darkflow - Translate darknet to tensorflow

•    Python

---

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

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

•    C++

---

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

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

•    C

---

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

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

•    Python

---

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

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

•    Python

---

[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

raster-vision - deep learning for aerial/satellite imagery

•    Python

---

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

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

•    Python

---

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

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

•    Swift

---

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

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

•    C

---

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

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

---

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

Yolo_mark - GUI for marking bounded boxes of objects in images for training neural network Yolo v3 and v2

•    C++

---

GUI for marking bounded boxes of objects in images for training neural network Yolo v3 and v2

labeling object-detection dnn training-yolo marking-bounded-boxes yolo darknet

hed - code for Holistically-Nested Edge Detection

•    C++

---

We develop a new edge detection algorithm, holistically-nested edge detection (HED), which performs image-to-image prediction by means of a deep learning model that leverages fully convolutional neural networks and deeply-supervised nets. HED automatically learns rich hierarchical representations (guided by deep supervision on side responses) that are important in order to resolve the challenging ambiguity in edge and object boundary detection. We significantly advance the state-of-the-art on the BSD500 dataset (ODS F-score of .790) and the NYU Depth dataset (ODS F-score of .746), and do so with an improved speed (0.4s per image). Detailed description of the system can be found in our paper. If you have downloaded the previous version (testing code) of HED, please note that we updated the code base to the new version of Caffe. We uploaded a new pretrained model with better performance. We adopted the python interface written for the FCN paper instead of our own implementation for training and testing. The evaluation protocol doesn't change.

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

•    Python

---

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.

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

•    Python

---

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

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

•    Python

---

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

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

•    Python

---

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

cvat - Computer Vision Annotation Tool (CVAT) is a web-based tool which helps to annotate video and images for Computer Vision algorithms

•    Javascript

---

CVAT is completely re-designed and re-implemented version of Video Annotation Tool from Irvine, California tool. It is free, online, interactive video and image annotation tool for computer vision. It is being used by our team to annotate million of objects with different properties. Many UI and UX decisions are based on feedbacks from professional data annotation team. Code released under the MIT License.

video-annotation computer-vision computer-vision-annotation deep-learning image-annotation annotation-tool annotation labeling labeling-tool image-labeling image-labelling-tool bounding-boxes boundingbox image-classification annotations imagenet detection recognition tensorflow

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

•    Python

---

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

luminoth - Deep Learning toolkit for Computer Vision

•    Python

---

Luminoth is an open source toolkit for computer vision. Currently, we support object detection, but we are aiming for much more. It is built in Python, using TensorFlow and Sonnet. Read the full documentation here.

tensorflow sonnet deep-learning computer-vision object-detection machine-learning toolkit faster-rcnn

mAP - mean Average Precision - This code evaluates the performance of your neural net for object recognition

•    Python

---

This code will evaluate the performance of your neural net for object recognition. In practice, a higher mAP value indicates a better performance of your neural net, given your ground-truth and set of classes.

machine-learning pascal-voc average-precision ground-truth object-detection computer-vision metrics detection neural-network darkflow yolo darknet