ImageAI Save

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

Project README

ImageAI (v3.0.3)

Build Status License: MIT PyPI version Downloads Downloads

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 Github sponsor page.

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ImageAI now uses PyTorch backend.

As from ImageAI 3.0.2, the library now uses PyTorch has the backend. For full details on this and if you plan on using existing Tensorflow pretrained models, custom models and Pascal VOC dataset, visit the BACKEND_MIGRATION.md documentation.

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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:

  • PyTorch backend
  • TinyYOLOv3 model training

TABLE OF CONTENTS

Installation

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 [email protected]+https://github.com/gautamchitnis/[email protected]#subdirectory=PythonAPI
    
  • Then run the command below to install ImageAI

    pip install imageai --upgrade
    

Features

Image Classification

>>> Get Started

Object Detection

>>> Get Started

Video Object Detection & Analysis

>>> Get Started

Custom Classification model training

>>> Get Started

Custom Model Classification

>>> Get Started

Custom Detection Model Training

>>> Get Started

Custom Object Detection

>>> Get Started

Custom Video Object Detection & Analysis

>>> Get Started

Documentation

We have provided full documentation for all ImageAI classes and functions. Visit the link below:

Sponsors

Real-Time and High Performance Implementation

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.

Projects Built on ImageAI

AI Practice Recommendations

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

Contact Developer

Citation

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--"
}

References

  1. Somshubra Majumdar, DenseNet Implementation of the paper, Densely Connected Convolutional Networks in Keras https://github.com/titu1994/DenseNet
  2. Broad Institute of MIT and Harvard, Keras package for deep residual networks https://github.com/broadinstitute/keras-resnet
  3. Fizyr, Keras implementation of RetinaNet object detection https://github.com/fizyr/keras-retinanet
  4. Francois Chollet, Keras code and weights files for popular deeplearning models https://github.com/fchollet/deep-learning-models
  5. Forrest N. et al, SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size https://arxiv.org/abs/1602.07360
  6. Kaiming H. et al, Deep Residual Learning for Image Recognition https://arxiv.org/abs/1512.03385
  7. Szegedy. et al, Rethinking the Inception Architecture for Computer Vision https://arxiv.org/abs/1512.00567
  8. Gao. et al, Densely Connected Convolutional Networks https://arxiv.org/abs/1608.06993
  9. Tsung-Yi. et al, Focal Loss for Dense Object Detection https://arxiv.org/abs/1708.02002
  10. O Russakovsky et al, ImageNet Large Scale Visual Recognition Challenge https://arxiv.org/abs/1409.0575
  11. TY Lin et al, Microsoft COCO: Common Objects in Context https://arxiv.org/abs/1405.0312
  12. Moses & John Olafenwa, A collection of images of identifiable professionals. https://github.com/OlafenwaMoses/IdenProf
  13. Joseph Redmon and Ali Farhadi, YOLOv3: An Incremental Improvement. https://arxiv.org/abs/1804.02767
  14. Experiencor, Training and Detecting Objects with YOLO3 https://github.com/experiencor/keras-yolo3
  15. MobileNetV2: Inverted Residuals and Linear Bottlenecks https://arxiv.org/abs/1801.04381
  16. YOLOv3 in PyTorch > ONNX > CoreML > TFLite https://github.com/ultralytics/yolov3
Open Source Agenda is not affiliated with "ImageAI" Project. README Source: OlafenwaMoses/ImageAI