Qdrant/wolt-food-clip-ViT-B-32-embeddings

--- language: - en pretty_name: clip-ViT-V-32 embeddings of the Wolt food images task_categories: - feature-extraction size_categories: - 1M<n<10M --- # wolt-food-clip-ViT-B-32-embeddings Qdrant's [Food Discovery](https://food-discovery.qdrant.tech/) demo relies on the dataset of food images from the Wolt app. Each point in the collection represents a dish with a single image. The image is represented as a vector of 512 float numbers. ## Generation process The embeddings generated with clip-ViT-B-32 model have been generated using the following code snippet: ```python from PIL import Image from sentence_transformers import SentenceTransformer image_path = "5dbfd216-5cce-11eb-8122-de94874ad1c8_ns_takeaway_seelachs_ei_baguette.jpeg" model = SentenceTransformer("clip-ViT-B-32") embedding = model.encode(Image.open(image_path)) ```

原始信息汇总

wolt-food-clip-ViT-B-32-embeddings

数据集概述

• 语言: 英语
• 名称: clip-ViT-V-32 embeddings of the Wolt food images
• 任务类别: 特征提取
• 大小类别: 1M<n<10M

数据集详情

• 数据来源: Wolt 应用中的食物图片数据集
• 数据描述: 每个数据点代表一道菜，包含一张图片，图片被表示为512个浮点数的向量

生成过程

• 模型: clip-ViT-B-32

• 生成代码: python from PIL import Image from sentence_transformers import SentenceTransformer

  image_path = "5dbfd216-5cce-11eb-8122-de94874ad1c8_ns_takeaway_seelachs_ei_baguette.jpeg"

  model = SentenceTransformer("clip-ViT-B-32") embedding = model.encode(Image.open(image_path))