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.