ImageAI (v2.1.6)
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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ImageAI will switch to PyTorch backend starting from June, 2021.
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An DeepQuest AI (A Brand of AI Commons Global Limited) project deepquestai.com.
Developed and Maintained by Moses Olafenwa and John Olafenwa, brothers, creators of TorchFusion, Authors of Introduction to Deep Computer Vision and creators of DeepStack AI Server.
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 including and not limited to image recognition in special environments and special fields.
New Release : ImageAI 2.1.6
What's new:
- Support Tensorflow 2.4.0
- SqueezeNet replaced with MobileNetV2
- Added TF 2.x compatible pre-trained models for ResNet recognition and RetinaNet detection
- Deprecates '.predictImage()' function for '.classifyImage()'
- Renames Model types as below:
- ResNet >> ResNet50
- DenseNet >> DenseNet121
TABLE OF CONTENTS
-
π³ Dependencies -
π³ Installation -
π³ Image Prediction -
π³ Object Detection -
π³ Video Object Detection, Tracking & Analysis -
π³ Custom Model Training -
π³ Custom Image Prediction -
π³ Custom Detection Model Training -
π³ Custom Object Detection -
π³ Custom Video Object Detection & Analysis -
π³ Documentation -
π³ Projects Built on ImageAI -
π³ High Performance Implementation -
π³ AI Practice Recommendations -
π³ Contact Developers -
π³ Citation -
π³ Sponsors -
π³ Contributors -
π³ References
Dependencies
To use ImageAI in your application developments, you must have installed the following dependencies before you install ImageAI :
- Python 3.7.6
- Tensorflow 2.4.0
- OpenCV
- Keras 2.4.3
You can install all the dependencies by running the commands below
Tensorflow
pip install tensorflow==2.4.0or Tensorflow GPU if you have NVIDIA GPU with CUDA and cuDNN installed.
pip install tensorflow-gpu==2.4.0Other Dependencies
pip install keras==2.4.3 numpy==1.19.3 pillow==7.0.0 scipy==1.4.1 h5py==2.10.0 matplotlib==3.3.2 opencv-python keras-resnet==0.2.0Installation
To install ImageAI, run the python installation instruction below in the command line:
pip install imageai --upgradeImage Prediction
convertible : 52.459555864334106
sports_car : 37.61284649372101
pickup : 3.1751200556755066
car_wheel : 1.817505806684494
minivan : 1.7487050965428352
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.
Object Detection
person : 91.946941614151
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person : 73.61021637916565
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laptop : 90.24320840835571
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laptop : 73.6881673336029
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laptop : 95.16398310661316
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person : 87.10319399833679
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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.
Video Object Detection and Tracking
Video Object Detection & Analysis
Below is a snapshot of a video with objects detected.
Video Custom Object Detection (Object Tracking)
Below is a snapshot of a video with only person, bicycle and motorcyle detected.
Video Analysis Visualization
Below is a visualization of video analysis returned by ImageAI into a 'per_second' function.
ImageAI provides very convenient and powerful methods to perform object detection in videos and track specific object(s). The video object detection class provided only supports the current state-of-the-art RetinaNet, but with options to adjust for state of the art performance or real time processing. Click the link to see the full videos, sample codes, explanations and best practices guide.
Custom Model Training
A sample from the IdenProf Dataset used to train a Model for predicting professionals.
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 SqueezeNet, 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.
Custom Image Prediction
Prediction from a sample model trained on IdenProf, for predicting professionals
mechanic : 76.82620286941528
chef : 10.106072574853897
waiter : 4.036874696612358
police : 2.6663416996598244
pilot : 2.239348366856575
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 SqueezeNet, 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.
Custom Detection Model Training
Training detection models to detect and recognize new objects.
ImageAI provides classes and methods for you to train new YOLOv3 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.
Custom Object Detection
Detection result from a custom YOLOv3 model trained to detect the Hololens headset.
hololens : 39.69653248786926 : [611, 74, 751, 154]
hololens : 87.6643180847168 : [23, 46, 90, 79]
hololens : 89.25175070762634 : [191, 66, 243, 95]
hololens : 64.49641585350037 : [437, 81, 514, 133]
hololens : 91.78624749183655 : [380, 113, 423, 138]
ImageAI now provides classes and methods for you detect and recognize your own custom objects in images using your own model trained with the DetectionModelTraining class. You can use your custom trained YOLOv3 mode and the detection_config.json file generated during the training. Click the link below to see the guide to sample training codes, explanations, and best practices guide.
