darkflow - Translate darknet to tensorflow

•    Python

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Real-time object detection and classification. Paper: version 1, version 2. Read more about YOLO (in darknet) and download weight files here. In case the weight file cannot be found, I uploaded some of mine here, which include yolo-full and yolo-tiny of v1.0, tiny-yolo-v1.1 of v1.1 and yolo, tiny-yolo-voc of v2.

tensorflow graph darknet deep-learning deep-neural-networks convolutional-neural-networks convolutional-networks image-processing object-detection machine-learning real-time mobile-development

pytorch-yolo-v3 - A PyTorch implementation of the YOLO v3 object detection algorithm

•    Python

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[UPDATE] : This repo serves as a driver code for my research. I just graduated college, and am very busy looking for research internship / fellowship roles before eventually applying for a masters. I won't have the time to look into issues for the time being. Thank you. This repository contains code for a object detector based on YOLOv3: An Incremental Improvement, implementedin PyTorch. The code is based on the official code of YOLO v3, as well as a PyTorch port of the original code, by marvis. One of the goals of this code is to improve upon the original port by removing redundant parts of the code (The official code is basically a fully blown deep learning library, and includes stuff like sequence models, which are not used in YOLO). I've also tried to keep the code minimal, and document it as well as I can.

yolov3 yolo object-detection pytorch

Deep-Learning-with-Keras - Code repository for Deep Learning with Keras published by Packt

•    Jupyter

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This is the code repository for Deep Learning with Keras, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish. This book starts by introducing you to supervised learning algorithms such as simple linear regression, classical multilayer perceptron, and more sophisticated Deep Convolutional Networks. In addition, you will also understand unsupervised learning algorithms such as Autoencoders, Restricted Boltzmann Machines, and Deep Belief Networks. Recurrent Networks and Long Short Term Memory (LSTM) networks are also explained in detail. You will also explore image processing involving the recognition of handwritten digital images, the classification of images into different categories, and advanced object recognition with related image annotations. An example of the identification of salient points for face detection is also provided.

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

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

Pyod - A Python Toolkit for Scalable Outlier Detection (Anomaly Detection)

•    Python

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Important Notes: PyOD contains some neural network based models, e.g., AutoEncoders, which are implemented in keras. However, PyOD would NOT install keras and tensorflow automatically. This would reduce the risk of damaging your local installations. You are responsible for installing keras and tensorflow if you want to use neural net based models. An instruction is provided here. Anomaly detection resources, e.g., courses, books, papers and videos.

outlier-detection anomaly-detection outlier-ensembles outliers anomaly machine-learning data-mining unsupervised-learning python2 python3 fraud-detection autoencoder neural-networks deep-learning

lightnet - 🌓 Bringing pjreddie's DarkNet out of the shadows #yolo

•    C

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LightNet provides a simple and efficient Python interface to DarkNet, a neural network library written by Joseph Redmon that's well known for its state-of-the-art object detection models, YOLO and YOLOv2. LightNet's main purpose for now is to power Prodigy's upcoming object detection and image segmentation features. However, it may be useful to anyone interested in the DarkNet library. Once you've downloaded LightNet, you can install a model using the lightnet download command. This will save the models in the lightnet/data directory. If you've installed LightNet system-wide, make sure to run the command as administrator.

machine-learning computer-vision neural-network neural-networks object-detection darknet-image-classification image-classification ai artificial-intelligence cython cython-wrapper yolo

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

keras-rcnn - Keras package for region-based convolutional neural networks (RCNNs)

•    Python

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keras-rcnn is the Keras package for region-based convolutional neural networks. The data is made up of a list of dictionaries corresponding to images.

deep-learning theano tensorflow cntk object-detection image-segmentation

keras-rl - Deep Reinforcement Learning for Keras.

•    Python

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keras-rl implements some state-of-the art deep reinforcement learning algorithms in Python and seamlessly integrates with the deep learning library Keras. Just like Keras, it works with either Theano or TensorFlow, which means that you can train your algorithm efficiently either on CPU or GPU. Furthermore, keras-rl works with OpenAI Gym out of the box. This means that evaluating and playing around with different algorithms is easy. Of course you can extend keras-rl according to your own needs. You can use built-in Keras callbacks and metrics or define your own. Even more so, it is easy to implement your own environments and even algorithms by simply extending some simple abstract classes. In a nutshell: keras-rl makes it really easy to run state-of-the-art deep reinforcement learning algorithms, uses Keras and thus Theano or TensorFlow and was built with OpenAI Gym in mind.

keras tensorflow theano reinforcement-learning neural-networks machine-learning

raster-vision - deep learning for aerial/satellite imagery

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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

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

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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

CarND-Vehicle-Detection - Vehicle detection using YOLO in Keras runs at 21FPS

•    Jupyter

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This is a project for Udacity self-driving car Nanodegree program. The aim of this project is to detect the vehicles in a dash camera video. The implementation of the project is in the file vehicle_detection.ipynb. This implementation is able to achieve 21FPS without batching processing. The final video output is here. In this README, each step in the pipeline will be explained in details.

yolo vehicle-detection bounding-boxes convolutional-neural-networks keras

dist-keras - Distributed Deep Learning, with a focus on distributed training, using Keras and Apache Spark

•    Python

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Distributed Deep Learning with Apache Spark and Keras. Distributed Keras is a distributed deep learning framework built op top of Apache Spark and Keras, with a focus on "state-of-the-art" distributed optimization algorithms. We designed the framework in such a way that a new distributed optimizer could be implemented with ease, thus enabling a person to focus on research. Several distributed methods are supported, such as, but not restricted to, the training of ensembles and models using data parallel methods.

machine-learning deep-learning apache-spark data-parallelism distributed-optimizers keras optimization-algorithms tensorflow data-science hadoop

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

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

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

tensornets - High level network definitions with pre-trained weights in TensorFlow

•    Python

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High level network definitions with pre-trained weights in TensorFlow (tested with >= 1.1.0). You can install TensorNets from PyPI (pip install tensornets) or directly from GitHub (pip install git+https://github.com/taehoonlee/tensornets.git).

tensorflow zoo pretrained-models machine-learning deep-learning image-classification object-detection yolo yolov2 yolov3 faster-rcnn resnet inception nasnet pnasnet vgg densenet mobilenet mobilenetv2 squeezenet

YOLO_Object_Detection - This is the code for "YOLO Object Detection" by Siraj Raval on Youtube

•    Python

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Real-time object detection and classification. Paper: version 1, version 2. Read more about YOLO (in darknet) and download weight files here. In case the weight file cannot be found, I uploaded some of mine here, which include yolo-full and yolo-tiny of v1.0, tiny-yolo-v1.1 of v1.1 and yolo, tiny-yolo-voc of v2.

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