albumentations - fast image augmentation library and easy to use wrapper around other libraries

•    Jupyter

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You can use this Google Colaboratory notebook to adjust image augmentation parameters and see the resulting images.

image-augmentation machine-learning augmentation deep-learning detection fast-augmentations segmentation image-segmentation image-processing image-classification object-detection

frustum-pointnets - Frustum PointNets for 3D Object Detection from RGB-D Data

•    Python

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Created by Charles R. Qi, Wei Liu, Chenxia Wu, Hao Su and Leonidas J. Guibas from Stanford University and Nuro Inc. This repository is code release for our CVPR 2018 paper (arXiv report here). In this work, we study 3D object detection from RGB-D data. We propose a novel detection pipeline that combines both mature 2D object detectors and the state-of-the-art 3D deep learning techniques. In our pipeline, we firstly build object proposals with a 2D detector running on RGB images, where each 2D bounding box defines a 3D frustum region. Then based on 3D point clouds in those frustum regions, we achieve 3D instance segmentation and amodal 3D bounding box estimation, using PointNet/PointNet++ networks (see references at bottom).

object-detection 3d point-cloud robotics deep-learning

Augmentor - Image augmentation library in Python for machine learning.

•    Python

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Augmentor is an image augmentation library in Python for machine learning. It aims to be a standalone library that is platform and framework independent, which is more convenient, allows for finer grained control over augmentation, and implements the most real-world relevant augmentation techniques. It employs a stochastic approach using building blocks that allow for operations to be pieced together in a pipeline. Augmentor is written in Python. A Julia version of the package is also being developed as a sister project and is available here.

augmentation machine-learning deep-learning neural-networks

keras-multiprocess-image-data-generator - Accelerating Deep Learning with Multiprocess Image Augmentation in Keras

•    Jupyter

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Accelerating Deep Learning with Multiprocess Image Augmentation in Keras

deep-learning keras tensorflow multiprocessing

OpenLabeling - Open Source labeling tool to generate the training data in the format YOLO requires.

•    Python

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Bounding box labeler tool to generate the training data in the format YOLO v2 requires. The idea is to use OpenCV so that later it uses SIFT and Tracking algorithms to make labeling easier.

darknet yolo gui training-yolo opencv labeling-tool bounding-boxes object-detection

raster-vision - deep learning for aerial/satellite imagery

•    Python

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Note: this project is under development and may be difficult to use at the moment. The overall goal of Raster Vision is to make it easy to train and run deep learning models over aerial and satellite imagery. At the moment, it includes functionality for making training data, training models, making predictions, and evaluating models for the task of object detection implemented via the Tensorflow Object Detection API. It also supports running experimental workflows using AWS Batch. The library is designed to be easy to extend to new data sources, machine learning tasks, and machine learning implementation.

deep-learning tensorflow computer-vision remote-sensing geospatial object-detection

keras-preprocessing - Utilities for working with image data, text data, and sequence data.

•    Python

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Keras Preprocessing is the data preprocessing and data augmentation module of the Keras deep learning library. It provides utilities for working with image data, text data, and sequence data. Keras Preprocessing is compatible with Python 2.7-3.6 and is distributed under the MIT license.

detection-2016-nipsws - Hierarchical Object Detection with Deep Reinforcement Learning

•    Python

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We present a method for performing hierarchical object detection in images guided by a deep reinforcement learning agent. The key idea is to focus on those parts of the image that contain richer information and zoom on them. We train an intelligent agent that, given an image window, is capable of deciding where to focus the attention among five different predefined region candidates (smaller windows). This procedure is iterated providing a hierarchical image analysis. We compare two different candidate proposal strategies to guide the object search: with and without overlap. Moreover, our work compares two different strategies to extract features from a convolutional neural network for each region proposal: a first one that computes new feature maps for each region proposal, and a second one that computes the feature maps for the whole image to later generate crops for each region proposal.

deep-reinforcement-learning deep-learning deep-neural-networks

Amazon-Forest-Computer-Vision - Amazon Forest Computer Vision: Satellite Image tagging code using PyTorch / Keras with lots of PyTorch tricks

•    Jupyter

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Amazon Forest Computer Vision: Satellite Image tagging code using PyTorch / Keras with lots of PyTorch tricks

pytorch data-augmentation kaggle-competition kaggle deep-learning computer-vision keras neural-networks neural-network-example transfer-learning

Detectron - FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet

•    Python

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Detectron is Facebook AI Research's software system that implements state-of-the-art object detection algorithms, including Mask R-CNN. It is written in Python and powered by the Caffe2 deep learning framework. At FAIR, Detectron has enabled numerous research projects, including: Feature Pyramid Networks for Object Detection, Mask R-CNN, Detecting and Recognizing Human-Object Interactions, Focal Loss for Dense Object Detection, Non-local Neural Networks, Learning to Segment Every Thing, and Data Distillation: Towards Omni-Supervised Learning.

