Qdrant/Unicom-ViT-B-16
By Qdrant · ↓ 0 · ♥ 2 · Updated 2024-07-15
What's in this repo
This page lists the files in Qdrant/Unicom-ViT-B-16, including model.onnx.
- 1 ONNX models (.onnx files)
- 4 files, including 1 .md, 1 .onnx, 1 .json
- Last updated 2024-07-15
ONNX files
| File | Size | Download |
|---|---|---|
| model.onnx | — | Download |
How to use
These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.
README
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)
# ]
Model info
onnx
image-feature-extraction
arxiv:2304.05884
license:apache-2.0
region:us
↓ 0
♥ 2
Created: 2024-07-10
Updated: 2024-07-15
View on Hugging FaceFiles (4)
.gitattributes
README.md
model.onnx
ONNX
preprocessor_config.json