Qdrant/Unicom-ViT-B-32

🤗 Hugging Face 来源image-feature-extractionmit467 MBother✓ 1 个校验和今天更新

一条命令提交

在你的模型文件夹旁边运行它。它会制作种子、将文件与 Hugging Face 比对，然后提交。你只需开始做种，并粘贴你账户中的密钥。它只读取你的文件，绝不修改。如果愿意，可以先阅读脚本。

curl -fsSL https://pirateface.co/package.sh | bash -s -- --repo Qdrant/Unicom-ViT-B-32 ./model-folder

需要做种者 →

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)
# ]