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Ocean-FAISS
lowunderabove/Ocean-FAISS
Ocean-FAISS is a machine learning model from lowunderabove. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for faiss. The card lists the license as apache-2.0.
High-speed FAISS vector index and metadata for marine image retrieval using BioCLIP embeddings. Core component of the OceanGPT-X pipeline.
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Public
Updated May 15, 2026
Repo size
16.6 MB
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.faiss16.6 MB · 80%
At a glance
- Library
- faiss
- License
- apache-2.0
- Access
- Public
- Created
- May 15, 2026
- Updated
- May 15, 2026
- SHA
- 8514e14a
Model card
From the Hugging Face model README
Ocean-FAISS: Marine Image Retrieval Index
High-speed FAISS vector index and metadata for marine image retrieval using BioCLIP embeddings. Core component of the OceanGPT-X pipeline.
Repository Contents
| Path | Description |
|---|---|
faiss/index.faiss | Pre-built FAISS index containing BioCLIP feature vectors |
faiss/id_map.json | Mapping between FAISS internal IDs and dataset image IDs |
metadata/metadata.jsonl | Rich metadata for each indexed image (species, location, capture info) |
Usage
Requires faiss-cpu or faiss-gpu.
import faiss
import json
import jsonlines
index = faiss.read_index("faiss/index.faiss")
with open("faiss/id_map.json", "r") as f:
id_map = json.load(f)
# Query vector must match the embedding dimension of the index
query_vector = ... # Shape: (1, dim), dtype: float32
D, I = index.search(query_vector, k=5)
# Retrieve metadata
with jsonlines.open("metadata/metadata.jsonl") as reader:
metadata = {obj["id"]: obj for obj in reader}
for idx in I[0]:
img_id = id_map[str(idx)]
print(metadata.get(img_id, "Not found"))
- Library
- faiss
- License
- apache-2.0
- Languages
- en
- Created
- May 15, 2026
- Updated
- May 15, 2026