Custom Video Object Detection & Analysis
Video Detection result from a custom YOLOv3 model trained to detect the Hololens headset in a video.
Now you can use your custom trained YOLOv3 model to detect, recognize and analyze objects in videos. Click the link below to see the guide to sample training codes, explanations, and best practices guide.
Documentation
We have provided full documentation for all ImageAI classes and functions in 2 major languages. Find links below:
- Documentation - English Version https://imageai.readthedocs.io
- Documentation - Chinese Version https://imageai-cn.readthedocs.io
- Documentation - French Version https://imageai-fr.readthedocs.io
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 Tensorflow backbone for it's Computer Vision operations. Tensorflow 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. To use Tensorflow that supports the use of GPUs, follow the link below :
- FOR WINDOWS - install_windows
- FOR macOS - install_mac
- FOR UBUNTU - install_linux
Projects Built on ImageAI
BatBot : BatBot is an open source Intelligent Research Robot with image and speech recognition. It comes with an Android App which allows you to speak voice commands and instruct the robot to find objects using its camera and an AI engine powered by ImageAI. It also allows you to re-train and improve the AI capabilities from new images captured by the robot. Learn more about the incredible capabilities and components of BatBot via the GitHub repository . It is developed and maintained by Lee Hounshell
We also welcome submissions of applications and systems built by you and powered by ImageAI for listings here. Should you want your ImageAI powered developments listed here, you can reach to us via our Contacts below.
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
- Moses Olafenwa
- Email: [email protected]
- Website: http://olafenwamoses.me
- Twitter: @OlafenwaMoses
- Medium: @guymodscientist
- Facebook: moses.olafenwa
- John Olafenwa
- Email: [email protected]
- Website: https://john.aicommons.science
- Twitter: @johnolafenwa
- Medium: @johnolafenwa
- Facebook: olafenwajohn
Contributors
We are inviting anyone who wishes to contribute to the ImageAI project to reach to us. We primarily need contributions in translating the documentation of the project's code to major languages that includes but not limited to French, Spanish, Portuguese, Arabian and more. We want every developer and researcher around the world to benefit from this project irrespective of their native languages.
We give special thanks to Kang vcar for his incredible and excellent work in translating ImageAI's documentation to the Chinese language. Find below the contact details of those who have contributed immensely to the ImageAI project.
- Kang vcar
- Email: [email protected]
- Website: http://www.kangvcar.com
- Twitter: @kangvcar
Citation
You can cite ImageAI in your projects and research papers via the BibTeX entry below.
@misc {ImageAI,
author = "Moses and John Olafenwa",
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
- Somshubra Majumdar, DenseNet Implementation of the paper, Densely Connected Convolutional Networks in Keras https://github.com/titu1994/DenseNet
- Broad Institute of MIT and Harvard, Keras package for deep residual networks https://github.com/broadinstitute/keras-resnet
- Fizyr, Keras implementation of RetinaNet object detection https://github.com/fizyr/keras-retinanet
- Francois Chollet, Keras code and weights files for popular deeplearning models https://github.com/fchollet/deep-learning-models
- Forrest N. et al, SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size https://arxiv.org/abs/1602.07360
- Kaiming H. et al, Deep Residual Learning for Image Recognition https://arxiv.org/abs/1512.03385
- Szegedy. et al, Rethinking the Inception Architecture for Computer Vision https://arxiv.org/abs/1512.00567
- Gao. et al, Densely Connected Convolutional Networks https://arxiv.org/abs/1608.06993
- Tsung-Yi. et al, Focal Loss for Dense Object Detection https://arxiv.org/abs/1708.02002
- O Russakovsky et al, ImageNet Large Scale Visual Recognition Challenge https://arxiv.org/abs/1409.0575
- TY Lin et al, Microsoft COCO: Common Objects in Context https://arxiv.org/abs/1405.0312
- Moses & John Olafenwa, A collection of images of identifiable professionals. https://github.com/OlafenwaMoses/IdenProf
- Joseph Redmon and Ali Farhadi, YOLOv3: An Incremental Improvement. https://arxiv.org/abs/1804.02767
- Experiencor, Training and Detecting Objects with YOLO3 https://github.com/experiencor/keras-yolo3
- MobileNetV2: Inverted Residuals and Linear Bottlenecks https://arxiv.org/abs/1801.04381