An advanced facial recognition system designed for real-time identification using deep learning models and optimized vector search. Features include face detection, embedding generation, and scalable deployment options.
Is your feature request related to a problem? Please describe.
Embedding searches in vector databases for face recognition can be slow, especially with large datasets. Faster retrieval methods are essential for efficient model evaluation and experimentation.
Describe the solution you'd like
Implement optimizations for embedding search by leveraging indexing strategies like Approximate Nearest Neighbor (ANN) and caching mechanisms. These optimizations should be adaptable across different vector DBs (such as FAISS, Pinecone, Milvus) to ensure faster face recognition queries.
Additional context
These optimizations will enhance overall system performance and enable more efficient searches, thereby improving the response time.
Checklist
[ ] Research Approximate Nearest Neighbor (ANN) libraries
FAISS, Pinecone, Milvus, or other relevant vector DBs.
[ ] Ensure support across different vector DBs
Test optimizations with different databases (FAISS, Pinecone, Milvus).
[ ] Integrate ANN search capabilities
Implement ANN indexing in vector DBs used by the system.
[ ] Implement caching mechanisms for frequent searches
Add caching for common queries to further improve performance.
[ ] Test the optimized embedding search on large datasets
Benchmark and compare performance before and after the optimizations.
[ ] Document the embedding search optimization process
Add detailed documentation detailing how to configure and optimize searches.
Is your feature request related to a problem? Please describe. Embedding searches in vector databases for face recognition can be slow, especially with large datasets. Faster retrieval methods are essential for efficient model evaluation and experimentation.
Describe the solution you'd like Implement optimizations for embedding search by leveraging indexing strategies like Approximate Nearest Neighbor (ANN) and caching mechanisms. These optimizations should be adaptable across different vector DBs (such as FAISS, Pinecone, Milvus) to ensure faster face recognition queries.
Additional context These optimizations will enhance overall system performance and enable more efficient searches, thereby improving the response time.
Checklist
[ ] Research Approximate Nearest Neighbor (ANN) libraries
[ ] Ensure support across different vector DBs
[ ] Integrate ANN search capabilities
[ ] Implement caching mechanisms for frequent searches
[ ] Test the optimized embedding search on large datasets
[ ] Document the embedding search optimization process