---
license: mit
---

# Conditional ViT - B/16 - Categories

*Introduced in **Weakly-Supervised Conditional Embedding for Referred Visual Search**, Lepage et al. 2023*

[`Paper`](https://arxiv.org/abs/2306.02928) | [`Training Data`](https://huggingface.co/datasets/Slep/LAION-RVS-Fashion) | [`Training Code`](https://github.com/Simon-Lepage/CondViT-LRVSF) | [`Demo`](https://huggingface.co/spaces/Slep/CondViT-LRVSF-Demo)

## General Infos

Model finetuned from CLIP ViT-B/16 on LRVSF at 224x224. The conditioning categories are the following : 
- Bags
- Feet
- Hands
- Head
- Lower Body
- Neck
- Outwear
- Upper Body
- Waist
- Whole Body

Research use only.

## How to Use 

```python
from PIL import Image
import requests
from transformers import AutoProcessor, AutoModel
import torch

model = AutoModel.from_pretrained("Slep/CondViT-B16-cat")
processor = AutoProcessor.from_pretrained("Slep/CondViT-B16-cat")

url = "https://huggingface.co/datasets/Slep/LAION-RVS-Fashion/resolve/main/assets/108856.0.jpg"
img = Image.open(requests.get(url, stream=True).raw)
cat = "Bags"

inputs = processor(images=[img], categories=[cat])
raw_embedding = model(**inputs)
normalized_embedding = torch.nn.functional.normalize(raw_embedding, dim=-1)
```