Qdrant/wolt-food-clip-ViT-B-32-embeddings
收藏Hugging Face2024-02-01 更新2024-03-04 收录
下载链接：
https://hf-mirror.com/datasets/Qdrant/wolt-food-clip-ViT-B-32-embeddings
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资源简介：
---
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))
```
提供机构：
Qdrant
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))
