# OlafenwaMoses/ImageAI

A python library built to empower developers to build applications and systems  with self-contained Computer Vision capabilities

Repository: https://github.com/OlafenwaMoses/ImageAI
Canonical: https://ross.abutalabs.com/products/imageai
Homepage: https://www.genxr.co/#products
Language: Python
License: MIT
License Family: permissive
Topics: artificial-intelligence, machine-learning, prediction, image-prediction, python, python3, offline-capable, imageai, artificial-neural-networks, algorithm, image-recognition, object-detection, squeezenet, densenet, video, inceptionv3, detection, gpu, ai-practice-recommendations
Last push: 2024-08-03T09:45:20+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 3089, "days_push": 760, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 8877, forks 2187 (observed 2026-08-28T04:10:26.126728+00:00)

## What it is
ImageAI is a Python library that lets developers add computer vision capabilities like image classification, object detection, and video object detection/tracking with just a few lines of code. It bundles pre-trained deep learning models (e.g., SqueezeNet, DenseNet, InceptionV3, RetinaNet/YOLO variants) and supports training custom models, all running offline.

## Use cases
- detect objects in images with python
- classify images using pretrained models offline
- detect and track objects in video files
- train a custom image recognition model
- add computer vision to a python app with few lines of code
- run object detection on gpu without cloud services

## When to choose
- you want simple, high-level APIs for image prediction and object detection in Python
- you need offline, self-contained inference without cloud APIs
- you want to train custom image classification models easily
- you're prototyping computer vision features quickly

## When to avoid
- you need state-of-the-art or highly customizable detection pipelines - use Ultralytics YOLO or MMDetection directly
- you need generative or multimodal vision tasks beyond detection/classification
- you require very active development and frequent updates - the project is in maintenance mode

## Facets
- artifact type: library
- maturity: maintenance
- function: computer-vision, image-processing, machine-learning, deep-learning, video-processing
- domain: computer-vision, image-processing, machine-learning, deep-learning, artificial-intelligence
- platform: python, cross-platform
- tags: object-detection, image-recognition, video-detection, object-tracking, custom-model-training, offline-capable, simplicity, gpu

## Member repositories
- OlafenwaMoses/ImageAI (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:26.126728+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:24:45.331672+00:00, confidence not recorded.
  - readme: https://github.com/OlafenwaMoses/ImageAI (fetched 2026-08-28T04:10:26.126728+00:00, sha 2f13eaf2ee3f)
  - homepage: https://www.genxr.co/#products (fetched 2026-08-29T08:24:58.495938+00:00, sha 3fa99aa40d19)
  - registry_pypi: https://pypi.org/pypi/imageai/json (fetched 2026-08-29T08:24:58.504922+00:00, sha 2131563f3f64)
- Data as of 2026-08-30T08:39:29.467469+00:00.