ImageAI (v3.0.3)

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.

If you will like to sponsor this project, kindly visit the <strong>Github sponsor page</strong>.

---------------------------------------------------

Introducing Jarvis and TheiaEngine.

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.

Visit https://jarvis.genxr.co to get started.

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

• Detect 300+ objects ( 220 more objects than ImageAI)
• Provide answers to any content or context questions asked on an image
  • very useful to get information on any object, action or information without needing to train a new custom model for every tasks
• Generate scene description and summary
• Convert 2D image to 3D pointcloud and triangular mesh
• Semantic Scene mapping of objects, walls, floors, etc
• Stateless Face recognition and emotion detection
• Image generation and augmentation from prompt
• etc.

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:

• PyTorch backend
• TinyYOLOv3 model training

TABLE OF CONTENTS

• <a href="#installation" > :white_square_button: Installation</a>
• <a href="#features" > :white_square_button: Features</a>
• <a href="#documentation" > :white_square_button: Documentation</a>
• <a href="#sponsors" > :white_square_button: Sponsors</a>
• <a href="#sample" > :white_square_button: Projects Built on ImageAI</a>
• <a href="#real-time-and-high-performance-implementation" > :white_square_button: High Performance Implementation</a>
• <a href="#recommendation" > :white_square_button: AI Practice Recommendations</a>
• <a href="#contact" > :white_square_button: Contact Developers</a>
• <a href="#citation" > :white_square_button: Citation</a>
• <a href="#ref" > :white_square_button: References</a>

Installation

<div id="installation"></div>

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
  

Features

<div id="features"></div> <table> <tr> <td><h2> Image Classification</h2> </td> </tr> <tr> <td><img src="data-images/1.jpg" > <h4>ImageAI provides 4 different algorithms and model types to perform image prediction, trained on the ImageNet-1000 dataset. The 4 algorithms provided for image prediction include MobileNetV2, ResNet50, InceptionV3 and DenseNet121. Click the link below to see the full sample codes, explanations and best practices guide.</h4> <a href="imageai/Classification"> >>> Get Started</a> </td> </tr> </table> <div id="features"></div> <table> <tr> <td><h2> Object Detection </h2> </td> </tr> <tr> <td> <img src="data-images/image2new.jpg"> <h4>ImageAI provides very convenient and powerful methods to perform object detection on images and extract each object from the image. The object detection class provides support for RetinaNet, YOLOv3 and TinyYOLOv3, with options to adjust for state of the art performance or real time processing. Click the link below to see the full sample codes, explanations and best practices guide.</h4> <a href="imageai/Detection"> >>> Get Started</a> </td> </tr> </table> <table> <tr> <td><h2> Video Object Detection & Analysis</h2> </td> </tr> <tr> <td><img src="data-images/video_analysis_visualization.jpg"> <h4>ImageAI provides very convenient and powerful methods to perform object detection in videos. The video object detection class provided only supports the current state-of-the-art RetinaNet. Click the link to see the full videos, sample codes, explanations and best practices guide.</h4> <a href="imageai/Detection/VIDEO.md"> >>> Get Started</a> </td> </tr> </table> <table> <tr> <td><h2> Custom Classification model training </h2> </td> </tr> <tr> <td> <img src="data-images/idenprof.jpg"> <h4>ImageAI provides classes and methods for you to train a new model that can be used to perform prediction on your own custom objects. You can train your custom models using MobileNetV2, ResNet50, InceptionV3 and DenseNet in 5 lines of code. Click the link below to see the guide to preparing training images, sample training codes, explanations and best practices.</h4> <a href="imageai/Classification/CUSTOMTRAINING.md"> >>> Get Started</a> </td> </tr> </table> <table> <tr> <td><h2> Custom Model Classification</h2> </td> </tr> <tr> <td><img src="data-images/4.jpg"> <h4>ImageAI provides classes and methods for you to run image prediction your own custom objects using your own model trained with ImageAI Model Training class. You can use your custom models trained with MobileNetV2, ResNet50, InceptionV3 and DenseNet and the JSON file containing the mapping of the custom object names. Click the link below to see the guide to sample training codes, explanations, and best practices guide.</h4> <a href="imageai/Classification/CUSTOMCLASSIFICATION.md"> >>> Get Started</a> </td> </tr> </table> <table> <tr> <td><h2> Custom Detection Model Training </h2> </td> </tr> <tr> <td> <img src="data-images/headsets.jpg"> <h4>ImageAI provides classes and methods for you to train new YOLOv3 or TinyYOLOv3 object detection models on your custom dataset. This means you can train a model to detect literally any object of interest by providing the images, the annotations and training with ImageAI. Click the link below to see the guide to sample training codes, explanations, and best practices guide.</h4> <a href="imageai/Detection/Custom/CUSTOMDETECTIONTRAINING.md"> >>> Get Started</a> </td> </tr> </table> <table> <tr> <td><h2> Custom Object Detection</h2> </td> </tr> <tr> <td><img src="data-images/holo2-detected.jpg"> <h4>ImageAI now