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Description

Darknet is a neural network framework that is open-source, developed using C and CUDA. Known for its speed and simplicity in installation, it accommodates both CPU and GPU processing. The source code is available on GitHub, where you can also explore its capabilities further. The installation process is straightforward, requiring only two optional dependencies: OpenCV for enhanced image format support and CUDA for GPU acceleration. While Darknet performs efficiently on CPUs, it boasts a performance increase of approximately 500 times when running on a GPU! To leverage this speed, you'll need an Nvidia GPU alongside the CUDA installation. By default, Darknet utilizes stb_image.h for loading images, but for those seeking compatibility with more obscure formats like CMYK jpegs, OpenCV can be employed. Additionally, OpenCV provides the functionality to visualize images and detections in real-time without needing to save them. Darknet supports the classification of images using well-known models such as ResNet and ResNeXt, and it has become quite popular for employing recurrent neural networks in applications related to time-series data and natural language processing. Whether you're a seasoned developer or a newcomer, Darknet offers an accessible way to implement advanced neural network solutions.

Description

OpenFaceTracker is a facial recognition application designed to recognize one or more faces in images or videos by using a database for identification. To run OpenFaceTracker, your system must have OpenCV 3.2 and QT4 installed; you can either compile the libraries manually by following build_oft or install OpenCV and QT through your preferred package manager. You have the option to compile OpenFaceTracker either as a library or as a standalone executable. Once compiled, you can open the resulting file to utilize the detection and recognition features, display help and exit options, list all available cameras, test the XML database, read the configuration settings, and verify environmental parameters. OpenFaceTrackerLib is built on OpenCV 3.2, which has brought numerous new algorithms and enhancements compared to version 2.4, with several modules being restructured and rewritten. While most algorithms from version 2.4 remain available, the interfaces may vary, necessitating users to familiarize themselves with the changes. Ultimately, OpenFaceTracker offers a versatile solution for facial recognition tasks across various platforms.

API Access

Has API No 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

No details available.

Integrations

No details available.

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based No 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac Yes 
Linux Yes 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac No 
Linux Yes 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Darknet

Website

pjreddie.com/darknet/

Vendor Details

Company Name

OpenFaceTracker

Founded

2017

Website

www.openfacetracker.net

Product Features

Product Features

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