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Qdrant/
Unicom-ViT-B-16

Name: Unicom-ViT-B-16
Availability: InStock

onnx

image-feature-extraction

2304.05884

apache-2.0

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ONNX port of Unicom model from open-metric-learning.

This model is intended to be used for similarity search.

Usage

Here's an example of performing inference using the model with FastEmbed.

from fastembed import ImageEmbedding

images = [
    "./path/to/image1.jpg",
    "./path/to/image2.jpg",
]

model = ImageEmbedding(model_name="Qdrant/Unicom-ViT-B-16")
embeddings = list(model.embed(images))

# [
#   array([ 1.70463976e-02, -3.60863991e-02,  1.24569749e-02, -4.28437591e-02 , ...], dtype=float32),
#   array([ 0.03675087,  0.00696867, -0.01495106, -0.02828627, ...], dtype=float32)
# ]