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