Qdrant/Unicom-ViT-B-16

🤗 Hugging Face sourceimage-feature-extractionmit808 MBother✓ 1 checksumupdated today

Submit in one command

Run it next to your model folder. It makes the torrent, checks your files against Hugging Face, and submits it. You just start seeding and paste your key from your account. It only reads your files and never changes them. Read the script first if you like.

curl -fsSL https://pirateface.co/package.sh | bash -s -- --repo Qdrant/Unicom-ViT-B-16 ./model-folder

Needs a seeder →

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)
# ]