Qdrant/Unicom-ViT-B-32
By Qdrant · ↓ 0 · ♥ 4 · Updated 2024-07-15
What's in this repo
This page lists the files in Qdrant/Unicom-ViT-B-32, 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-32")
embeddings = list(model.embed(images))
# [
# array([ 0.04177791, 0.0550059 , 0.00025418, 0.0252876 , ..., dtype=float32),
# array([2.23932182e-03, 4.68995124e-02, 3.28772422e-03, 7.57176951e-02, ...], dtype=float32)
# ]
Model info
onnx
image-feature-extraction
arxiv:2304.05884
license:apache-2.0
region:us
↓ 0
♥ 4
Created: 2024-07-10
Updated: 2024-07-15
View on Hugging FaceFiles (4)
.gitattributes
README.md
model.onnx
ONNX
preprocessor_config.json