🔍 SigLIP Person Search - Open Set

This model is a fine-tuned version of google/siglip-base-patch16-224 for open-set person retrieval based on natural language descriptions. It's built to support image-text similarity in real-world retail and surveillance scenarios.

🧠 Use Case

This model allows you to search for people in crowded environments (like malls or stores) using only a text prompt, for example:

"A man wearing a white t-shirt and carrying a brown shoulder bag"

The model will return person crops that match the description.

💾 Training

Base: google/siglip-base-patch16-224
Loss: Cosine InfoNCE
Data: ReID dataset with multimodal attributes (generated via Gemini)
Epochs: 10
Usage: Retrieval-style search (not classification)

📈 Intended Use

Smart surveillance
Anonymous retail behavior tracking
Human-in-the-loop retrieval
Visual search & retrieval systems

🔧 How to Use

from transformers import AutoProcessor, AutoModel
import torch

processor = AutoProcessor.from_pretrained("adonaivera/siglip-person-search-openset")
model = AutoModel.from_pretrained("adonaivera/siglip-person-search-openset")

text = "A man wearing a white t-shirt and carrying a brown shoulder bag"
inputs = processor(text=text, return_tensors="pt")
with torch.no_grad():
    text_features = model.get_text_features(**inputs)

📌 Notes

This model is optimized for feature extraction and cosine similarity matching
It's not meant for classification or image generation
Similarity threshold tuning is required depending on your application