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Darknet
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Darknet

Overview
Synopsis

Darknet is an open source neural network framework written in C and CUDA. It is fast, easy to install, and supports CPU and GPU computation.

Category

Artificial Neural Network Software

Features

YOLO: Real-Time Object Detection
ImageNet Classification
Nightmare
RNNs in Darknet
DarkGo
Tiny Darknet
Train a Classifier on CIFAR-10

License

Proprietary Software

Pricing

Subscription

Free Trial

Available

Users Size

Small (<50 employees), Medium (50 to 1000 employees), Enterprise (>1001 employees)

Website
Company

Darknet

Rating
Our Rating
User Rating
Ease of use
7.6
Features & Functionality
7.6
Advanced Features
7.6
Integration
7.6
Customer Support
7.6
Performance
7.6
Training
Implementation
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Bottom Line

Darknet is an open source neural network framework written in C and CUDA. It is fast, easy to install, and supports CPU and GPU computation.

7.6
Our Rating
User Rating
You have rated this

Darknet is an open source neural network framework written in C and CUDA. It is fast, easy to install, and supports CPU and GPU computation. Users can find the source on GitHub. Darknet is installed with only two optional dependencies: OpenCV if users want a wider variety of supported image types or CUDA if they want GPU computation. Neither is compulsory but users can start by just installing the base system which has only been tested on Linux and Mac computers. The framework features You Only Look Once (YOLO), a state-of-the-art, real-time object detection system. On a Titan X it processes images at 40-90 FPS and has a mAP on VOC 2007 of 78.6% and a mAP of 44.0% on COCO test-dev. Users can use Darknet to classify images for the 1000-class ImageNet challenge. Darknet displays information as it loads the config file and weights then it classifies the image and prints the top-10 classes for the image. Moreover, the framework can be used to run neural networks backward in a feature appropriately named Nightmare. Recurrent neural networks are powerful models for representing data that changes over time and Darknet can handle them without making use of CUDA or OpenCV. The framework also allows its users to venture into game-playing neural networks. It features a neural network that predicts the most likely next moves in a game of Go. Users can play along with professional games and see what moves are likely to happen next, make it play itself, or try to play against it.

Darknet

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Ease of use
Features & Functionality
Advanced Features
Integration
Customer Support
Performance
Training
Implementation
Renew & Recommend

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