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Image & Video AI

Imageai

ImageAI is an open-source Python library that developers use to add deep-learning computer-vision capabilities to applications, including image prediction, object and video detection, object tracking, and custom model training.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Imageai — OpenViST project profile in Image & Video AI.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use ImageAI when you are building a Python application and want library-based image or video recognition workflows, rather than a hosted analysis service.

Who it is for

  • Python developers adding computer-vision workflows to applications or systems.
  • Teams building image classification, object detection, video analysis or custom recognition features.

Documented uses

  • Classify or predict image contents using documented ImageNet-1000-trained algorithms.
  • Detect objects in images or video and track objects in video.
  • Train custom models for detecting or recognizing new objects.
Before you choose

Requirements & limits

The repository identifies ImageAI as open source and MIT-licensed.

  • The README lists Python 3.7 through 3.10 and dependencies including PyTorch, torchvision, NumPy, Pillow and OpenCV.
  • CPU and GPU/CUDA dependency instructions are provided separately.
  • Custom-model training requires an additional requirements file.
  • The documented image-prediction algorithms are trained on ImageNet-1000; object and video detection options named in the README include RetinaNet, YOLOv3 and TinyYOLOv3 trained on COCO.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up ImageAI with CPU dependencies

Install the documented Python package and CPU dependency set.

Before you start

  • Python 3.7, 3.8, 3.9 or 3.10
  • Command-line access

Steps

  1. Install one of the Python versions listed in the README.

  2. Install the CPU dependencies using the repository's requirements.txt file.

  3. Install ImageAI with pip install imageai --upgrade.

Expected outcome

The documented installation stages install ImageAI and its CPU dependency set.

Guide limitations

  • This covers installation only, not loading a model or running an inference workflow.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
14 October 2026
Original website title
OlafenwaMoses/ImageAI · Official homepage title · retained in its source language
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