Displaying 1 to 20 from 22 results

Spots - :bamboo: Spots is a cross-platform view controller framework for building component-based UIs

  •    Swift

Spots is a cross-platform view controller framework for building component-based UIs. The internal architecture is built using generic view models that can be transformed both to and from JSON. So, moving your UI declaration to a backend is as easy as pie. Data source and delegate setup is handled by Spots, so there is no need for you to do that manually. The public API is jam-packed with convenience methods for performing mutation, it is as easy as working with a regular collection type. If you are looking for a way to get started with Spots, we recommend taking a look at our Getting started guide.

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.

YOLO_v3_tutorial_from_scratch - Accompanying code for Paperspace tutorial series "How to Implement YOLO v3 Object Detector from Scratch"

  •    Python

About when is the training code coming? I have my undergraduate thesis this May, and will be busy. So, you might have to wait for a till the second part of May.

Spots - :bamboo: Spots is a cross-platform view controller framework for building component-based UIs

  •    Swift

Spots is a cross-platform view controller framework for building component-based UIs. The internal architecture is built using generic view models that can be transformed both to and from JSON. So, moving your UI declaration to a backend is as easy as pie. Data source and delegate setup is handled by Spots, so there is no need for you to do that manually. The public API is jam-packed with convenience methods for performing mutation, it is as easy as working with a regular collection type. If you are looking for a way to get started with Spots, we recommend taking a look at our Getting started guide.




pytorch-yolo2 - Convert https://pjreddie.com/darknet/yolo/ into pytorch

  •    Python

Convert https://pjreddie.com/darknet/yolo/ into pytorch. This repository is trying to achieve the following goals. We get the results by using Focal Loss to replace CrossEntropyLoss in RegionLosss.

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).

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.


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.

pytorch-caffe-darknet-convert - convert between pytorch, caffe prototxt/weights and darknet cfg/weights

  •    Python

This repository is specially designed for pytorch-yolo2 to convert pytorch trained model to any platform. It can also be used as a common model converter between pytorch, caffe and darknet. MIT License (see LICENSE file).

keras-yolo3 - Training and Detecting Objects with YOLO3

  •    Python

Grab the pretrained weights of yolo3 from https://pjreddie.com/media/files/yolov3.weights. Download the Raccoon dataset from from https://github.com/experiencor/raccoon_dataset.

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

  •    Jupyter

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.

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.

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.

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.

VoTT - Visual Object Tagging Tool: An electron app for building end to end Object Detection Models from Images and Videos

  •    Javascript

This tool provides end to end support for generating datasets and validating object detection models from video and image assets.Run the app by launching the "VOTT" executable which will be located inside the unzipped folder.

tensorflow.js_explained - This is the code for "Tensorflow.js Explained" By Siraj Raval on Youtube

  •    Javascript

Detect objects in images right in your browser using Tensorflow.js! Currently takes ~800ms to analyze each frame on Chrome MBP 13" mid-2014. Supports Tiny YOLO, as of right now, tfjs does not have support to run any full YOLO models (and your user's computers probably can't handle it either).

satellite-image-object-detection - YOLO/YOLOv2 inspired deep network for object detection on satellite images (Tensorflow, Numpy, Pandas)

  •    Python

The dataset is/was available on https://www.datasciencechallenge.org/challenges/1/safe-passage/ . preprocess.py lets you transform the 2000x2000 images into 250x250 images and a CSV file with all the objects annotations. The dataset contains only the position of the center of the objects (no bounding boxes). A bounding box is generated. It's just a square centered on the provided position (x,y). The size of the square varies depending on the type of vehicle. We're using 8 object classes: Motorcycle, Light short rear, Light long rear, Dark short rear, Dark long rear, Red short rear, Red long rear, Light van. Other types of vehicles are ignored.

tfjs-tiny-yolov2 - Tiny YOLO v2 object detection with tensorflow.js.

  •    TypeScript

JavaScript object detection in the browser based on a tensorflow.js implementation of tiny yolov2. The face detection model is one of the models available in face-api.js.

mxnet-yolo - YOLO: You only look once real-time object detector

  •    Python

Still under development. 71 mAP(darknet) and 74mAP(resnet50) on VOC2007 achieved so far. This is a pre-released version.





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