Repo-2017 - Python codes in Machine Learning, NLP, Deep Learning and Reinforcement Learning with Keras and Theano

•    Python

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Welcome to my GitHub repo. I am a Data Scientist and I code in R, Python and Wolfram Mathematica. Here you will find some Machine Learning, Deep Learning, Natural Language Processing and Artificial Intelligence models I developed.

anomaly-detection deep-learning autoencoder keras keras-models denoising-autoencoders generative-adversarial-network glove keras-layer word2vec nlp natural-language-processing sentiment-analysis opencv segnet resnet-50 variational-autoencoder t-sne svm-classifier latent-dirichlet-allocation

sketch-code - Keras model to generate HTML code from hand-drawn website mockups

•    Python

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SketchCode is a deep learning model that takes hand-drawn web mockups and converts them into working HTML code. It uses an image captioning architecture to generate its HTML markup from hand-drawn website wireframes. This project builds on the synthetically generated dataset and model architecture from pix2code by Tony Beltramelli and the Design Mockups project from Emil Wallner.

keras tensorflow image-processing deep-learning augmentation

sod - An Embedded Computer Vision & Machine Learning Library (CPU Optimized & IoT Capable)

•    C

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SOD is an embedded, modern cross-platform computer vision and machine learning software library that expose a set of APIs for deep-learning, advanced media analysis & processing including real-time, multi-class object detection and model training on embedded systems with limited computational resource and IoT devices. SOD was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in open source as well commercial products.

computer-vision library deep-learning image-processing object-detection cpu real-time convolutional-neural-networks recurrent-neural-networks face-detection facial-landmarks machine-learning-algorithms image-recognition image-analysis vision-framework embedded detection iot-device iot

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

•    Python

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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. 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. 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.

artificial-intelligence machine-learning prediction image-prediction python3 offline-capable imageai artificial-neural-networks algorithm image-recognition object-detection squeezenet densenet video inceptionv3 detection gpu ai-practice-recommendations

tensorflow-image-detection - A generic image detection program that uses Google's Machine Learning library, Tensorflow and a pre-trained Deep Learning Convolutional Neural Network model called Inception

•    Python

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A generic image detection program that uses Google's Machine Learning library, Tensorflow and a pre-trained Deep Learning Convolutional Neural Network model called Inception. This model has been pre-trained for the ImageNet Large Visual Recognition Challenge using the data from 2012, and it can differentiate between 1,000 different classes, like Dalmatian, dishwasher etc. The program applies Transfer Learning to this existing model and re-trains it to classify a new set of images.

image-detection machine-learning deep-learning deep-neural-networks convolutional-neural-networks tensorflow

T-CNN - ImageNet 2015 Object Detection from Video (VID)

•    Python

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The TCNN framework is a deep learning framework for object detection in videos. This framework was orginally designed for the ImageNet VID chellenge in ILSVRC2015. If you are using the T-CNN code in you project, please cite the following works.

computer-vision deep-learning imagenet-vid video detection object-detection

tensorlayer - Deep Learning and Reinforcement Learning Library for Developers and Scientists

•    Python

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TensorLayer is a novel TensorFlow-based deep learning and reinforcement learning library designed for researchers and engineers. It provides a large collection of customizable neural layers / functions that are key to build real-world AI applications. TensorLayer is awarded the 2017 Best Open Source Software by the ACM Multimedia Society. Simplicity : TensorLayer lifts the low-level dataflow interface of TensorFlow to high-level layers / models. It is very easy to learn through the rich example codes contributed by a wide community.

tensorlayer deep-learning tensorflow machine-learning data-science neural-network reinforcement-learning artificial-intelligence gan a3c tensorflow-tutorials dqn object-detection chatbot tensorflow-tutorial imagenet google

android-yolo - Real-time object detection on Android using the YOLO network with TensorFlow

•    C++

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android-yolo is the first implementation of YOLO for TensorFlow on an Android device. It is compatible with Android Studio and usable out of the box. It can detect the 20 classes of objects in the Pascal VOC dataset: aeroplane, bicycle, bird, boat, bottle, bus, car, cat, chair, cow, dining table, dog, horse, motorbike, person, potted plant, sheep, sofa, train and tv/monitor. The network only outputs one predicted bounding box at a time for now. The code can and will be extended in the future to output several predictions. To use this demo first clone the repository. Download the TensorFlow YOLO model and put it in android-yolo/app/src/main/assets. Then open the project on Android Studio. Once the project is open you can run the project on your Android device using the Run 'app' command and selecting your device.

android-device yolo tensorflow android-studio tensorflow-yolo detection demo apk android object-detection pascal-voc prediction

YOLO-CoreML-MPSNNGraph - Tiny YOLO for iOS implemented using CoreML but also using the new MPS graph API

•    Swift

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This is the source code for my blog post YOLO: Core ML versus MPSNNGraph. YOLO is an object detection network. It can detect multiple objects in an image and puts bounding boxes around these objects. Read my other blog post about YOLO to learn more about how it works.

core-ml mps metal machine-learning deep-learning yolo ios

MMLSpark - Microsoft Machine Learning for Apache Spark

•    Scala

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MMLSpark provides a number of deep learning and data science tools for Apache Spark, including seamless integration of Spark Machine Learning pipelines with Microsoft Cognitive Toolkit (CNTK) and OpenCV, enabling you to quickly create powerful, highly-scalable predictive and analytical models for large image and text datasets.MMLSpark requires Scala 2.11, Spark 2.1+, and either Python 2.7 or Python 3.5+. See the API documentation for Scala and for PySpark.

machine-learning spark cntk pyspark azure microsoft-machine-learning microsoft ml