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---
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))
``` |