A 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.
An AI Commons project https://commons.specpal.science
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 trained on COCO dataset.
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 2.0.1
What's new:
- Addition of Custom Image Prediction model trainings using SqueezeNet, ResNet50, InceptionV3 and DenseNet121
- Addition of Custom Image Prediction with custom trained models and generated model class json
- Preview Release: Addition of Video Object Detection and Video Custom Objects Detection (object tracking)
- Addition of file, numpy array and stream input types for all image prediction and object detection tasks (file inputs only for video detections)
- Addition of file and numpy array output types for object detection and custom object detection in images
- Introduction of 4 speed modes ('normal', 'fast', 'faster' and 'fastest') for image prediction, allowing prediction time to reduce by 50% at 'fastest' while maintaining prediction accuracy
- Introduction of 5 speed modes ('normal', 'fast', 'faster', 'fastest' and 'flash') for all object detection and video object detection tasks, allowing detection time to reduce by over 80%
with 'flash' detection accuracy balanced with 'minimum_percentage_probability' which is kept at low values - Introduction of rate of frame detections, allowing developers to adjust intervals of detections in videos in favour of real time/close to real time result.
▣ Dependencies
▣ Installation
▣ Image Prediction
▣ Object Detection
▣ Video Object Detection and Tracking
▣ Custom Model Training
▣ Custom Image Prediction
▣ Sample Applications
▣ AI Practice Recommendations
▣ Contact Developers
▣ References
- Python 3.5.1 (and later versions) Download (Support for Python 2.7 coming soon)
- pip3 Install
- Tensorflow 1.4.0 (and later versions) Install or install via pip
pip3 install --upgrade tensorflow- Numpy 1.13.1 (and later versions) Install or install via pip
pip3 install numpy- SciPy 0.19.1 (and later versions) Install or install via pip
pip3 install scipy- OpenCV Install or install via pip
pip3 install opencv-python- Pillow Install or install via pip
pip3 install pillow- Matplotlib Install or install via pip
pip3 install matplotlib- h5py Install or install via pip
pip3 install h5py- Keras 2.x Install or install via pip
pip3 install kerasTo install ImageAI, run the python installation instruction below in the command line:
pip3 install https://github.com/OlafenwaMoses/ImageAI/releases/download/2.0.1/imageai-2.0.1-py3-none-any.whl
or download the Python Wheel
imageai-2.0.1-py3-none-any.whl and run the python installation instruction in the command line
to the path of the file like the one below:
pip3 install C:\User\MyUser\Downloads\imageai-2.0.1-py3-none-any.whl
convertible : 52.459555864334106 sports_car : 37.61284649372101 pickup : 3.1751200556755066 car_wheel : 1.817505806684494 minivan : 1.7487050965428352
>>> Full Details & Documentation
ImageAI provides very convenient and powerful methods to perform object detection on images and extract each object from the image. The 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. You will find below the result of an example object detection using the RetinaNet model, and the 'Full Details & Documentation' link. Click the link to see the full sample codes, explainations, best practices guide and documentation.
Input Image
Output Image
>>> Full Details & Documentation
Video Object Detection
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.
>>> Full Details & Documentation
A sample from the IdenProf Dataset used to train a Model for predicting professionals.
>>> Full Details & Documentation
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
>>> Full Details & Documentation
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
https://www.tensorflow.org/install/install_windows
FOR macOS
https://www.tensorflow.org/install/install_mac
FOR UBUNTU
https://www.tensorflow.org/install/install_linux
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.
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/
Moses Olafenwa
Email: guymodscientist@gmail.com
Website: https://moses.specpal.science
Twitter: @OlafenwaMoses
Medium : @guymodscientist
Facebook : moses.olafenwa
John Olafenwa
Email: johnolafenwa@gmail.com
Website: https://john.specpal.science
Twitter: @johnolafenwa
Medium : @johnolafenwa
Facebook : olafenwajohn
-
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