ONNX port of Unicom model from open-metric-learning.
2
4 commits
3 linked in READMEs
updated Jul 15, 2024
image-feature-extraction
onnx
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)
# ]
Contributors
Similar repos
Linked in READMEs
ONNX port of Unicom model from open-metric-learning.
2
4 commits
3 linked in READMEs
updated Jul 15, 2024
image-feature-extraction
onnx
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)
# ]