| 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)) | |
| ``` |