and functions.