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I am a beginner in computer vision, my goal is to extract K-nearest neighbours for an image, I had a look at Annoy, Faiss, NMSLIB algorithms and decided to use Faiss for image similarity. I have a large dataset to check for the nearest neighbours. I am currently using faiss.IndexFlatIP, Since it is a brute force approach, it provides good results at the cost of time.

Questions:

  1. Instead of brute force approach, is there any other way we can do this with good results?
  2. Currently I am using pickle to save the large extracted features, is there any other way we can store and retrieve those features effectively?
  3. I am currently using SIFT for feature extraction, whether CNN model will outperform this?

Any help would be greatly appreciated.

Gopinath
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2 Answers2

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Milvus is an open-source vector database built to power embedding similarity search and AI applications.(integrated Faiss/annoy/hnsw) Compared to faiss, it's easy to manage embeddings with milvus

https://milvus.io/

https://github.com/milvus-io/milvus

coolflower
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You might want to use a managed faiss solution which supports feature retrieval.

Ron
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