A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities
MIT License
An open-source python library built to empower developers to build applications and systems with self-contained Deep Learning and Computer Vision capabilities using simple and few lines of code.
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We the creators of ImageAI are glad to announce 2 new AI projects to provide state-of-the-art Generative AI, LLM and Image Understanding on your personal computer and servers.
Install Jarvis on PC/Mac to setup limitless access to LLM powered AI Chats for your every day work, research and generative AI needs with 100% privacy and full offline capability.
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TheiaEngine, the next-generation computer Vision AI API capable of all Generative and Understanding computer vision tasks in a single API call and available via REST API to all programming languages. Features include
Visit https://www.genxr.co/theia-engine to try the demo and join in the beta testing today.
Developed and maintained by Moses Olafenwa
Built with simplicity in mind, ImageAI supports a list of state-of-the-art Machine Learning algorithms for image prediction, custom image prediction, object detection, video detection, video object tracking and image predictions trainings. ImageAI currently supports image prediction and training using 4 different Machine Learning algorithms trained on the ImageNet-1000 dataset. ImageAI also supports object detection, video detection and object tracking using RetinaNet, YOLOv3 and TinyYOLOv3 trained on COCO dataset. Finally, ImageAI allows you to train custom models for performing detection and recognition of new objects.
Eventually, ImageAI will provide support for a wider and more specialized aspects of Computer Vision
New Release : ImageAI 3.0.2
What's new:
To install ImageAI, run the python installation instruction below in the command line:
Download and Install Python 3.7, Python 3.8, Python 3.9 or Python 3.10
Install dependencies
CPU: Download requirements.txt file and install via the command
pip install -r requirements.txt
or simply copy and run the command below
pip install cython pillow>=7.0.0 numpy>=1.18.1 opencv-python>=4.1.2 torch>=1.9.0 --extra-index-url https://download.pytorch.org/whl/cpu torchvision>=0.10.0 --extra-index-url https://download.pytorch.org/whl/cpu pytest==7.1.3 tqdm==4.64.1 scipy>=1.7.3 matplotlib>=3.4.3 mock==4.0.3
GPU/CUDA: Download requirements_gpu.txt file and install via the command
pip install -r requirements_gpu.txt
or smiply copy and run the command below
pip install cython pillow>=7.0.0 numpy>=1.18.1 opencv-python>=4.1.2 torch>=1.9.0 --extra-index-url https://download.pytorch.org/whl/cu102 torchvision>=0.10.0 --extra-index-url https://download.pytorch.org/whl/cu102 pytest==7.1.3 tqdm==4.64.1 scipy>=1.7.3 matplotlib>=3.4.3 mock==4.0.3
If you plan to train custom AI models, download requirements_extra.txt file and install via the command
pip install -r requirements_extra.txt
or simply copy and run the command below
pip install pycocotools@git+https://github.com/gautamchitnis/cocoapi.git@cocodataset-master#subdirectory=PythonAPI
Then run the command below to install ImageAI
pip install imageai --upgrade
We have provided full documentation for all ImageAI classes and functions. Visit the link below:
ImageAI provides abstracted and convenient implementations of state-of-the-art Computer Vision technologies. All of ImageAI implementations and code can work on any computer system with moderate CPU capacity. However, the speed of processing for operations like image prediction, object detection and others on CPU is slow and not suitable for real-time applications. To perform real-time Computer Vision operations with high performance, you need to use GPU enabled technologies.
ImageAI uses the PyTorch backbone for it's Computer Vision operations. PyTorch supports both CPUs and GPUs ( Specifically NVIDIA GPUs. You can get one for your PC or get a PC that has one) for machine learning and artificial intelligence algorithms' implementations.
For anyone interested in building AI systems and using them for business, economic, social and research purposes, it is critical that the person knows the likely positive, negative and unprecedented impacts the use of such technologies will have. They must also be aware of approaches and practices recommended by experienced industry experts to ensure every use of AI brings overall benefit to mankind. We therefore recommend to everyone that wishes to use ImageAI and other AI tools and resources to read Microsoft's January 2018 publication on AI titled "The Future Computed : Artificial Intelligence and its role in society". Kindly follow the link below to download the publication.
https://blogs.microsoft.com/blog/2018/01/17/future-computed-artificial-intelligence-role-society
You can cite ImageAI in your projects and research papers via the BibTeX entry below.
@misc {ImageAI,
author = "Moses",
title = "ImageAI, an open source python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities",
url = "https://github.com/OlafenwaMoses/ImageAI",
month = "mar",
year = "2018--"
}