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  1. Stackups
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  4. Search Tools
  5. OpenFace vs Solr

OpenFace vs Solr

OverviewComparisonAlternatives

Overview

Solr
Solr
Stacks805
Followers644
Votes126
OpenFace
OpenFace
Stacks31
Followers104
Votes3
GitHub Stars15.4K
Forks3.6K

OpenFace vs Solr: What are the differences?

Developers describe OpenFace as "Free and open source face recognition with deep neural networks". OpenFace is a Python and Torch implementation of face recognition with deep neural networks and is based on the CVPR 2015 paper FaceNet: A Unified Embedding for Face Recognition and Clustering by Florian Schroff, Dmitry Kalenichenko, and James Philbin at Google. On the other hand, Solr is detailed as "An open source enterprise search server based on Lucene search library, with XML/HTTP and JSON APIs, hit highlighting, faceted search, caching, replication etc". Solr is the popular, blazing fast open source enterprise search platform from the Apache Lucene project. Its major features include powerful full-text search, hit highlighting, faceted search, near real-time indexing, dynamic clustering, database integration, rich document (e.g., Word, PDF) handling, and geospatial search. Solr is highly reliable, scalable and fault tolerant, providing distributed indexing, replication and load-balanced querying, automated failover and recovery, centralized configuration and more. Solr powers the search and navigation features of many of the world's largest internet sites.

OpenFace belongs to "Facial Recognition" category of the tech stack, while Solr can be primarily classified under "Search Engines".

Some of the features offered by OpenFace are:

  • Detect faces with pre-trained models
  • Transform faces for the neural network
  • Use deep neural networks to reprsent or embed the face on a hypersphere

On the other hand, Solr provides the following key features:

  • Advanced Full-Text Search Capabilities
  • Optimized for High Volume Web Traffic
  • Standards Based Open Interfaces - XML, JSON and HTTP

OpenFace is an open source tool with 12.3K GitHub stars and 3.02K GitHub forks. Here's a link to OpenFace's open source repository on GitHub.

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Detailed Comparison

Solr
Solr
OpenFace
OpenFace

Solr is the popular, blazing fast open source enterprise search platform from the Apache Lucene project. Its major features include powerful full-text search, hit highlighting, faceted search, near real-time indexing, dynamic clustering, database integration, rich document (e.g., Word, PDF) handling, and geospatial search. Solr is highly reliable, scalable and fault tolerant, providing distributed indexing, replication and load-balanced querying, automated failover and recovery, centralized configuration and more. Solr powers the search and navigation features of many of the world's largest internet sites.

OpenFace is a Python and Torch implementation of face recognition with deep neural networks and is based on the CVPR 2015 paper FaceNet: A Unified Embedding for Face Recognition and Clustering by Florian Schroff, Dmitry Kalenichenko, and James Philbin at Google.

Advanced full-text search capabilities; Optimized for high volume web traffic; Standards-based open interfaces - XML, JSON and HTTP; Comprehensive HTML administration interfaces; Server statistics exposed over JMX for monitoring; Linearly scalable, auto index replication, auto-failover and recovery; Near real-time indexing; Flexible and adaptable with XML configuration; Extensible plugin architecture
Detect faces with pre-trained models; Transform faces for the neural network; Use deep neural networks to reprsent or embed the face on a hypersphere; Apply favorite clustering or classification techniques to the features to complete recognition task
Statistics
GitHub Stars
-
GitHub Stars
15.4K
GitHub Forks
-
GitHub Forks
3.6K
Stacks
805
Stacks
31
Followers
644
Followers
104
Votes
126
Votes
3
Pros & Cons
Pros
  • 35
    Powerful
  • 22
    Indexing and searching
  • 20
    Scalable
  • 19
    Customizable
  • 13
    Enterprise Ready
Pros
  • 3
    Open Source
Integrations
Lucene
Lucene
No integrations available

What are some alternatives to Solr, OpenFace?

Algolia

Algolia

Our mission is to make you a search expert. Push data to our API to make it searchable in real time. Build your dream front end with one of our web or mobile UI libraries. Tune relevance and get analytics right from your dashboard.

Dejavu

Dejavu

dejaVu fits the unmet need of being a hackable data browser for Elasticsearch. Existing browsers were either built with a legacy UI and had a lacking user experience or used server side rendering (I am looking at you, Kibana).

Elassandra

Elassandra

Elassandra is a fork of Elasticsearch modified to run on top of Apache Cassandra in a scalable and resilient peer-to-peer architecture. Elasticsearch code is embedded in Cassanda nodes providing advanced search features on Cassandra tables and Cassandra serve as an Elasticsearch data and configuration store.

Tantivy

Tantivy

It is a full-text search engine library inspired by Apache Lucene and written in Rust. It is not an off-the-shelf search engine server, but rather a crate that can be used to build such a search engine.

Jina

Jina

It is geared towards building search systems for any kind of data, including text, images, audio, video and many more. With the modular design & multi-layer abstraction, you can leverage the efficient patterns to build the system by parts, or chaining them into a Flow for an end-to-end experience.

Mirage

Mirage

The Elasticsearch query DSL supports 100+ query APIs ranging from full-text search, numeric range filters, geolocation queries to nested and span queries. Mirage is a modern, open-source web based query explorer for Elasticsearch.

Elastic

Elastic

Elastic is an Elasticsearch client for the Go programming language.

Searchkit

Searchkit

Searchkit is a suite of React components that communicate directly with your Elasticsearch cluster. Each component is built in React and is fully customisable to your needs.

Rekognition API

Rekognition API

ReKognition API offers services for detecting, recognizing, tagging and searching faces and concepts as well as categorizing scenes in any photo, through a RESTFUL API. We process and analyze photos from anywhere, so you can mix and match photo sources with user IDs, which can enable you to, say, recognize objects in Facebook and Flickr photos.

Kairos API

Kairos API

Commercial-grade emotion analysis, face detection and recognition engine provided as a public API. Kairos takes the complexity out of facial recognition and emotion analysis so you can focus on building a great product.

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